esa-sceva/satcom-chunk-collection huggingface dataset:
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 4 Methodologies for the Beidou-Based LEO POD
### Theory of Reduce-Dynamic Orbit Determination  
The reduced-dynamic model for the LEO POD has been the mainstream method, which combined the GNSS observations and the dynamic models for LEO POD. To reduce the computation complexity, a few empirical forces or pseudo-stochastic parameters are introduced into the system to assimilate the effect of unmodeled forces. Hence, the pseudo-stochastic parameters are critical for the LEO POD. The precise point positioning (PPP) model is used for involving the GNSS observations, while the LEO observations are not perturbed by the tropospheric delay. The motion equation of the LEO satellite can be expressed as follows:  
\[\ddot{\mathbf{r}}=-GM\frac{\mathbf{r}}{r^{3}}+\mathbf{f}_{1}\big{(}t,\mathbf{r},\dot{\mathbf{r}},Q _{1},\ldots,Q_{d},P_{1},\ldots,P_{s}\big{)}\doteq\mathbf{f} \tag{3}\]  
where the \(GM\) is the product of the constant of gravity and the mass of Earth, \(\mathbf{r}\) is the geocentric radius vector, \(\dot{\mathbf{r}}\) is the velocity of the LEO satellite, with the initial condition \(\mathbf{r}^{(k)}(t_{0})=\mathbf{r}^{(k)}(a,e,i,\Omega,\omega,T_{0};t_{0})\), \(k=0,1\), whereas \(a,e,i,\Omega,\omega,T_{0}\) represent the six Keplerian elements at the time \(t_{0}\). \(\mathbf{f}_{1}\) is the perturbing acceleration, and \(\mathbf{f}\) is the total acceleration. \(Q_{1},\ldots,Q_{d}\) represent the force model parameters, and \(P_{1},\ldots,P_{s}\) represent the pseudo-stochastic orbit parameters. There are different kinds of pseudo-stochastic orbit parameters, such as instantaneous velocity changes, piecewise constant accelerations, and piecewise linear accelerations. Usually, instantaneous velocity pulses and piecewise constant accelerations are adopted in the orbit improved and final orbit determination procedure, respectively. For the instantaneous velocity changes added at epoch \(t_{i}\) in the direction \(\mathbf{e}(t)\), the pseudo-stochastic parameter \(P_{i}=V_{i}\) in Equation (3) is written as \(P_{i}=V_{i}\cdot\delta(t-t_{i})\cdot\mathbf{e}(t)\), and the \(\delta(t)\) is Dirac's delta function. Moreover, the piecewise constant accelerations are generally adopted as  
Figure 6: The elevation-dependent multipath models for the GEO, IGSO, and MEO satellites. The upper and the bottom panels represent models for B1 and B2 respectively.  
the empirical accelerations for orbit determination. Adding the change of acceleration \(a_{i}\) in direction \(\mathbf{e}(t)\) during the time \(t_{i-1}<t<t_{i}\), the pseudo-stochastic parameter \(P_{i}=a_{i}\) in Equation (3) is written as \(P_{i}=a_{i}\cdot\mathbf{e}(t)\).
## 2 Materials and Methods
### Tunnel Point Cloud Displacement Detection
#### 2.3.1 Point Cloud Alignment Using the ICP Approach
(**b**) Sield ring point clouds aligned. inherent in the least-squares minimization of ICP, additional techniques such as ellipse fitting and outlier detection are incorporated to preserve and highlight significant changes. Additionally, local geometric features, such as centerline shifts and shield ring ovality, complement point displacements derived from ICP results, enhancing the robustness of the analysis by distinguishing true structural changes from artifacts caused by point cloud variability or alignment inaccuracies.
