esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experimental Development of
## 2 Working Mechanism of the Stiffness-Adjustable Foot System
### Theoretical Analysis of the Variable Stiffness Mechanism
Similar to pneumatic artificial muscles, the stiffness of the pneumatic bladder is dependent upon two factors: one is the inherent stiffness of the bladder in the natural state without inflation, which is determined by its material properties and mechanical structure, and the other is the stiffness that relates to the air pressure inside the bladder [36]. Stiffness of a body can be defined as the infinitesimal force variation with regards to resulting compliant displacement [37]. If we assume \(F\) be the external force acted on the pneumatic bladder, and \(x\) is the corresponding amount of compression, then the system stiffness of the PVSU in the first phase can be described as:  
\[K_{1}=\frac{dF}{dx}=\frac{k_{1}k_{2}}{k_{1}+k_{2}}+k_{3}+\frac{d(p\cdot A)}{ dx}=\frac{k_{1}k_{2}}{k_{1}+k_{2}}+k_{3}+p\frac{dA}{dx} \tag{1}\]  
where \(k_{1}\), \(k_{2}\), and \(k_{3}\) indicate the stiffness values of the restoring spring, the supporting spring, and the bladder, respectively, \(A\) represents the effective circumferential cross-sectional area of the pneumatic bladder, and \(p\) represents the air pressure inside the bladder. When the reversing valve is triggered, the air inside the pneumatic bladder is expelled quickly. Then the compression process enters the second phase and lasts until the piston moves to its limit position. The restoring spring, supporting spring, and pneumatic bladder are all compressed, but the pressure inside the pneumatic bladder is changed to ambient pressure which is represented by \(p_{\text{a}}\). The system stiffness is expressed by:  
\[K_{2}=\frac{k_{1}k_{2}}{k_{1}+k_{2}}+k_{3} \tag{2}\]  
The third phase starts as soon as the piston reaches its limit and the restoring spring cannot be compressed anymore. As the external force continues to increase, only the supporting spring and pneumatic bladder undergo further compression. The system stiffness of this phase is given by:  
\[K_{3}=k_{2}+k_{3} \tag{3}\]  
It should be noted that this section is focused on the introduction of the variable stiffness mechanism in principle, rather than the investigation of the accurate analytical model of the PVSU. Thus, only simple stiffness expressions are provided above. The variable stiffness of the PVSU in the entire compression process is illustrated in Figure 4. The first phase which has the largest stiffness is also termed as the high stiffness phase, and the second and third phases are termed as low stiffness phases. In this figure, \(h_{ch}\) and \(h_{max}\) represent the trigger displacement and the maximum displacement of the valve piston, respectively. Figure 4: The system stiffness of the PVSU in the entire compression process.
## 6 Performance Evaluation
### Packet Length Effect on Sparse Signal Transmission  
Based on the relationship between packet length and mutual coherence, we present the performance evaluation of signal transmission efficiency with varying packet length in this part. Compared to using successfully transmitted data times as the efficiency criterion, we take the IAR at the fusion center (FC) as the criterion to measure the efficiency of signal/information transmission.  
Specifically, considering that the reconstruction error should be less than 30%, so as to satisfy the engineering requirement in the practical application of structural health monitoring (SHM) [35], we set \(threshold=0.3\) in this part. Furthermore, for a high quality requirement application, we set \(threshold=0.001\) compared to \(threshold=0.3\) in SHM applications.
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 4 Experimental Verification
### Experimental Test 1
#### 4.1.1. Sparsity Verification  
In order to verify the influence of the hierarchical sparsity strategy on the sparsity of the proposed method network neurons, the HSDAE method was tested on the above four data sets. The test experiment adopts the controlled variable method, one group uses hierarchical sparse parameters, and the other group uses non-hierarchical sparse parameters. The optimal sparsity parameter calculated by PSO is applied to the two groups, and finally, the KL divergence is used to measure the sparsity of each layer of neurons. Further, the smaller the KL divergence value, the smaller the average activation of neurons in the layer and the higher the sparsity. In order to ensure the reliability of the data, the average value of 20 test data is used for comparative analysis. Among them, the optimal hierarchical sparse parameter is [0.0679, 0.0448, 0.0396, 0.0398, 0.0336, 0.0522], and the optimal non-hierarchical sparse parameter is [0.1064, 0.0592].  
