esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Synaptic Activity during Neuronal Oscillations
## 3 Results
### Effect of input noise
#### 3.1.3 Estimation When the Neural Model Is Approximated by a Base Model  
In many experimental cases, the exact model describing the target neuron is not known. One plausible way to apply our methodology is by fitting the measured data to a reference model. In this section, we explore this paradigm and study the errors of the estimation procedure. For this purpose, we generate membrane potential traces from the noisy pyramidal cell model (3) (see also Figure 2 in Section 2) with the synaptic drive shown in Figure 5a, and use them to fit an exponential integrate-and-fire model (5). The underlying assumption is that the fitted EIF model provides a good approximation of the original model (see Figure 2).  
In order to challenge our methodology, we consider the worst scenario regarding data discretization and noise levels according to our previous results (see Tables 2 and 3): we assume a discretization step size for the conductances (\(\delta_{g}=10^{-3}\)) and a high standard deviation for the white noise in the membrane potential (\(\sigma_{V}=0.1\) mV). Even so, results still show a good performance of the estimation procedure, see Figure 5, unexpectedly similar to the results obtained in Section 3.1.1.  
As we can see in Figure 5a, some of the small oscillations are tracked properly, yielding to some temporal shifts on the onset of spikes when reconstructing the membrane potential (see Figure 5b). However, despite of this loss, the estimation keeps capturing the spiking frequency, and the conductances are estimated with a relative error of \(2.7\%\) (mean over time) whereas the MSE is \(7.038\times 10^{-7}\). After interpolating the data, the relative error drops to \(1.45\%\) and the MSE is equal to \(7.348\times 10^{-7}\). Note that the difference between these errors and those provided by the same case when we use the exact neural model in the estimation procedure does not differ qualitatively (see the first two rows of Table 4). Therefore, even though we assume that the exact model is not known, we still obtain good estimates of the time course of the synaptic conductance extracting the \(T-g_{sym}\) relationship from an approximated base model (EIF in this case).  
In the estimation procedure we have built a conductances' table to be able to make the estimation. However, note that for this specific case, and when the conductances are supposed to be constant, there exists a closed expression for the EIF period, \(T_{EIF}(g_{sym})\), see Equation (6). This fact allows us to replace the table of the estimation procedure for the solution \(T_{EIF}(g_{sym})=T^{*}\), where \(T^{*}\) is the specific interspike considered in each iteration of the procedure. Knowledge of this expression allows us to estimate the conductances more efficiently and with less numerical errors (see third row in Table 4).
# Shear Behavior of Superposed Perfobond Connectors Considering Lateral Constraints
## 1 Introduction
The experimental results show that the curve of a superposed perfobond connector can be seen to rapidly decrease above the peak load, owing to the failure of the concrete dowel. This indicates that the superposed perfobond connectors poorly maintain the load. By summarizing the studies for both types of perfobond connectors, we can see that the confinement of the concrete dowel has a significant impact on the failure mode and capacity of the connectors. [PERSON] et al. [24] indicated that the shear capacities of the superposed perfobond connectors under relatively weak constraints were mostly controlled by the constraint effect when the hole size exceeded a specific range. [PERSON] et al. [25; 26] showed that the lateral constraint provided by transverse reinforcements represents a crucial factor underlying the mechanical behavior differences between superposed and inserted perfobond connectors. [PERSON] [27] drew the conclusion that the lateral constraint of the concrete dowel primarily comprises the concrete-wrapping perforating rebar and bottom friction. Because of the different design methods and specimen-loading environments, it is difficult to model a unified bottom friction; hence, we neglect this factor here. As a result, it is important to study the different mechanical properties of weakly constrained superposed perfobond connectors when varying the hole diameters and lateral constraints. Changes in lateral constraint can be realized by varying the concrete compressive strength, diameter of the perforating rebar, and stirrup form. Thus, when superposed perfobond connectors are used in composite structures, their shear and load-holding capacities can be improved by increasing the confining effect of the concrete dowel. This study aimed at evaluating the influence of lateral constraints on shear behavior of superposed perfobond connectors in composite structures and 12 push-out specimens were tested. The parameters included hole number, hole area, and lateral constraint. The lateral constraints were mainly affected by the concrete compressive strength, diameter of perforating rebar, and the number of transverse reinforcements. Finally, based on the test results, an analytical model for shear capacity of the multi-hole perfobond connectors considering the lateral constraint was proposed to thereby guide the design of perfobond connectors in composite structures.