## 4 Experimental Results
### Experimental Setting  
We trained the CNN architecture to extract the features. The complete neural network had three inputs: normalized PPG signal, NN interval, and statistical features. The network had two parallel CNN models with three and two convolutional layers, respectively. After feature fusion, there was a fully connected layer with a 0.5 dropout rate. Shuffled mini-batch size = 64, learning rate = 0.01, training epoch = 200, Adam optimizer, and cross entropy for the loss function were used in the experiment. From the entire DEAP dataset, eighty percent of the samples were used for the training and 20% of the samples for testing. All the training procedures were implemented using Keras with a single GPU of GTX 1060. It was trained for 5 min, which was very fast because the inputs were one-dimensional PPG signals. Moreover, less than 1 ms was required for the inference time. All values were calculated as the average of the results of 10 measurements.
## 4 The Reachable Zone Predictive Method
### Reachable Zone Predictive Method  
In practice, the gliding vehicle may experience unusual occurrences or unexpected faults. For example:  
* The gliding vehicle malfunctions during the flight. In these circumstances, the vehicle should be able to operate autonomously and make decisions such as abandoning the preplanned mission and immediately implementing safety measures. It is necessary to immediately forecast the target reachable zone online and to make a reasonable selection of target points, which provide basic information for the ground commander to develop countermeasures.
* In addition, the target point needs to be adjusted momentarily during the gliding phase. The ground commander must forecast the target reachable zone online according to the current state and determine if the new target point is inside it.  
Therefore, a database method (DBM) for online prediction of the reachable zone of the gliding vehicle is proposed. A database of the reachable zone regarding the predicted time is constructed according to the standard trajectory before launch. When the vehicle encounters an unexpected situation, its reachable zone can be quickly determined online, and new safety measures can be quickly implemented to avoid disastrous consequences or  
Figure 4: The reachability discrimination schematic for the new target.  
leverage operational effectiveness according to the current time, providing a guarantee for the online trajectory replanning of the vehicle.  
The DBM procedure of the reachable zone is as follows,  
**Step 1** (Database Building): Because the timing \(t_{p}\) of unusual events in the vehicle cannot be determined in advance, a database needs to be constructed by employing the time for the prediction of the reachable zone as the independent variable. Select a proper time step length \(\Delta t\) and divide most of the flight time of the vehicle into a series \(\{t_{0},\,t_{1},\,\cdots,\,t_{N}\}\), where \(t_{0}=0\)s and \(t_{N}=1000\)s; then, calculate the six characteristic points by the SPM at the current time \(t_{i}\) and construct the database with this simulation data;  
**Step 2** (Database use): If an unusual event occurs in the vehicle, then combined with the offline calculated database, the reachable zone in the current state can be predicted in less time by the simple interpolation method at the current flight time \(t_{p}\). Our numerical studies show that interpolation takes only a brief amount of time, making the DBM ideally suited for online applications;  
**Step 3:** A suitable strike target point or safe disposal area can be quickly determined from the reachable zone, and other safety measures can be taken autonomously by the vehicle, thus improving the survivability, operational effectiveness, and maneuverability of the vehicle.
## 4 Results and Discussion
### Comprehensive Analysis of Typhoon-Induced Oceanic and Societal Impacts
Enterprises are the second most reported group, with 169 reports, accounting for 10.4% of the total, mainly concentrated on political and economic topics, reflecting their social responsibility and economic influence in disaster recovery and economic rebuilding. In contrast, news reports on \"object\" subjects account for 13.4% of the total, with 218 reports. Among these, typhoons themselves are the most prominent, with 144 reports, accounting for 8.8% of the total. These reports are often associated with ecological and scientific topics, discussing the formation mechanisms of typhoons, path prediction, and their impact on the natural environment. As previously discussed, typhoons influence the ocean environment through complex physical processes, including significant changes in ocean temperature and the formation of \"cold wakes\". These reports further emphasize the media's focus on typhoon scientific research and ecological impacts. Additionally, agricultural products and livestock, as subjects, appear in 42 reports, primarily in economic topics, accounting for 2.5% of the total, highlighting the deep impact of typhoons on agricultural production and rural economies. By combining the themes of news reports with the impact of typhoons on the ocean and society, this study not only reveals the narrative strategies of the media in disaster events but also showcases the roles and influence of different stakeholders in typhoon events. More importantly, this analysis underscores the comprehensive role of typhoons in multiple domains, including the dynamic changes in physical oceanography and societal responses, providing new perspectives for understanding the overall impact of typhoons.
