esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Solution
The flows of this group are further classified based on the additional factors of the third group (lines 15-22). The second group flow cycle has an additional factor \(\frac{lcm_{pre}}{lcm_{max}}\). The third group contains the flows that are intermixed with other flow cycles. Flows in this category require a separate path for transmission and cannot share links with other flows. Moreover, flows in the same group can share the same link. The function \(generateFlowPartition\) can be easily implemented based on the flow class index. Finally, flows in each group are sorted in ascending order (line 24). The FC is a strategy that can be superimposed on any routing and scheduling algorithms based on finer-grained classification. Figure 3: Schedule for three flows on the \(sw_{0}\)’s output port. The squares symbolize transmission time slots for the respective flow on the link (\(sw_{0}\)4 and \(sw_{2}\)). Figure 2: A scenario where four time-triggered flows traverse through one route \([sw_{0},sw_{2},\) and \(sw_{4}]\) generated by SRP routing algorithm. ```
Input: flow collection \(F\), flow period set \(\mathbf{prd^{F}}\) Output: An ordered set of flows by flow partitioning \(\mathcal{G}\)
1\(lcm_{pre}=LCM(\mathbf{prd^{F}})\)
2 for\(f_{i}\in F\)do
3\(lcm_{new}=LCM(\mathbf{prd^{F}})\)
4 if\(lcm_{new}==lcm_{pre}/f_{i}\):\(prd\) or \(f_{i}\):\(prd\) is primethen
5\(f_{i}\):\(class=3\)
6 end if
7 if\(lcm_{new}==lcm_{pre}\)then
8\(f_{i}\):\(class=1\)
9\(tmp\):\(append(f_{i})\)
10 else
11\(f_{i}\):\(class=2\)
12\(flow\_with\_factor[\frac{lcm_{pre}}{lcm_{new}}]=f_{i}\)
13 end if
14
15 end for
16 for\(f_{i}\in tmp\)do
17 for\(factor\in list(flow\_with\_factor.keys())\)do
18 if\(GCD(factor,f_{j}\_prd)!=1\)then
19\(f_{j}\):\(class=2\)
20\(tmp\):\(remove(f_{j})\)
21 end if
22
23 end for
24
25 end for
26\(\mathcal{G}=generateFlowPartition(F)\)
27\(\mathcal{G}\leftarrowsorted(\mathcal{G})\) by ascending order
```  
**Algorithm 1**Flows classification algorithm  
Then, we focus on the time-sensitive routing and scheduling constraints as well as the optimization objective, which is used for ILP in the next part.
## 2 Material and Methods
### Countermovement Jump and Rate of Force Development  
During the second day, athletes visited the laboratory for CMJs and isometric leg press RFD. Measurements were performed during morning hours, and athletes were instructed to be well-fed and to avoid any training 24 h before measurement. After a 10 min warmup on a treadmill at approximately 7-9 km\(\cdot\)h\({}^{-1}\) and some full body dynamic stretching, athletes performed 4 CMJs with submaximal intensity, with 1 min rest between jumps. Then, athletes performed 5 maximum CMJs, with 2 min rest between attempts. All CMJs were performed on a force platform (WP800; A/D sampling frequency, 1 kHz; Applied Measurements Ltd. Co, Reading, UK) with arms akimbo. Athletes were instructed to jump as high as possible, and after each jump, they were informed of their jump height, which served as an incentive to jump even higher. All CMJs recorded from the force platform were analysed (Sensor Interface PCD-320A; Kyowa Electronic Instruments, Tokyo, Japan) to calculate the maximum vertical jump height, power output and power relative to body mass during the push-off phase [32]. The best jumping height performance was used for statistical analysis. The ICCs for CMJ height, force, power and power relative to body mass were as follows: 0.97, (95% CIs: lower = 0.850; upper = 0.993), 0.97 (95% CIs: lower = 0.849; upper = 0.994), 0.99 (95% CIs: lower = 0.991; upper = 0.998) and 0.99 (95% CI, lower = 0.992; upper = 0.999), respectively.  
