esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Anticancer Activity of the Malloapelta B-Nanoliposomal Complex against Lung Carcinoma
###### Abstract  
Previous studies regarding malloapelta B (malB), a natural compound isolated from the Vietnamese medicinal plant, showed a strong NF-kB inhibitory effect, making it a promising source for the development of novel anticancer drugs. However, similar to many other natural compounds from plants, malB has several disadvantages for clinical applications, including high toxicity and low solubility. To improve its bioavailability, malB was conjugated into nanoliposomes, which are ideal drug carriers. The formulations with 1,2-dipalmitoyl-sn-glycero-3-phosphocholine, mPEG-cholesterol, malB, with or without cholesterol exhibited nanoliposomes with an average diameter of approximately 76.98 nm, PDI of 0.28, zeta potential of \(-\)5.53 mV, and the highest encapsulation efficiency of 78.73% \(\pm\) 9.5%. These malB-nanoliposomes inhibited the survival of all lung cancer cell lines examined with IC\({}_{50}\) values ranging from 11.86 to 13.12 uM. Moreover, malB-nanoliposomes showed stronger inhibition of A549 colony-forming activity compared to that of the free compound. The effects of malB and its nanoliposomal formulation may be mediated through activation of apoptosis by the significant induction of caspase 3 activity. The nanoliposomal formulations also showed potential to inhibit tumor growth (37.03%) and prolong survival (32.20 days) of tumor-bearing mice compared with the unloaded drug (\(p<0.05\)). The improved antitumor activity of malB-nanoliposomes suggests their promising clinical applications.  
apoptosis; caspase 3; Lewis lung carcinoma; malloapelta B; nanoliposome +
Footnote †: journal: Applied Physics Letters
## 4 Results and Analysis
### Throughput (kbps)  
Figure 6 shows the throughput of the network when the nodes are transferring the data packets. As shown in the figure, the proposed technique and the native AODV are compared in the presence of malicious nodes in the network. When any two normal nodes are communicating and any malicious node sends the false routing information, claiming that it has a valid route when it actually wants to drop the data packets, it decreases the throughput. Another reason for this is the mobility of the nodes, which directly affects the performance of the network, as the movement of the nodes causes link breakage, leading to a decrease in the throughput of the network. Table 6 shows the improved results reported based on throughput (kbps) of the proposed technique. As in our proposed technique, all of the IDS nodes send status packets to which every node responds positively. If any node does not satisfy the pre-defined conditions then the IDS node marks it as a malicious node and the other nodes stop communication with that particular node.
# Interval Extended Kalman Filter--Application to Underwater Localization and Control
###### Abstract  
The extended Kalman filter has been shown to be a precise method for nonlinear state estimation and is the facto standard in navigation systems. However, if the initial estimated state is far from the true one, the filter may diverge, mainly due to an inconsistent linearization. Moreover, interval filters guarantee a robust and reliable, yet imprecise and discontinuous localization. This paper proposes to choose a point estimated by an interval method, as a linearization point of the extended Kalman filter. We will show that this combination allows us to get a higher level of integrity of the extended Kalman filter.  
Extended Kalman Filter; interval filter; interval analysis; localization; nonlinear control; state estimation; underwater robotics +
Footnote †: journal: Applied Soft Matter Communications  
1
## 3 Results and Discussion
### Evolution of the Volatile Compounds during Dehydration  
In order to intuitively visualize the evolution pattern of volatile compounds during on-vine grape dehydration, their contents (expressed as ug/L and ng/berry) were analyzed by hierarchical clustering, and the results are presented as heatmaps in Figure 3a,b. In the heat maps, each column corresponds to a dehydration degree (T1, T2, T3, T4, and T5), each row represents a compound, and each cell color reflects the content level of the compound, where red indicates a high content level and blue indicates a low level. As the columns are arranged according to the time sequence of sampling, the color variation in each row reflects the evolution of a compound during the dehydration process. All volatiles were hierarchically clustered based on their evolution pattern, and are illustrated in the dendrogram on the left of each heatmap.  