## 3 Control System
### Controller Comparison  
In this section, a brief theoretical comparison was conducted between the selected voltage control algorithm, which is the POVC, and several other voltage control techniques. The first technique compared was the cascaded converter method, where two different DC-DC converters are connected in series to allow both MPPT and voltage regulation ability [25; 28]. The second technique was the inverter side control method, where voltage  
Figure 5: POVC tracking operation and regulating range representation using (**a**) P-V curve. (**b**) Input and output characteristics.  
regulation was achieved by modifying the modulation index of the pulse width modulation (PWM) at the inverter side switches [29; 30; 31]. The last selected voltage control technique was the PI-based controller, in which the PI controller was used to perform the voltage regulating operation by taking \(\rm V_{ref}\) and \(\rm V_{B}\) as the inputs to the controller [20; 21; 22]. The comparison is presented in Table 2. It can be concluded that the POVC had better characteristics overall compared to the other control methods for standalone PV applications where a very high voltage control range with power tracking ability could be achieved by using only a single controller and DC-DC converter.
# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
## 3 Control Architecture
### Motion Control and Visual Interface Layers  
The low-level sensors, which measure physical quantities such as acceleration, velocity, and orientation, and the encoders in the robot system are connected to the robot control unit (RCU). The high-level sensors, which perceive the environment around the robot, such as cameras, Lidar, and inertial measurement units (IMUs), are connected to a powerful computer, such as the Jetson Nano board. The RCU is responsible for collecting data from the low-level sensors and sending them to the Jetson Nano. The Jetson Nano is responsible for processing the data from the high-level sensors and detecting falls. The complete control system is built using ROS melodic with a visual user interface and continuous monitoring.
## 2 Multi-UAV Cooperative Persistent Monitoring Path Planning Model
### Min-Max Optimization for Multi-UAV Cooperative Monitoring
#### 2.6.2 UAVs Monitoring Path Privacy Criterion  
As long as any UAV accesses a task node, its waiting time will be cleared. Therefore, it is necessary to evaluate the privacy of the monitoring path based on the actual visiting period of all task nodes. Since the uncertainty of the monitoring period indirectly reflects the monitoring privacy, this study proposes the concept of monitoring period entropy (MPE) which refers to the uncertainty when the UAV returns to the task node for monitoring again. The larger the MPE, the higher the randomness of the monitoring period. Define a vector \(\widetilde{p}_{i}=\left\{p_{k,i}|p_{k,i}>0,k=1,2,\ldots,K\right\}\) to represent the vector composed of all the monitoring cycles of task node \(i\) in the entire monitoring horizon. The length of the vector, \(\widetilde{p}_{i}\), is \(l_{\widetilde{p}_{i}}=\sum_{k=1}^{K}x_{k,i}\). Define the monitoring period entropy of node \(i\) as:  
\[H(\widetilde{p}_{i})=-\sum_{j=1}^{l_{\widetilde{p}_{i}}}P(\widetilde{p}_{i}(j ))\log P(\widetilde{p}_{i}(j)) \tag{13}\]  
where \(P(\widetilde{p}_{i}(j))\) is the probability that the \(j\)th element in vector \(\widetilde{p}_{i}\). One should note that \(H(\widetilde{p}_{i})\) is always positive. The minimum monitoring period entropy among all task nodes is:  
\[\underset{i\in V}{\text{min}}H(\widetilde{p}_{i}) \tag{14}\]  
Therefore, in order to improve the randomness of the monitoring period, the optimization objective is designed to maximize the entropy of the smallest monitoring period among all task nodes, namely \(\underset{Y,F}{\text{max}}\left(\underset{i\in V}{\text{min}}H(\widetilde{p} _{i})\right)\). This criterion is also equivalent to the reciprocal of the minimum monitoring period entropy (because \(H(\widetilde{p}_{i})\) is a positive number), so the following optimization objectives can be designed:  
\[\underset{Y,F}{\text{min}}J_{2}=\frac{1}{\underset{i\in V}{\text{min}}H( \widetilde{p}_{i})} \tag{15}\]  
The dimension of the multi-UAV path solution \(\boldsymbol{Y}\) is \(K\times(N+\mid U\mid)\), and the algorithm time complexity of the calculation for the monitoring of overdue time and the evaluation of path privacy is \(O(n^{2})\).