Fifteen minutes after the CMJs, athletes performed the isometric leg press RFD. Athletes were seated on the leg press and placed their feet on a force platform (WP800, A/D sampling frequency 1 kHz; Applied Measurements Ltd., Co), which was mounted vertically on the laboratory wall. Knee angle was set at 120\({}^{\circ}\), and hip angle was set at 110\({}^{\circ}\)[33]. Warmup included 2 attempts with progressive application of force and 2 more attempts as fast as possible but only for 1 s duration. Then athletes performed 4 maximum attempts with 3 s duration. Athletes were instructed to apply their force as fast as possible, and a real-time monitor showed the application of their force. Data from the force platform were recorded (Sensor Interface PCD-320A; Kyowa Electronic Instruments, Tokyo, Japan) and analysed for calculation of specific time windows of 0-30, 0-50, 0-80, 0-100, 0-150, 0-200 and 0-250 milliseconds [33]. The best performance according to overall RFD was used for statistical analysis. The ICCs for RFD time windows were as follows: RFD 30 milliseconds = 0.92 (95% CIs: lower = 0.374; upper = 0.989), RFD 50 milliseconds = 0.96 (95% CIs: lower = 0.385; upper = 0.994), RFD 80 milliseconds = 0.818 (95% CIs: lower = 0.818; upper = 0.995), RFD 100 milliseconds = 0.98 (95% CIs: lower = 0.890; upper = 0.996), RFD 150 milliseconds = 0.98 (95% CIs: lower = 0.940; upper = 0.998), RFD 200 milliseconds = 0.98 (95% CIs: lower = 0.871; upper = 0.996) and RFD 250 milliseconds = 0.97 (95% CIs: lower = 0.778; upper = 0.995).
## 5 Use Cases of Attack Detection with Matana
### Use Case: Return-Oriented Programming Attack Detection
#### 5.3.1 Technical Background  
ROP [36] is an example of code reuse attacks. It manipulates return addresses to redirect the control flow to a series of small pieces of assembly code (called gadgets) chosen by the attacker from the target executable code itself or shared libraries. These gadgets usually execute elementary actions, such as performing a computation on a register, and end with a return instruction. This return instruction jumps to the caller function based on a return address previously provided by the caller function; however, as the attacker controls these return addresses, the return instruction actually jumps to a specific address chosen by the attacker to continue the execution. The gadgets are run just as if they form part of the victim program--with the same privilege, in the same memory zone. Unlike traditional buffer overflow attacks, code reuse attacks do not write the code to be executed into the memory, thus circumventing the protection of the type \"Write xor Execute\", which disables the executable stack. Other variants of ROP also exist, which do not use return addresses but function calls or indirect jumps to link gadgets.  
ROP attacks can be carried out in RISC-V platforms [37]. Unlike in x86, the return address in RISC-V is not read from the stack, but from a register called ra. When calling a function, the caller puts the current address into ra; when the function returns, it executes the instruction ret (alias of jalr zero, 0(ra)), which jumps to the address pointed by ra. In case of nested function calls, the content of the ra register is pushed on the stack and restored when necessary. This allows an attacker who controls the stack to also control the return address of the gadget in the function. One sample gadget on RISC-V is shown in Listing 2.  
\begin{table}
\begin{tabular}{l r r r r r r r} \hline \hline \multirow{2}{*}{**Resources**} & \multirow{2}{*}{
\begin{tabular}{c} **Base SoC** \\ **(Unprotected)** \\ \end{tabular} } & \multicolumn{2}{c}{**InstTimer**} & \multicolumn{2}{c}{**MemAccess**} & \multicolumn{2}{c}{**InstTimer + MemAccess**} \\ \cline{3-8}  & & **Module Alone** & **Total** & **Module Alone** & **Total** & **Module Alone** & **Total** \\ \hline Flip-Flops & 22,167 & 2448 & 24,615 & 2172 & 24,339 & 2669 & 24,836 \\ LUTs & 31,668 & 1620 & 33,288 & 946 & 32,614 & 1354 & 33,022 \\ BlockRAM & 26 & 0 & 26 & 0 & 26 & 0 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Area footprint of MATANA for Prime + Probe.  