Figure 3: Hierarchical cluster heatmaps of free, bound, and total volatile compounds expressed by \(\mu\)g/L in grape juice (**a**) and ng/berry in every single berry (**b**). The corresponding compounds for the abbreviations are listed in Tables S1 and S2. T1, T2, T3, T4, and T5 represent the sampling points at different dehydration degrees.  
To the best of our knowledge, few studies have investigated the evolution of volatiles during grape dehydration using the two expressions of \(\upmu\)g/L and ng/berry simultaneously. The heatmaps indicated that the evolution patterns of volatile compounds were completely different, according to these two compound content expressions.
## 4 Discussion
### Reference Evaptranspiration (ETo) Estimates
Understanding these limitations is crucial for selecting appropriate ETo models in different climatic contexts and ensuring the robustness of irrigation recommendations. Selecting the appropriate ETo model depends on specific climatic conditions and the availability of meteorological data. For arid and semi-arid regions, the Hargreaves-Samani model is preferred due to its reliance primarily on temperature data, which are commonly available in such environments. In contrast, for humid climates, the Priestley-Taylor model is more suitable as it accounts for the energy balance, which plays a significant role in these areas. The Blaney-Criddle model is recommended for temperate climates, where additional meteorological data, such as wind speed and humidity, are available, allowing for more accurate estimates. We also acknowledge the limitations of these models, particularly in terms of their spatial and temporal resolution, which may impact their applicability in regions with complex microclimates. To address these limitations, we suggest incorporating high-resolution satellite data to refine the accuracy of the models. Additionally, we highlight the importance of local calibration and validation of these models to ensure their accuracy in specific agroecological conditions. Lastly, we propose that future research focus on integrating machine learning techniques with traditional ETo models, which could improve their adaptability and predictive capabilities across different agricultural regions. Integrating ETo models with NDVI-derived Kc values enabled the dynamic management of crop water requirements. By adjusting irrigation schedules based on seasonally varying Kc values, this study demonstrated how irrigation practices could be refined to ensure that water is applied efficiently, reducing the risk of over- or under-irrigation. However, the variability in ETo estimates among the models highlights the importance of selecting the right model for specific irrigation decisions. The differences observed among the Hargreaves-Samani, Blaney-Criddle, and Priestley-Taylor models, especially during the dry season, suggest that relying solely on one model in regions with significant seasonal changes in temperature and precipitation could lead to less efficient water use.
# Wavelet Time-Frequency Analysis on Bridge Resonance in Train-Track-Bridge Interactive System
###### Abstract  
With the continuous improvement in the operation speed of trains, the impact of train-induced vibration through the track on the bridge is increasingly prominent. In particular, when the loading frequency is the same as or close to the natural frequency of the bridge, the resonant response of the bridge will be activated, which will probably endanger the safety of the operation and the bridge structure. Normally, the traditional method to indicate the appearance of resonant response is to analyze the frequency spectrum of the response through the Fourier transform from its time history. However, it can simply reflect the contribution of different frequency components within a stationary window. Therefore, continuous wavelet transform is adopted on a 2D train-track-bridge interactive system in this article. It illustrates the evolutionary characteristics of different frequencies from the input excitation to the output response during the bridge resonance in the time-frequency domain, compared with the cases when the bridge is nonresonant. Finally, the article demonstrates the feasibility of the method. It concludes that the resonance and quasi-resonance-triggering band accounts for the highly intensified bridge response, while the staggering domination between the steady-state and the transient response is the main phenomenon for the nonresonant bridge. Additionally, within the low-frequency band, the resonant bridge will have a more significant impact on the track subsystem than the train subsystem.  
wavelet transform; bridge resonance; train-track-bridge interactive system; time-frequency analysis 2022
# Novel Dual Beam Cascaded Schemes for 346 GHz Harmonic-Enhanced TWTs
## 2 HHTWT  
### Operating Principle of HHTWT  
A HHTWT operating at 346 GHz is introduced firstly, which utilizes FWG as SWS, FWG is a promising type of SWS with wide bandwidth and high power. Compared with other conventional SWSs such as helix, FWG is of easy fabrication and assembling. Within it, the wave transmission path can be folded back upon itself multiple times, with a beam tunnel passing through its center. Energy exchange is achieved by synchronizing both the longitudinal energy flow speed and the electron beam velocity.  