## 4 Multi-Branch SEC Receiver
### SEC Receiver  
The PDF of the instantaneous SNR, \(\gamma_{SEC}\), at the SEC output can be expressed as [3]:  
\[f_{\gamma_{SEC}}(\gamma)=\begin{cases}P_{1}f_{\gamma}(\gamma),&\gamma\leq \gamma_{\tau};\\ P_{2}f_{\gamma}(\gamma),&\gamma>\gamma_{\tau},\end{cases} \tag{45}\]  
where \(P_{1}=[F_{\gamma}(\gamma_{\tau})]^{L-1},\ P_{2}=(1-[F_{\gamma}(\gamma_{\tau}) ]^{L})/(1-F_{\gamma}(\gamma_{\tau}))\). \(f_{\gamma}(\gamma)\) and \(F_{\gamma}(\gamma_{\tau})\) can be obtained by using (1) and (2), respectively. By integrating the expression of \(f_{\gamma_{SEC}}(\gamma)\) in (45) with respect to \(\gamma\), the corresponding CDF of \(\gamma_{SEC}\) can be written as:  
\[F_{\gamma_{SEC}}(\gamma)=\begin{cases}P_{1}F_{\gamma}(\gamma),&\gamma\leq \gamma_{\tau};\\ P_{2}F_{\gamma}(\gamma),&\gamma>\gamma_{\tau}.\end{cases} \tag{46}\]  
Similar to (41), the \(q\)th moments of \(\gamma_{SEC}\) can be given as:  
\[\mu_{\gamma_{SEC}}(q)=P_{1}\int_{0}^{\infty}\gamma^{q}f_{\gamma}(\gamma)d \gamma+P_{3}\int_{\gamma_{\tau}}^{\infty}\gamma^{q}f_{\gamma}(\gamma)d\gamma, \tag{47}\]  
where \(P_{3}=(1-[F_{\gamma}(\gamma_{\tau})]^{L-1})/(1-F_{\gamma}(\gamma_{\tau}))\). Substituting (1) in (47), the above expression can be calculated as:  
\[\begin{array}{ll}\mu_{\gamma_{SEC}}(q)&=\frac{P_{1}B(m+q,m-q)}{B(m,m)\Lambda ^{q}}+\frac{P_{3}\gamma_{\tau}^{L-n}}{B(m,m)\Lambda^{q}(n-q)}\\ &\times_{2}F_{1}(m+n,n-q;n-q+1;-1/\gamma_{\tau}\Lambda).\end{array} \tag{48}\]  
Likewise, the corresponding MGF of \(\gamma_{SEC}\) can be expressed as:  
\[\begin{array}{ll}MGF_{\gamma_{SEC}}(s)&=P_{1}\int_{0}^{\infty}\exp(-s\gamma )f_{\gamma}(\gamma)d\gamma+P_{3}\int_{\gamma_{\tau}}^{\infty}\exp(-s\gamma)f_ {\gamma}(\gamma)d\gamma\\ &=\frac{(P_{1}+P_{3})C_{12}^{21}|s/\Lambda|_{0,n}^{1-m}}{\Gamma(m)\Gamma(n)}- \frac{P_{3}\Lambda^{m}\gamma_{\tau}^{m}}{\Gamma(m)\Gamma(n)}C_{1,1,0,1,1,1}^{0,1,1,1}[s\gamma_{\tau},\gamma_{\tau}\Lambda]_{-m}^{1-m}|_{0}^{-1-(m+n)}].\end{array} \tag{49}\]
# Adaptive Algorithm for Fast 3D Characterization of Magnetic Sensors
## 3 Results
### Parameters and Realization Details  
As mentioned in Section 2.1, we used an FLC 100 fluxgate sensor [21] for the proof of concept experiments performed in this work. To circumvent a significant influence of signal breakthrough at its self-excitation frequency around 13.5 kHz, we employed a digital notch filter around this frequency after analog-digital conversion with the used ADC. The filter is designed with its center frequency at 13.5 kHz, a bandwidth of 200 Hz, and a gain of \(-40\) dB.  