```
1 gadget_a7 plus1: addia7,a7,1;Elenentalaction
3 ldra,0(sp);Readthenextgadgetaddressfromthestacktora
4 addisp,sp,8;Incrementthestackpointer
5 ret;Jumptothennextgadgetaddressstoredinra
```  
Listing 2: Example of a gadget in RISC-V built with our ROP generator: this simple gadget adds 1 to the register a7 and can be used, for example, to prepare the value of a7 during a ROP chain execution.
# Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks
## 3 The Proposed Methodology
### Model Training and Attack Classification  
The FedSec incorporates GCNN-GRU to construct the model parameters and classify the various attacks in the MC-SDN infrastructure or data plane layer. It is further divided into two parts that are GCNN-GRU-based model training, and model retraining and attack classification. The FedSec performs model training and attack classification using the enriched NSL-KDD dataset.  
In the experiment, hyperparameters play a paramount role in the decision-making to build and train the GCNN-GRU model. To provide the generalization ability of the model, the parameters of the GCNN-GRU model are given in Table 2. For the implementation of the GCNN model, this work employs the GeLU and Tanh activation functions that enable the learning of complex and nonlinear input patterns. Moreover, the Adam optimizer in GCNN facilitates bias correction during the training of spatial dependencies. The application of adadelta in GRU aids in capturing the temporal dependencies over the timesteps. Moreover, the ReLU activation function in the GRU leverages the mitigation of the vanishing gradient problem in the recurrent networks.
# Assist-as-Needed Controller of a Rehabilitation Exoskeleton for Upper-Limb Natural Movements
## 3 Experimental Result
### Parameter Identification of Friction Model  
To simplify the measurement process, each joint was mounted on a horizontal bracket for data collection prior to exoskeleton assembly. At this stage, the joint can rotate freely without doing any work against gravity. The collected data are then used to fit the friction  
Figure 6: Torque response of J6. (**a**) Torque tracking curve. (**b**) bode diagram of amplitude of torque closed-loop. (**c**) bode diagram of phase of torque closed-loop. torque model. In this experiment, 46 joint velocities were selected to generate uniform velocity excitation trajectories, with each velocity repeated three times. To reduce signal noise, a fourth-order Butterworth low-pass filter with a cutoff frequency of 10 Hz was applied to preprocess current signals. A zero-phase filter ensured that there was no delay in filtered data. Table 2 presents a comparison between three friction models. M1, M2, and M3, corresponding to (5), (6), and (7), respectively. f represents the forward rotation of the motor, and r represents the reverse rotation of the motor. Figure 7 shows the fitting results of three models. M1, with its linear term, cannot accurately describe joint friction at both low and high speeds. M2 improves the fitting performance at low and high speeds by modeling the Stribeck effect and the nonlinear relationship between viscous friction and speed. However, M2's fitting curve shows two turning points at low speeds, which does not match the single extreme point of the actual friction model. Based on M2, M3 uses a linear equation to fit the data before the first extreme point of M2 and replaces the original curve, achieving better fitting results. Table 2 shows that the M3 model consistently performs with high \(R^{2}\) values under all testing conditions, particularly under the J2 test condition, where the \(R^{2}\) values are 0.9994 and 0.9974, demonstrating the best fit. \(RSME\) is a metric used to assess the prediction accuracy of a model, with smaller values indicating less error and higher precision. Table 2 reveals that M3 performs the best in terms of RMSE, especially under the J3 test condition, where the RMSEs are 0.5211 and 0.5945, significantly lower than those of other models. Across all test conditions, the \(RSME\) of M3 is generally lower, indicating minimal prediction error and high accuracy. Specifically, in the J3 test, the RMSE of m3 is 0.5211, which is 78.9% lower than that of M1 (2.4775) and 55.6% lower than that of M2 (1.1752). Overall, M3 consistently exhibits the lowest \(RSME\) across all test conditions, indicating minimal prediction error and superior precision. RSD \(\sigma\) (%) is used to measure the stability of the model results, with smaller values indicating more stable outcomes. M3 shows low RSD values under all test conditions, particularly under the J2 and J5 test conditions, where the values are 0.5681 and 0.3794,
## 5 Conclusions  
In this work, the effects that the array spatial distribution, transmit PW sequence, and apodization law have on CoMTUS imaging performance have been investigated using both simulations and experiments. The findings show that CoMTUS spatial resolution is mostly defined by the size of the effective aperture created rather than the maximum transmitted PW angle, and that compounding PWs at different angles may aid in reducing side lobe energy. In addition, the use of apodization on transmit reduces the cross-talk artifacts without degrading spatial resolution and adding weighted compounding further decreases artifacts and helps to compensate the differences in the brightness across the image. Thus, an optimum relative location and scan sequence of the arrays can produce images with improved resolution while maintaining high-frame rates. In practice, the relative spatial position of the multiple arrays and the scan sequence should be adapted for the application. This study could be considered as a user's guideline to quickly determine the imaging performance trade-off and compare with the specific application requirements.  