The experimental results of HSDAE with hierarchical strategy and non-hierarchical strategy in data sets A, B, C and D are shown in Figure 8. Among them, \(KL_{x}\) in Figure 8 represents the KL divergence of the neurons in the \(x\)-th hidden layer. From the results in the figure, the hierarchical sparsity strategy has a significant impact on the sparsity of the first layer of neurons and the second layer of neurons because the KL divergence value of the hierarchical strategy is smaller than that of the non-hierarchical strategy. Although the KL divergence values of neurons in the third layer have little difference, the overall KL loss hierarchical strategy is smaller than that of the non-hierarchical strategy, as shown in Figure 8. In short, the measurement results of the sparsity of neurons in each layer show that the hierarchical sparsity strategy can adjust the sparsity of each layer of a multi-layer network and enhance the sparsity of the network.
## 5 A Novel Protection Mechanism against the SIP-DRDoS Attack  
In this section, we present an effective mitigation mechanism against the SIP-DRDoS attack presented in Section 4. Our mechanism borrows ideas from several approaches used against existing attack scenarios [14; 15]. It has SIP protocol sniffing and analysis capabilities that enable real-time network traffic monitoring. It listens to the network and performs protocol analyses to detect attacks. It utilizes multiple firewalls and IDS/IPS strategies as its working principle, giving it a powerful mechanism for attack detection and response. It possesses specially designed detection, notification, and response capabilities that are tailored to the SIP protocol specifications. At the seventh layer (application layer) of the OSI reference model, it examines each packet in the passing traffic and creates a packet and status table for each SIP communication channel in accordance with SIP specifications. These characteristics allow the proposed mechanism to execute stateful packet inspection (SPI) in the SIP protocol using application firewall features.  
The proposed mechanism uses the behavior-based detection approach, as it monitors and processes differences in network traffic behavior, such as traffic rate, number of incoming requests, and number of returned responses. Using its learning mechanism, it establishes a baseline for the system and learns the typical traffic rate. It looks for deviations from the baseline, and when it finds them, it reports an alarm and, if necessary, starts an action. It does not rely on signatures to function but instead employs a customized detection algorithm to look for particular parameters and behavior when analyzing traffic. This gives it qualities that are common with heuristic detection approaches. Activity logs produce alarms when there is questionable network activity, which is then used to carry out a preset action. For instance, it can block the IP, block the port or reset the SIP session. It can also record SIP data for forensic analysis.  
Figure 4: Average SIP server CPU loads for registered, nonregistered, and random users (3 min after the SIP-DRDoS attack was initiated).
## 6 Conclusions  
As mass production applications using CubeSats are expanding, it can be deduced that an increase in productivity through the enhancement of flexibility and integration speed looks particularly important for the profitability and success of these missions. In this study, a highly flexible and easy-to-use 3U and 1U slot-based structure was developed, which facilitates easy and quick integration. It is believed to increase the reliability of satellite integration by reducing the complexity that case human errors during the integration and testing phases. Both structural analysis and vibration tests show that the developed model is stiffened and strong enough to withstand expected severe launch loads.  
Finally, the design goals are evaluated against the conventional CubeSat structural design using the part and assembly step count, as well as complexity analysis. The results show significant improvements in the critical design parameters. The scalability of the concept is also examined by developing an equivalent 1U slot-type model showing similar design outputs. This 3U design concept is currently being implemented on one of Kyutech's mass production CubeSat projects, with additional improvements.  
However, the acquired advantages are more noticeable, especially for missions where the total mass of the satellite is not critical. However, in addition to an improvement in the design, several other options are being explored to reduce the total weight of the structure. In future work, the employment of new production techniques such as additive manufacturing is believed to provide further enhancement for this design concept.  