The schematic of the HHTWT SWS is shown in Figure 1. Port 1 and 4 are input and output ports, respectively. Attenuators are applied to match two severed ports (Port 2 and 3). The electron beam is sent from the electron gun into the tunnel. The SWS consists of three sections: Modulation section, drift tube, and radiation section. The modulation section operates at E band. The radiation section operates at THz band, corresponding to the fourth harmonic of input signal. There is also a drift tube between the modulation section and radiation section, where the EM wave is cutoff and only electron beam can pass.  
In the modulation section, the velocity of electron beam is modulated by the E-band input signal. When the electron beam traverses the drift tube, the velocity modulation is transformed into longitudinal density modulation. If the cutoff frequency of the radiation section is 300 GHz, the fundamental wave and lower-order harmonic waves can be cut off in the radiation section, and then only high-frequency EM waves are excited and amplified by the high-order harmonic beam current. Hence, when inputting a signal at 86.5 GHz, we can get a 346 GHz output signal.  
Compared with conventional FWG TWT, HHTWT is featured by adopting two high frequency structures operating in two different bands. The power of conventional THz band FWG TWT is restricted due to the high loss. It leads to low gain and overlap SWS. Generally, the low input power of THz source could exacerbate the deleterious effects. The introduction of modulation section could modulate the electron beam efficiently. It is also instructive to mitigate the demand for a THz high-power signal source, by using a millimeter-wave high-power source.
## 4 Results and Discussion
### Results of Different Estimators
The comparison between GA and DE algorithms fluctuated, which was a result of the combined effect of noise and the CFO estimation accuracy. The noise uncertainty led to a performance undulation with the same SNR for both estimators. With an increase in the number of result statistics or SNR, the gap between the DE and GA estimations became apparent, as was evidenced by the results at 20 dB. The results of the three algorithms when the CFO values varied in the form of a sinusoidal function with the same computing times are shown in Figure 4. By comparing the data in Figures 2 and 4, similar conclusions can be drawn: the estimation accuracy of the DE algorithm was significantly better than those of the SAMGA and GA. The mismatch between population size and mutation operation adopted by the SAMGA for the TVCFO estimation caused it to have the worst performance of the three estimators. In addition, it should be noted that for the SAMGA estimation, the RMSE of the CFO value with the sinusoidal function was roughly an order of magnitude worse than that in the polynomial function. The difference in the estimation accuracy also led to distinct demodulation results. As seen by the BER statistics for the SAMGA estimator in Figure 5, the demodulation results were almost unacceptable because of the large deviation in the CFO estimation. Additionally, the CFO estimation bias of the SAMGA estimator fluctuated very little for different SNRs. Therefore, the BER improvement seen in Figure 5 came  
Figure 2: Performances of three estimators on the improved model with CFO values varying in polynomial function. merely from the increase in the SNR. The BER curves for the GA and DE estimators further corroborated the conclusions we drew above: the demodulation performances of the DE and GA estimators were dominated by the SNR in the cases with low SNRs, whereas under high SNR conditions, the quality of the TVCFO estimation was reflected in the BER results. The RMSE of the DE estimator was 36.13% smaller than that of the GA estimator when the SNR was 20 dB, which corresponded to a 75% improvement in the BER after demodulation. Based on an analysis of the results of the simulation experiments with the three estimators using two different variations of CFO values, we can safely conclude that, because the dependencies between adjacent variables were considered by the DE algorithm and not the other intelligent optimization algorithms (the GA and SAMGA), it would be appropriate to use the DE algorithm to estimate the TVCFO in underwater acoustic communication.