Conceptualization, L.P.; methodology, L.P. and D.M.; software, L.P., D.M., A.R. and P.T.; validation, L.P. and D.M.; formal analysis, L.P., A.R. and J.V.H.; investigation, L.P., K.C.-J., A.R. and J.V.H.; resources, L.P. and J.V.H.; data curation, L.P.; writing--original draft preparation, L.P.; writing--review and editing, L.P., D.M., K.C.-J., A.R., P.T. and J.V.H.; visualization, L.P.; supervision, L.P. and J.V.H.; project administration, L.P. and J.V.H.; funding acquisition, L.P. and J.V.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Royal Society (URF/RI/211049) and Wellcome Trust/EPSRC iFIND project IEFI Award (102431) (www:iFINDproject.com). The University of Florence acknowledges the contribution of the National Recovery and Resilience Plan, Mission 4 Component 2--Investment 1.5--THE--Tuscany Health Ecosystem--funded by the European Union--NextGenerationEU--(CUP B83C22003920001) and the contribution of the project PRIN 2020--CONUS (grant number 20205 HFXE7)--funded by the Italian Ministry of Education, University and Research (CUP B13C21000190005).  
Not applicable.  
The original contributions presented in the study are included in the article further inquiries can be directed to the corresponding author.  
The authors would like to thank the members of the QUIIN lab at King's College London for the helpful discussions.  
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.
###### Abstract  
The biodegradation effect and pathway of unsymmetrical dimethylhydrazine (UDMH), which is a major rocket propellant with highly toxic properties, with two strains isolated from the acclimated activated sludge were investigated in solution and in soil. The results demonstrated that _Stentrorophomonas_ sp. M12 (M12) was able to degrade UDMH of 50 mg\(\cdot\) L\({}^{-1}\) as the sole carbon source in aqueous mineral salt medium (MSM), but could not degrade UDMH in soil. _Comanomons_ sp. P4 (P4) barely degraded UDMH of 50 mg\(\cdot\) L\({}^{-1}\) as the sole carbon source in aqueous MSM, but the degrading capacity of P4 could be improved by the addition of an extra carbon source. Meanwhile, P4 was able to degrade UDMH of 100-600 mg\(\cdot\) kg\({}^{-1}\) in the soil. The degradation of UDMH in the soil was influenced by organic matter, autochthonous microorganisms, and metal ions. UDMH could inhibit metabolism of M12 and P4, and the inhibition influence was more severe in aqueous MSM than in soil. Oxygen content was important for M12 biodegradation UDMH, and co-metabolism helped P4 to self-detoxify and self-recover. The main intermediates of UDMH were identified by Gas Chromatography-Mass Spectrometer (GC/MS) qualitative analysis, and the concentrations of UDMH and its important transformation products were determined in solution and soil. According to the determination results, the synchronous degradation theory was proposed, and the degradation pathway was discussed.  
unsymmetrical dimethylhydrazine (UDMH); biodegradation; soil; solution +
Footnote †: journal: Applied Physics Letters B
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 2 Materials and Methods
### Sensors on the UAS Data Ferry and Node Stations
#### 2.3.2 Node Station Sensors  
The CR1000X (Campbell Scientific, Logan, UT, USA) datalogger provides measurement and control for a wide variety of applications because of its reliability and ruggedness. Applications include weather stations, mesonet systems, wind profiling, air quality monitoring, hydrological systems, water quality monitoring, and hydrometeorological stations. The electronics of CR1000X are radiofrequency shielded by a unique sealed, stainless-steel canister. The data logger is equipped with a battery-backed clock that assures accurate timekeeping. The secondary RF 450 radio was attached to the CR1000 datalogger in each node station. See [PERSON], [34] for details of the operation.  