## 4 Proposed Online Method for Impedance Characterization of Electrical Machines
### Step 3--Search for dominant frequency components  
Then, the idea is to extract the frequency components whose amplitude is predominant depending on the frequency range considered. For this purpose, we have defined adaptive thresholds for the frequency spectrum of voltages and currents. These thresholds are characterized by relations (8) and (9), respectively. The graphical representation of this search for the dominant frequency components is explained for the simple voltages in Figure 18. Note that these thresholds have been defined using the DC bus voltage (\(U_{DC}\), which for this excitation corresponds to the amplitude at the switching frequency) and the maximum sampling frequency (to satisfy the Nyquist criterion).  
\[S_{V}(f)=\frac{U_{DC}}{\sqrt{3}},\frac{f_{max}}{f_{PWM}}.\frac{1}{f} \tag{8}\]  
\[S_{I}(f)=S_{V}(f).R_{ph} \tag{9}\]  
where \(S_{V}\) and \(S_{I}\) are the thresholds for simple voltages and currents, respectively, \(f\) is the given frequency, \(U_{DC}\) is the DC bus voltage, \(f_{max}\) is the maximum sampling frequency, \(f_{PWM}\) is the switching frequency, and \(R_{ph}\) is the electrical resistance of the machine phases.  
Figure 17: Frequential analysis of the simple voltages (**a**) and measured currents (**b**).  
Figure 18: Graphical representation of the adaptive threshold defined to extract the predominant frequency components within single voltages.  
Finally, for each of the processed signals (simple voltages and currents), once the amplitude of the given component exceeds the threshold set at a given frequency (\(f_{\mathit{prep}}\)), the following quintet is stored:  
\[Q=\left[\,f_{\mathit{prep}}\;,\,|V_{\mathit{abc}}(f_{\mathit{prep}})|\;,\;|I_{ \mathit{abc}}(f_{\mathit{prep}})|\;,\;\angle V_{\mathit{abc}}(f_{\mathit{prep}}) \;,\;\angle I_{\mathit{abc}}(f_{\mathit{prep}})\;\right]\]
###### Abstract  
The KITSUNE satellite is a 6-unit CubeSat platform with the main mission of 5-m-class Earth observation in low Earth orbit (LEO), and the payload is developed with a 31.4 MP commercial off-the-shelf sensor, customized optics, and a camera controller board. Even though the payload is designed for Earth observation and to capture man-made patterns on the ground as the main mission, a secondary mission is planned for the classification of wildfire images by the convolution neural network (CNN) approach. Therefore, KITSUNE will be the first CubeSat to employ CNN to classify wildfire images in LEO. In this study, a deep-learning approach is utilized onboard the satellite in order to reduce the downlink data by pre-processing instead of the traditional method of performing the image processing at the ground station. The pre-trained CNN models generated in Colab are saved in RPi CM3+, in which, an uplink command will execute the image classification algorithm and append the results on the captured image data. The on-ground testing indicated that it could achieve an overall accuracy of 98% and an F1 score of a 97% success rate in classifying the wildfire events running on the satellite system using the MiniVGGNet network. Meanwhile, the LeNet and ShallowNet models were also compared and implemented on the CubeSat with 95% and 92% F1 scores, respectively. Overall, this study demonstrated the capability of small satellites to perform CNN onboard in orbit. Finally, the KITSUNE satellite is deployed from ISS on March 2022.  
Keywords:wildfire; convolution neural network; optical payload; CubeSat; onboard classification +
Footnote †: journal: remote sensing
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 3 The B-REP Mechanism
Router 1 detects the link failure and becomes the router S. Router S selects a given backup path from the B-REP BP table based on the identity of the failed interface and the specific IP address of the protected flow. The S router encapsulates the packet of the original flow with a new BIER header and inserts the corresponding BS value into the BS field. The BS value of 01110 specifies a route via R2, R4, and R3. Based on the BS values, R1 sends a BIER packet towards the next-hop of a new alternative route, router number 2. The next-hop router, i.e., router 2 (router R), receives the B-REP packet. As it is a BIER packet, it does not use traditional IP routing. Instead, it analyses the BS and selects the next-hop based on the BS bits (R4). R2 then sets its own bit of the BS value to 0. This indicates that the R2 router has successfully processed the B-REP packet. R2 then forwards the packet to R4. R4 repeats these steps and switches the packet to R3. Finally, the D router R3 receives the B-REP packet. R3 detects that the BS already has only one bit set to 1 and that this bit corresponds to itself. This fact indicates that router 3 is  
Figure 8: Detection of a failure and the reaction of the B-REP mechanism. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Destination IP** & **Protected Interface** & **Destination B-REP R-ID (OSPF Router ID)** & **Bit-String Value** \\ \hline
192.168.2.1 & Interface R1–R3 & 3 (3.3.3.3) &... 01110 (LSB) \\
192.168.4.1 & Interface R1–R3 & 4 (4.4.4.4) &... 01010 (LSB) \\  &... &... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 3: B-REP Backup Table. B-REP packet. R3 is router D, the last B-REP router of the alternative B-REP route, where the B-REP FRR processing also ends. Therefore, R3 completely removes the BIER header and restores the packet to its original state. R3 routes the packet to its destination using a native unicast routing process. Router S can deactivate the B-REP mechanism in several ways. After the routing protocol convergence process is completed, or after the expiration of the special timer. The B-REP is the result of research focused on the protection of critical IP flows, i.e., flows of special significance, purpose, or priorities. Therefore, the B-REP IP FRR mechanism aims at the FRR protection of the limited number of specific IP flows and has been designed and tested to protect against a single network failure. The B-REP solves this well. However, such a property may, under certain conditions, be considered as a limitation of the mechanism, for example in situations where multiple failures occur. Another limitation of the B-REP is its granularity. The B-REP protects only a few selected important flows. These are the main drivers that have led our research to define a more flexible and modern IP FRR mechanism. The output is the Enhanced B-REP, which addresses the abovementioned B-REP limitations and offers multi-failure protection for all network flows.