Conceptualization, E.E.A. and H.M.; methodology, E.E.A., H.M. and M.C.; software, E.E.A.; validation, E.E.A., H.M. and M.C.; formal analysis, E.E.A.; investigation, E.E.A. and H.M.; resources, M.C. and H.M.; data curation, E.E.A., J.R.C.-A. and H.M.; writing--original draft preparation, E.E.A.; writing--review and editing, E.E.A., J.R.C.-A., H.M. and M.C.; visualization, E.E.A.; supervision, H.M. and M.C.; project administration, J.R.C.-A., H.M. and M.C.; funding acquisition, H.M. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Economy, Trade and Industry (METI), Japan under the grant. \"International Standard Development for Energy Conservation (International Standardization of CubeSat Interface)\" with grant number [FY2021-10]. The APC was funded by METI.  
Not applicable.  
The authors would like to acknowledge the support of the Japanese Ministry of Economy, Trade, and Industry through the Promotion of Energy Saving and Related International Standards.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Analysis of Proposed FP-AFSRM
### Comparison of Magnetostatic Analysis Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multicolumn{1}{c}{**Single-Stator and Single-Rotor FP-AFSRM**} & \multicolumn{6}{c}{**Proposed Double-Sided Rotor and stator FP-AFSRM**} \\ \hline \begin{tabular}{c} **Current** \\ **(Amps)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{zip}}\) (\%)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{avg}}\) (N)** \\ \end{tabular} & \begin{tabular}{c} **F\({}_{\text{avg}}\) (N)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{zip}}\) (\%)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{avg}}\) (N)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{avg}}\) (N)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{avg}}\) (N)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{avg}}\) (\%)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{avg}}\) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **T\({}_{\text{avg}}\) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **T\({}_{\text{avg}}\) (\%)** \\ \end{tabular} \\ \hline \(5\) & \(34.82\) & \(5.13\) & \(956.8\) & \(5\) & \(4.40\) & \(6.63\) & \(713.7\) & \(-30.42\) & \(29.23\) & \(-25.4\) \\ \(7.5\) & \(43.17\) & \(9.56\) & \(1305\) & \(7.5\) & \(4.91\) & \(13.03\) & \(1025\) & \(-38.25\) & \(36.26\) & \(-21.45\) \\ \(10\) & \(48.98\) & \(13.50\) & \(1501\) & \(10\) & \(9.63\) & \(18.42\) & \(1197.2\) & \(-39.35\) & \(36.43\) & \(-20.23\) \\ \(12.5\) & \(51.14\) & \(17.36\) & \(1671.2\) & \(12.5\) & \(10.06\) & \(23.85\) & \(1355.6\) & \(-41.08\) & \(37.32\) & \(-18.88\) \\ \(15\) & \(51.48\) & \(20.84\) & \(1825.2\) & \(15\) & \(10.34\) & \(28.52\) & \(1503.6\) & \(-41.14\) & \(36.85\) & \(-17.61\) \\ \hline \end{tabular}
\end{table} 
Table 4: The comparison of the proposed double-sided rotor and stator FP-AFSRM model and single-rotor FP-AFSRM. \begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{1}{c}{**Single-Stator and Single-Rotor FP-AFSRM**} & \\ \hline \begin{tabular}{c} **Current (Amps)** \\ \end{tabular} & \begin{tabular}{c} **T\({}_{\text{zip}}\) (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **T\({}_{\text{avg}}\) (Nm)** \\ \end{tabular} &
\begin{tabular}{c} **F\({}_{\text{avg}}\) (N)** \\ \end{tabular} \\ \hline \(5\) & \(34.82\) & \(5.13\) & \(956.8\) \\ \(7.5\) & \(43.17\) & \(9.56\) & \(1305\) \\ \(10\) & \(48.98\) & \(13.50\) & \(1501\) \\ \(12.5\) & \(51.14\) & \(17.36\) & \(1671.2\) \\ \(15\) & \(51.48\) & \(20.84\) & \(1825.2\) \\ \hline \end{tabular}
\end{table} 
Table 3: \(T_{\text{{arg}}}\), \(T_{\text{{rip}}}\), and \(F_{\text{avg}}\) values of single-stator and single-rotor FP-AFSRM.