While some parameters were changed depending on the type of characterization to enable the analysis of specific workings of the algorithm, most were kept the same to provide comparability and transferability to the different experiments.  
The parameters were chosen empirically based on experience gained during system development. Smoothing factors and thresholds are based on a trade-off between control response and estimation accuracy, and the filter length is set long enough to capture the full apparent impulse response.  
The algorithm's parameters are collected in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Symbol**} & \multicolumn{3}{c}{**Value**} \\ \cline{3-4}  & & **1D** & **2D** & **3D** \\ \hline Frameshift & \(N_{\text{frame}}\) & & 512 (10.67 ms) \\ Filter length & \(N_{\text{filter}}\) & & 20 ms \\ Noise estimation period & \(N_{b}\) & & 1 s \\ Initial settling period & \(N_{\text{set}}\) & & 1 s \\ Time period with fix step size & \(N_{\text{fix}}\) & & 32 ms \\ Smoothing factor rising & \(\gamma_{\text{r}}\) & & 0.997 \\ Smoothing factor falling & \(\gamma_{\text{f}}\) & & 0.999 \\ DT estimation interval & \(N_{\text{DT}}\) & & 200 (4.17 ms) \\ Adaptive control threshold & \(\eta_{\text{fix}}\) & & \(-10\) dB \\ CEA control threshold & \(\eta_{\text{DT}}\) & & \(-15\) dB \\ Minimum time threshold & \(N_{\text{min}}\) & 30 s & 50 ms \\ Maximum time threshold & \(N_{\text{max}}\) & 30 s & 5 s \\ Noise offset threshold & \(\zeta_{\text{SNR}}\) & — & 27 dB \\ Number of elevation steps & \(N_{\theta}\) & & 1 & 60 \\ Number of azimuth steps & \(N_{\varphi}\) & 1 & 360 & 120 \\ System distance quality threshold & \(\zeta_{\text{suff}}\) & — & \(-78\) dB & \(-72\) dB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Collection of algorithm parameters in the experiments. Values in colored cells are used in all indicated experiments.
## 3 Simulations  
Watermark [26] is a realistic simulation tool which is available for UWA communications. It is based on a replay channel simulator driven by measurements of the time-varying impulse response. KAU2 in Watermark is a single-input-multiple-output (SIMO) channel with a mean Doppler shift of about 1.28 m/s, and it contains 16 hydrophone channels. We use the hydrophone channel 4, 8 and 12 to validate the proposed method. Table 1 indicates the detailed parameters of an input packet. And the signal type is linear frequency modulation (LFM) signal which has good Doppler tolerance.  
We send the packet into Watermark and then it outputs 7 packets (2800 bits) for each hydrophone channel. The packet 1 of hydrophone channel 8 is taken example to describe the simulations. Figure 5 shows the clean input packet and the same packet after transmission through the hydrophone channel. The output signal is seriously distorted.  
After we get the Watermark output signal, copy-correlation technique is used to estimate the channel. Figure 6 shows the CIR at a certain moment. It can be seen that the hydrophone channel is characterized by a dispersive and multipath environment which causes the signal distortion. Therefore, we use a single element TRM to process the output packet. Figure 7 illustrates the outputs of the synchronous detection, wherein Figure 7a without using TRM while Figure 7b using TRM. Obviously,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(T_{s}\) (ms) & 100 \\ \(T_{ISI}\) (ms) & 200 \\ \(T_{P}\) (ms) & 16 \\ \(T_{c}\) (ms) & 24 \\ Bandwidth (kHz) & 2(5.5–7.5) \\ \(n\) (bit) & 4 \\ Information size (bit) & 400 \\ Communication rate (bit/s) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the communication signal.  
Table 2 shows the communication performance of different hydrophone channels. As can be seen, the performance is greatly improved by the proposed method.