## 5 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Methods** & **Acc (Avg.)** \\ \hline \multirow{8}{*}{Word} & _Large-scale LM_ & \\  & KoBERT & 65.8 \\  & +LSTM-M2O & 62.3 \\  & +LSTM-M2M & 61.6 \\  & +CNN-MP/AP & 69.7 \\  & KR-BERT & 69.4 \\  & +LSTM-M2O & 67.0 \\  & +LSTM-M2M & 63.8 \\  & +CNN-MP & 72.4 \\  & +CNN-AP & 73.4 \\  & +CNN-MP/AP & 73.9 \\  & +Dense for each pooling operation & 75.1 \\ \cline{2-3}  & _Not-use_ & \\  & LSTM-M2O & 66.5 \\  & LSTM-M2M & 66.5 \\  & CNN-MP & 66.0 \\  & CNN-AP & 71.2 \\  & CNN-MP/AP & 75.0 \\  & +Dense for each MP/AP & 77.2 \\  & CNN + LSTM & 70.4 \\  & LSTM + CNN & 71.7 \\  & Transformer & 74.2 \\ \hline \multirow{8}{*}{Word + Part-of-Speech} & _Large-scale LM and Pos Embeddings_ & \\  & KR-BERT & \\  & +CNN-MP/AP & 73.4 \\ \cline{1-1}  & _Not-use_ & \\  & CNN-MP & 69.3 \\  & CNN-AP & 72.1 \\  & CNN-MP/AP & 73.9 \\  & +Dense for concat output & 77.1 \\  & +Dense for each output & 76.3 \\  & Transformer & 75.0 \\  & **Proposed Model** & **78.0** \\  & +Not use notion of Siamese network & 73.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proposed Model** & **2-Year-Old** & **4-Year-Old** & **6-Year-Old** \\ \hline
2-year-old & 20.4 & 2.2 & 0 \\
4-year-old & 1.2 & 46.6 & 12.8 \\
6-year-old & 0 & 14.6 & 42 \\ \hline
**CNN-MP/AP** & **2-Year-Old** & **4-Year-Old** & **6-Year-Old** \\ \hline
2-year-old & 20.8 & 1.8 & 0 \\
4-year-old & 1.4 & 46.6 & 12.6 \\
6-year-old & 0 & 16 & 40.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix. Each value is an average in 5-fold. at the age of four, and the grammar and form of the mother tongue are completed by the age of five to six. For this reason, the deep learning-based model is considered to have trouble differentiating between 4-year-old and 6-year-old. In the case of a large-scale language model, significant performance could not be verified even if the same model was used downstream. Because a large-scale language model is trained with a written language, it is considered that representing the spoken language will be difficult. Moreover, children's language systems are incomplete, and it is not considered appropriate that the large-scale language model which trained a corpus based on adults.
## 2 Kinetic Analysis
### Kinetic Analysis Underwater  
Different from the motion mode on the land, the underwater motion mainly depends on the water propulsion on both sides of the fin surface. Besides the rotation motion, a roll motion should be taken into account as well. A 5-DOF motion model of the designed amphibious robot is shown in Figure 2, which is expressed in a Cartesian coordinate system without rotational motion around the z axis.  
Figure 1: Diagram of stress analysis.  