## 3 ML for SatCom RRM: Architecture and Techniques
### Supervised Learning for RRM
#### 3.2.1 Dnn-Based RRM: Power, Beamwidth and Bandwidth Flexibility  
In this section, we study the DNN architecture for payload resource management onboard a multibeam satellite with \(B\) user beams. The system will respond to changesin traffic demand by modifying two radio resources: bandwidth and power for a limited number of beams. A training database is generated with different possible traffic demand values for the beams based in the traffic model explained in [32]. For each of these possible combinations of the required capacity in each beam, there is a label indicating the configuration of the resource allocation set that minimizes the cost function (1) and meets constraints (2)-(5) [31]. The cost function (i.e., the regularized logistic regression) for DNN training can be written as follows:  
\[J(\hat{Y},W)=-\frac{1}{m}\sum_{i=1}^{m}[Y^{(i)}\cdot\log\hat{Y}\Big{(}X^{(i)} \Big{)}+\Big{(}1-Y^{(i)}\Big{)}\log\Big{(}1-\hat{Y}\Big{(}X^{(i)}\Big{)}\Big{)} ]+\frac{\lambda}{2m}\sum_{j=1}^{k}W_{j}^{2} \tag{9}\]  
where \(m\) represents the number of training samples, \(X^{(i)}\) indicates the \(i\)th training example with possible required capacities in each beam, \(X^{(i)}=[X_{1}^{(i)},X_{2}^{(i)},...,X_{B}^{(i)}]\), \(Y^{(i)}\) denotes the \(i\)th configuration corresponding to \(X^{(i)}\), \(\hat{Y}(X^{(i)})\) is the configuration predicted for \(X^{(i)}\), \(k\) is the order of logistic regression and \(\lambda\) is the regularization parameter to avoid overfitting. Logistic regression optimizes the log loss for all observations in which it is trained, which is equivalent to optimizing the average cross entropy.  
In this sense, Figure 4 serves as a reference for understanding how the problem is addressed. The vector \(X=[X_{1},X_{2},\ldots,X_{B}]\) represents the \(B\) (number of beams) inputs of the Neural Network. Vector \(\hat{Y}\) represents the \(L\) outputs of the Neural Network, and the \(L\) outputs correspond to the set of possible resources allocated to the each of the \(B\) beams.
## 3 Results and Discussion
### Determination of the Optimal Immersion Time  
When cassava bagasse is subject to immersion in an OD process under agitation conditions of 320 rpm, bagasse particles are released into the osmotic solution, generating a greater contact surface area that causes greater efficiency of the dehydration process, rapidly reaching equilibrium.  
Figure 7 shows that after 5 min of treatment, the bagasse sample loses almost all the possible moisture in all the tests, quickly tending to equilibrium; however, from a time of 10 min, stability can be observed in the obtained moisture values.  
Based on this, an immersion time of 10 min was defined and used to carry out all subsequent tests, corresponding to the application of OD as pretreatment of convective drying. In the state of the art immersion times of 1, 1.5, 2, 4, 5, 12, 16, 24, and up to 48 h were found [41; 42; 39; 43; 44; 1; 31; 39]. To reach equilibrium, long periods of time are generally needed. Nonetheless, it has been found that mass transfer is not significant after 4 to 5 h [31]. Process  
Figure 6: Plot of main effects.  
Figure 7: Moisture during OD.  
time depends on the changes to be achieved in the raw material: impregnation, osmotic pretreatment, OD, production of candidied fruit [31], and its nature, form, and composition.
## 2 Design  
### RF Switch  
An RF switch for the K-band has been studied using a light-controllable photoconductive element made by intrinsic silicon wafer [19]. The RF switch based on the photoconductive element is expensive and consumes a DC power of 200 mW. An RF switch using a beam lead PIN diode and switchable HMSIW unit operating in the frequency band from 4.6 to 5.3 GHz has been presented [18]. Its structure can be applied to K-band applications; however, it requires sufficient space for the DC circuits and consumes a DC power of 90 mW.  
Figure 1: Beam pattern according to switch state.  