## 4 Test Result  
### Thermal Properties  
The outside surface temperature variation process of each specimen (Figure 13) was observed by the six thermocouples shown in Figure 6a. From Figure 13, it can be seen that the outside surface temperature achieved a stable state after 15 hours. In fact, to ensure the thermal transfer process was fully completed, 48 hours was taken before the termination of the test. Experimental thermal properties such as the U-value, C-value, R-value and K-value of the specimens are shown in Figure 14. U-values of the LWM TCP were larger than that of the NWM (Figure 14a). The existence of the OFs increased the magnitude of the U-value in the two types of the TCPs because the U-value of the OF was larger than that of the plain panel. Similar observations could be found for the C-value (Figure 14a). However, in Figure 14c, the existence of the OFs had an inverse effect on the R-value in the two types of the TCPs; i.e., the peak C-value (Figure 14b) appeared in the NWM TCP with the highest VR, while it occurred in the plain LWM panel without OFs. To compare the experimental thermal properties of the facade panel with common materials--e.g., a glass sheet, granite stone and brick--their corresponding K-values are plotted in Figure 14d (BCA 2008) [22]. It can be easily seen that the K-values of the LWM TCP were smaller than that of the common facade, which proved its excellent energy efficient performance. This potentiality of the replacement of the traditional glass curtain wall system was predictable for the locations in which the lighting requirement was not particularly high. To ensure efficient thermal transmission, the initial outside temperatures for the NWM TCPs with VRs of 0% and 1.5% were slightly higher than those of other TCPs.
# Gain-Scheduled Disturbance Observer-Based Saturated Controllers for Non-Linear First-Order System +
### Nominal GS-DOB1 Tuning  
The transfer function of the equivalent stabilizing controller based on the nominal DOB1 considering \(a\
eq 0\) can be written as follows:  
\[R^{11}(s)=K_{P11}+\frac{1}{K_{s}}\frac{s+a}{T_{f11}s+1}. \tag{59}\]  
Taking into account the positive controller feedback, it yields automatic reset (series PI) controller with, whose gain is defined by the time constant \(T_{1}=T_{11}\) (53) and the \"integral\" time constant \(T_{f}=T_{f11}\) in (23)  
\[\begin{split}& C^{11}(s)=R^{11}\frac{1+T_{f11}s}{T_{f11}s}=K_{P11} \frac{1+T_{f11}s}{T_{f11}s}+\frac{s+a}{K_{s}T_{f11}s}\\ &=\frac{1}{K_{s}}\Bigg{(}\frac{1}{T_{11}}+\frac{1}{T_{f11}}- \frac{1}{T}\Bigg{)}\Bigg{[}1+\frac{1}{(T_{11}+T_{f11}-\frac{T_{11}T_{f11}}{T} )s}\Bigg{]}.\end{split} \tag{60}\]  
In the proportional control band, the dynamics of the closed control loop with DOB can be described by the transfer function  
\[F_{uy}^{11}(s)=\frac{C^{11}(s)S(s)}{1+C^{11}(s)S(s)}=\frac{\bigg{(}T_{11}+T_{f1 1}-\frac{T_{11}T_{f11}}{T}\bigg{)}s+1}{T_{f11}T_{11}s^{2}+(T_{f11}+T_{11})s+1} \tag{61}\]  
The nominal DOB, which is based on a local linear model, guarantees the stability of the loop in the form of small increments around the fixed operating point. For positive time constants used in controller tuning, all denominator coefficients in (57) and (61) are positive, so the corresponding closed-loop system remains stable. However, the tuning of the controller can change the numerator. Due to the numerator \(F_{uy}^{11}\), overshooting of the setpoint step responses can occur, which can be removed by using a pre-filter  
\[F_{p}^{11}(s)=\frac{1}{\bigg{(}T_{11}+T_{f11}-\frac{T_{11}T_{f11}}{T}\bigg{)}s +1} \tag{62}\]
# MAC-Based Compression Ratio Improvement for CAN Security
## 2 Existing CAN Data Authentication Methods
### Mini-MAC Algorithm  
The Mini-MAC algorithm generates a MAC as follows [13]:  
\[MAC_{Mini}=T[s,H_{k}(k\|M_{n}\|CTR_{ECU_{k}}\|M_{n-\lambda}\|\cdots M_{n-1})], \tag{3}\]  
where \(T[s,\cdot]\) is a function that extracts the first \(s\) bits from input, \(H_{k}(\cdot)\) denotes a hash function using the authentication key \(k\), \(M_{n}\) is the current message to send, and \(\{M_{n-\lambda}\|\cdots M_{n-1}\}\) is the concatenation of the most recent \(\lambda\) messages. The hash function used by Mini-MAC is Message-digest algorithm 5 (MD5) which takes 64-byte input.  