## 5 Experimentation: UGR'16 as a Case Study
### Experimental Environment  
The experimental environment was devised to run the proposed methodology. All the methods and functions have been developed in python language under the well-known python framework _scikit-learn_ for data science. Additionally, a set of tools called _scikit-optimize_, also developed in python, was used to perform the hyper-parameters selection using Bayesian optimization. In this sense, they are all together included in the project ff4 ml (Free Framework for Machine Learning, see Supplementary Materials), which implements the proposed methodology [43].  
UGR'16 dataset, previously introduced in Section 3.1, has been selected to test ML algorithms to classify network attacks. Such algorithms must initially learn from a training dataset, and its accuracy is later on tested on unseen data. For this reason, the dataset is transformed in a derived form of the original one (DD) and split into two different parts: 80% of DD used for training, and the rest (20% of DD) used for testing. Different train and test disjoint sets are selected performing a \(k=5\)-fold cross-validation process which is repeated \(r=20\) times. As a result, a total of \(r\times k=100\) executions of the methodology steps have been carried out, attempting to look for statistical results significance.  
Although the DD includes up to seven different class of malicious traffic, authors decide only to use _Botnet_, _DoS_, _Scan_ and _Spam_. _Blacklist_ was removed due to its appearance in all the observations, showing no differences from regular traffic observations at all. _SSH scan_ and _UDP scan_ were also removed because of the low number of samples available representing these attacks, which will be hard to learn by ML models and may reduce the efficacy of the overall NIDSs. Since the problem addressed is a multi-label classification task, every ML model has been calibrated and tested following a _One-Vs-All_ approach, where a model per class is built in such a way that one specific model will identify its assigned class and will consider the rest of the samples as instances.  
All the experiments were executed on the super-computing centre facilities at the University of Cadiz, which is composed of 48 nodes, each one containing two Intel Xeon E5 2670 2.6 GHz octa-core processors, and equipped with 128 GB RAM.
## 2 Conceptual Model and Problem Procedure
### Wavelet Transform (Stationary State)--SWT  
Derived from the DWT, the SWT is a time-frequency decomposition method where the input signal becomes invariant in time without being downsampled by the filters. As a result, the SWT method is better suited for real-time applications because it offers a faster transient detection than the DWT [34]. Figure 3 illustrates the filtering process without the two-fold downsampling.  
The SWT's scaling and wavelet coefficients are defined similarly to the DWT's:  
\[S_{j}(k)=\frac{1}{\sqrt{2}}\sum_{n=-\infty}^{\infty}h_{\varphi}(n-k)x_{j-1}(n), \tag{1}\]  
Figure 3: Procedure stage of three decomposition levels of DWT.  
Figure 2: Schematic of differential current measurement in a power transformer.  
distortions might happen. Consequently, in the first decomposition level, the RT-SWT computes the scaling \(S_{x}\) and wavelet \(w_{x}\) coefficients in the manner described below [35]:  
\[S_{x}(k)=\frac{1}{\sqrt{2}}\sum_{l=0}^{L-1}\Big{(}h_{\varphi}(l)\overset{\circ}{ x}(k+l-L+1)\Big{)}, \tag{9}\]  
\[w_{x}(k)=\frac{1}{\sqrt{2}}\sum_{l=0}^{L-1}\Big{(}h_{\varphi}(l)\overset{\circ} {x}(k+l-L+1)\Big{)}, \tag{10}\]  
where \(S_{x}\) and \(w_{x}\) can be obtained with just \(L\) samples. As a result, in order to produce results comparable to those of SWT, RT-SWT does not require all of the signal samples. The window shifts because a new sample is added to the window and an older sample is removed at each sampling step (\(1/f_{s}\) seconds). With the exception of the first \(L-1\) coefficients, which cannot be computed in real time and may present coefficients with border distortions of the signal sliding window, the wavelet coefficients of the main window computed in real time with the RT-SWT algorithm are the same wavelet coefficients calculated by the SWT pyramid algorithm [35].