In this article, an RF switch using beam lead PIN diode networks of excellent on/off characteristics and a DC power consumption of a few milliwatts was proposed at the K-band. A schematic of the proposed RF switch operating as a single-pole double-throw (SPDT) and PD for beamwidth reconfiguration is shown in Figure 2. The proposed SPDT/PD for the K-band consists of a T-junction PD, \(\lambda\)/4 lines, and diode networks. In this design, the beam lead PIN diodes DSM8100-000 manufactured by the Skyworks Company (Irvine, CA, USA), which can operate up to 25 GHz, were used [20; 21]. The diode could be modeled as a resistance of 4 \(\Omega\) for the ON state and as a parallel circuit with a capacitance of 0.03 pF and a resistance of 20 k\(\Omega\) for the OFF state [21]. Each diode network consisted of a K-band short-circuited radial stub, PIN diode, and transmission line, and was controlled by a DC bias network containing a current control resistor of 2 k\(\Omega\) for low DC power consumption. The designed shut type diode network with the effect of the parasitic inductance reduced by the transmission line was superior on/off characteristics to the series type diode network for the K-band. The cathode of the PIN diode was placed toward point A (A\({}^{\prime}\)) in Figure 2 and connected to a ground via, which resulted in a reduction in the insertion loss and circuit size, owing to the absence of a DC block circuit. The OFF and ON states of the diode were controlled with the voltages of 0 V and 3 V, respectively. The diode network at point A (A\({}^{\prime}\)) in Figure 2 served as an open or a short circuit according to the control voltage of 0 V or 3 V, respectively, and each impedance at point A toward antenna 1 (2) was transformed by a \(\lambda\)/4 line between A-B (A\({}^{\prime}\)-B), denoted as TL2 in Figure 2, into 50 \(\Omega\) or \(\infty\) at point B, respectively. Therefore, the operational state of the antenna could be determined by the state of the diode network connected to the antenna feed line. The proposed RF switch for antenna feed control served as the SPDT with the operation state of \"Ant1 ON, Ant2 OFF\" or \"Ant1 OFF, Ant2 ON\" and the PD with the operation state of \"Ant1 ON, Ant2 ON\". A \(\lambda\)/4 impedance transformer with a characteristic impedance of 41.5 \(\Omega\), denoted as TL1 in Figure 2, was adopted, satisfying the 10-dB impedance for both SPDT and PD operations.  
Figure 2: Proposed RF switch for beamwidth reconfiguration.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Exploring the Impact of Environmental Zones and Camera Rotation Angle
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Category** & \(ACC\) & \(PR\) & \(REC\) & \(F_{1}\) & \(\overline{F_{1}}\) \\ \hline
**1.(a)** Rural & 83.6\% & **82.3\%** & 78.4\% & **80.3\%** & **78.2\%** \\ \hline
**1.(b)** Urban/peri-urban & **85.4\%** & 78.4\% & **78.7\%** & 78.5\% & 76.9\% \\ \hline
**2.(a)** No sky & **85.2\%** & **81.6\%** & 78.0\% & **79.8\%** & **77.9\%** \\ \hline
**2.(b)** With sky & 84.4\% & 76.1\% & **79.5\%** & 77.7\% & 76.1\% \\ \hline All & 84.9\% & 79.5\% & 78.6\% & 79.1\% & 77.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for the categories of images existing in the Flood Area dataset. The images were divided according to the environmental zone into **1. (a)** rural, and **1. (b)** urban/peri-urban, and according to the camera rotation angle into **2. (a)** no sky (low angle), and **2. (b)** with sky (high angle).
## 3 Cascaded Droop Control to DFIG Integrated through VSC-HVDC for Frequency Regulation  
With the DFIG integrated into the system through the VSC-HVDC, the transmission of the system frequency signal to the DFIG needs telecommunication across the VSC-HVDC, which has the drawbacks of low reliability and high latency. The cascaded droop control from system frequency to the DC voltage, then to DFIG's output, as given in Figure 3, is a local control strategy, where \(f_{\text{wt}}\) is the frequency at the DFIG side, \(K_{\text{fwt/UDC}}\) is the droop coefficient between \(f_{\text{wt}}\) and \(U_{\text{DC}}\).  
Figure 3: Frequency regulation by DFIG integrated through VSC-HVDC.  
Figure 2: Change of active power output target of SG at time steps of DPF model.  
With the virtual inertia control applied to the DC capacitor, the DC voltage varies as the DC capacitor releases stored energy to provide active power support. The DFIG adopts the DC voltage deviation as the input signal of the droop control, to adjust its active power output to participate in the frequency regulation.