For the 64-byte input in (3), the counter and key need 8 and 16 bytes, respectively. Therefore, for \(\lambda=5,40\) bytes are allocated for 6 messages \((M_{n}\cdots M_{n-5})\), and each message is expected to consume an average of 6.67 bytes.  
If most ECUs use data fields smaller than 5 bytes, the remaining space in the data field can be used to transmit at least 4 bytes of truncated Mini-MAC data. Otherwise, a Mini-MAC algorithm does not provide sufficient authentication for CAN systems.
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 2 Overview of Test Stimulus Compression Techniques
### Enhancing Encoding Compression Efficiency  
Test compression methods aimed at enhancing encoding compression efficiency involve decomposing the original test set into multiple sub-component sets, followed by compression processing of these sub-component sets [31]. The compression process maps the original test set to a representation domain of sub-component sets that is more amenable to compression, after which encoding or other compression methods are applied to the sub-component sets [32]. This approach effectively utilizes the extensive contiguous data blocks within the original test set, allowing the encoding-based test stimulus compression methods to more efficiently partition symbol blocks and achieve higher compression efficiency.
## 2 Background and Motivation  
### Cold Start Problem  
In a serverless environment, the provider containerizes user requests, which are submitted as serverless functions. These functions are packaged and executed within one or more containers. When a request is received, the provider checks whether an available container can handle it. If such a container exists, the request is routed directly to it; this reusable container is referred to as a warm container. However, if there is no idle container ready to serve the request, the provider must spawn a new one. Initializing a new container involves uploading necessary libraries, loading dependencies if required, and allocating resources. This initialization time adds overhead to the function's actual execution and introduces additional latency, commonly referred to as a cold start. In other words, the cold start represents the time needed by the provider to launch a container from scratch. Once the container is initialized, it can be reused for future requests as a warm instance. The total execution time of a request, including cold start overhead, can be broken down as follows:  
\[T_{-T_{precision}+T_{initiize}+T_{Execution}} \tag{1}\]  
Due to the lightweight nature of serverless functions, their execution time typically lasts only a few milliseconds, and in some cases, is almost negligible. As a result, cold starts can contribute disproportionately to application latency, sometimes adding more than ten times the actual execution time [10].  
To demonstrate the latency impact of cold starts compared to warm starts, we conducted an experiment using several serverless applications. These applications are described in Table 1. We deployed them on AWS Lambda and measured the total execution time from the moment a request was queued to the point the response was received. The function execution time was reported by AWS Lambda. The results, presented in Figure 1, show that in all cases, the cold start overhead exceeds the actual execution time, adding up to 1000 milliseconds to the total latency depending on the nature and complexity of the application. These findings are consistent with previous research, which has shown that cold starts can be a major source of application latency.
# Fault Location in Double Circuit Medium Power Distribution Networks Using an Impedance-Based Method
## 4 Simulation Results
### Numerical Results
#### 4.2.2 The Effect of the Fault's Location  
To determine how the suggested technique is sensitive to the fault occurrence locations, a simulation of single-phase faults to the ground on different sections of the medium power network was done. The results are presented in Figure 7. Single-phase to the ground fault simulations with an inception angle of 45 degrees, different resistances, and with various distances (5 km steps) from the beginning of the feeder to the end were done on the medium power network. Based on the presented results in Figure 7, we can conclude that the fault distance did not have a significant effect on the fault location method, and the maximum errors were from the subsidiary lines.