In Figure 2, \(\alpha\), \(\beta\) and \(\gamma\) are the rotation angles corresponding to the x, y and z axes, respectively. The expression of the kinetic equation is given as follows:  
\[C_{t} =C_{t0}+\int\limits_{t0}^{t}v_{t0}dt \tag{5}\] \[\theta_{t}^{x} =\theta_{t0}^{x}+\int\limits_{t0}^{t}\omega_{t0}^{x}dt\] (6) \[\theta_{t}^{y} =\theta_{t0}^{y}+\int\limits_{t0}^{t}\omega_{t0}^{y}dt\] (7) \[V_{t} =V_{t0}+\int\limits_{t0}^{t}\frac{F}{m}dt+\int\limits_{t0}^{t} \frac{M}{I_{\text{C}}}dt\] (8) \[\omega_{t} =\omega_{t0}+\int\limits_{t0}^{t}\frac{M}{I_{\text{C}}}\times rdt \tag{9}\]  
where \(C_{t}\) is the centroid vector of the amphibious robot at time \(t\), \(\theta_{t}^{x}\) is the rotation angle around the x axis at time \(t\), \(V_{t}\) is the velocity, \(\omega_{t}\) is the angular velocity at time \(t\), \(M\) is the external moment, \(I_{\text{C}}\) is the inertia, and \(r\) is the distance from any point to the rotation centerline.  
The basic motion parameters can be determined in combination with the designed amphibious robot through the above analysis, and the specific parameters are shown in Table 1.  
Through the kinetic analysis of the motion modes in different environments, a more vivid, clear and accurate motion description is obtained, which can theoretically explain the basic principle of the motion propulsion mode of the amphibious robot. Meanwhile, these parameters also provide the motion information of the amphibious robot for the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Symbol** & **Underwater** & **Land** \\ \hline Total mass & \(m\) & 10 kg & 10 kg \\ Length of the fin surface & \(l\) & 75 cm & 75 cm \\ Wavelength & \(\lambda\) & 20 cm & 20 cm \\ Coulomb friction coefficient & \(\mu\) & / & 0.85 \\ Velocity & \(V\) & 0.23 m/s & 0.13 m/s \\ Angular velocity & \(\omega\) & 0.31 rad/s & 0.12 rad/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The amphibious robot motion parameters.  
Figure 2: Diagram of 5–DOF motion.  
algorithm, so that the simulation process can be closer to the practical case and also deliver more convincing results.
# Experimental Study on Mechanical Properties of Anisotropic Slate under Different Water Contents
## 4 Analysis of Anisotropic Properties of Slate Parameters
### Cohesion and Internal Friction Angle Anisotropy  
Applying the Mohr-Coulomb criterion, the regression equation \(\sigma_{1}=b+m\sigma_{3}\) is derived. Constants \(m\) and \(b\) are determined by fitting test data to this equation. Substituting these values into Equation (10) allows for calculating the cohesion (\(c\)) and internal friction angle (\(\varphi\)) of the slate under different bedding angles and water contents.  
\[\left\{\begin{array}{l}\sin\varphi=\frac{m-1}{m+1}\\ c=b\frac{1-\sin\varphi}{2\cos\varphi}\end{array}\right. \tag{10}\]  
As illustrated in Figure 16, cohesion exhibits a U-shaped pattern as the bedding angle increases, showing significant anisotropy. Cohesion, indicative of the shear resistance of the failure surface, reflects the sample's shear resistance. At \(\beta=0^{\circ}\) or \(90^{\circ}\), the fracture surface typically intersects the bedding plane, hindering continuous fracture propagation and making it challenging for shear fractures to develop along this plane, thereby increasing cohesion. The overall variations in dry, natural, and saturated states are 61.73, 31.75, and 72.30 MPa, respectively. Notably, the variation amplitude in dry and saturated states is more pronounced than in the natural state, suggesting that water content significantly influences cohesion. Fluctuations are considerable between \(0^{\circ}\) and \(45^{\circ}\), with decreases in  
Figure 15: [PERSON]’s ratio of slates with different bedding angles: (**a**) dry state, (**b**) natural state, and (**c**) saturated state.  
dry, natural, and saturated states being 100%, 68.7%, and 100%, respectively. The internal friction angle in the natural state mirrors the cohesion trend, while in dry and saturated states, it follows an increase-decrease-increase pattern. Excluding two minimum values in saturated samples, the change amplitudes in other states hover around 18 deg, showing no marked difference, indicating that water content slightly affects the internal friction angle variation but significantly influences its trend.