The GS-1 sensor (METER Group Inc., Pullman, WA, USA) was used to measure the soil water content (as described in [PERSON] et al. [35]) every 15 min as an average with a sampling frequency of 5s. It is a recently developed capacitance and frequency domain technology-based sensor with a rugged, durable design configured with two parallel waveguide rods (5.2 cm in length).  
SI-111 infrared radiometer (Apogee Instruments, Inc., Logan, UT, USA) measures emitted infrared radiation (within an atmospheric window of 8-14 \(\upmu\)m) from which target surface temperature is remotely determined. The infrared radiometer (also known as an infra-red thermometer or IRTs) monitors the maize field surface temperature continuously every 15 min as an average with a sampling frequency of 5 s [36; 37].
## 3 Proposed Architecture
### Experimental Design  
By using the OPPORTUNITY dataset, three comparative experiments were conducted with different configurations and parameters. In Experiment 1, the Deep ConvLSTM network was developed to train the original dataset, consisting of 113 channels of accelerometers and IMU sensors. Data were normalized, and linear interpolation was performed to fix the missing data. The sliding window approach has been used to increase the number of samples required for training and testing. The training process is summarized in Figure 3. In Experiment 2, the undersampling method was applied in the data preprocessing stage where the NULL class was resampled prior to model training. In Experiment 3, the number of sensors in the dataset was reduced to only five IMU sensors, consisting of RUA, RLARLA, BACK, LUA, and LLA. Using only the five IMU sensors, the number of features used for model development were also reduced. In addition, the number of IMU sensors required in the deployment phase were reduced.
## 4 Results and Discussion
### Performance of the Proposed Classifier  
To demonstrate the performance of the proposed hybrid classifier, ten thousand ECG segments in total were utilized for testing the performance of LPC and the training process to obtain the SVM model, among them five thousand normal and abnormal heartbeats.  
As a result, the proposed LPC can handle more than 23% of the abnormal beats and achieve an average filtering ratio of 11.5%. For LPC, we experimented with relaxing the comparison threshold. Although this would increase the proportion of LPC classifications and reduce the probability of activating high-performance classifiers, the accuracy loss caused by relaxing the threshold is unacceptable. Therefore, we chose to give LPC a relatively strict threshold. As a result, the classification accuracy of LPC is also high, at 98.86%.  
For the SVM classifier, three thousand ECG segments were used for the testing process. Table 2 presents the performance of the algorithm for data types double and 32Q16. Additionally, in order to reduce power consumption and save memory storage, we experimented with using the INT16 data type, but this resulted in unacceptable performance degradation. Therefore, we chose the 32Q16 data format. As a result, the detection results showed that it can achieve 97.34% overall detection accuracy for the task of ECG normal/abnormal heartbeat classification.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref** & **Methodology** & **SE** & **+P** \\ \hline
[46] & Adaptive derivative-based R-peak detection & 98.2\% & 99.3\% \\
[37] & Haar DWT-based R-peak detection & 99.73\% & 99.94\% \\
[15] & Pan-Tompkins algorithm based R-peak detection & 99.76\% & 99.56\% \\ Our method & Hilbert transform based R-peak detection & 99.83\% & 99.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: R-peak detection performance comparison with other methods.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Types** & **Train Number** & **Test Number** & **Accuracy** & **Accuracy** \\ \hline Normal & 5000 & 1500 & 99.32\% 1 & 99.44\% 2 \\ Abnormal & 5000 & 1500 & 95.88\% 1 & 95.24\% 2 \\  & 10,000 & 3000 & 97.6\% 1 & 97.34\% 2 \\ \hline \hline \end{tabular} \({}^{\ddagger}\) Use the Matlab double data format. 2 Use the 32Q16 data format.  
\end{table}
Table 2: Normal/abnormal classification outcomes of ECG heartbeats.