# A Lightweight Model for 3D Point Cloud Object Detection
## 4 Experiment
### Quantitative Evaluation
Model indicates which network architecture is selected; LW-S-3 represents LW-SECOND-3, indicating that no knowledge distillation is used. The symbol \({}^{\prime}\) indicates that only \(L_{\text{out}}\) is used for knowledge distillation; the symbol \({}^{\prime\prime}\) indicates that \(L_{\text{out}}\) and \(L_{\text{feature}}\) are used for knowledge distillation; the symbol \({}^{\prime\prime\prime}\) indicates that \(L_{\text{out}}\), \(L_{\text{feature}}\), and \(L_{\text{FSP}}\) are used for knowledge distillation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **KD** & \(L_{out}\) & \(L_{feature}\) & \(L_{FSP}\) & **mAP** & **NDS** & **mATE** & **mASE** & **mAOE** & **mAOE** & **mAAE** \\ \hline SECOND & - & - & - & - & 52.8 & 63.3 & - & - & - & - & - \\ LW-SECOND-3 & - & - & - & - & 40.5 & 51.4 & 45.2 & 28.8 & 52.6 & 34.9 & 19.6 \\ LW-SECOND-3\({}^{\prime}\) & ✓ & ✓ & ✕ & ✕ & 41.3 & 52.6 & 44.9 & 28.6 & 51.3 & 32.7 & 18.4 \\ LW-SECOND-3\({}^{\prime\prime}\) & ✓ & ✓ & ✓ & ✕ & 43.5 & 56.4 & 39.1 & 26.5 & 43.2 & 30.8 & 20.6 \\ LW-SECOND-3\({}^{\prime\prime\prime}\) & ✓ & ✓ & ✓ & ✓ & 50.8 & 60.8 & 31.9 & 25.4 & 32.1 & 26.4 & 19.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for the nuScenes dataset. Model indicates which network architecture is selected; KD indicates whether knowledge distillation is used; \(L_{\text{out}}\), \(L_{\text{feature}}\), and \(L_{FSP}\) indicate which knowledge transfer is used. **Experiment C.** In order to prove the necessity of model compression, the 3D object detection algorithm proposed in this paper is compared with other 3D object detection algorithms. The comparison results are shown in Table 4. It can be seen that, compared with other algorithms, the parameters and FLOPs of the algorithm proposed in this paper are extremely reduced and that the detection accuracy is maintained at the upper and middle levels. Obviously, the method proposed in this paper is more suitable for deployment on edge devices. Table 4 shows the results with AP calculated by recall 40 positions for car class on the KITTI test set. F and P indicate the number of float operations (/G) and parameters (/M).
# Challenging Assumptions of Normality in AES s-Box Configurations under Side-Channel Analysis
## 6 Conclusions  
Under the lens of examining data sampled using traditional TVLA, power trace data associated with AES s-box configurations most likely do not originate from a normal distribution. Classical SCA strategies rely on an assumption of normality, as has been made evident by the use of parametric statistical tests (i.e., [PERSON]'s \(t\)-test). This assumption, based on this study, may be a severe limitation to power-based side-channel analysis. While non-parametric statistical methods have been widely available, they have remained untapped and underused in SCA to this point. As the field continues to evolve and move towards more complex and varied analysis, equivalent non-parametric approaches should be examined for viability in both leakage exploitation and leakage detection related to power-based side-channel analysis. One natural extension of this work lies in applying parametric and non-parametric statistical methods to a larger set of s-box configurations with varying nonlinearity measures to establish a baseline of performance for each class of tests. Another possible direction would encompass an analysis centered on different microprocessor architectures.  
Conceptualization, C.C. (Clay Carper) and S.O.; Methodology, C.C. (Clay Carper) and J.B.; Software, C.C. (Clay Carper) and J.B.; Validation, C.C. (Clay Carper), S.O. and J.B.; Formal analysis, C.C. (Clay Carper) and J.B.; Investigation, C.C. (Clay Carper) and J.B.; Resources, C.C. (Clay Carper) and S.O.; Data curation, C.C. (Clay Carper) and C.C. (Cay Carper); Writing--original draft, C.C. (Clay Carper) and S.O.; Writing--review & editing, C.C. (Clay Carper), S.O., J.B., C.C. (Cay Carl'in) and M.B.; Visualization, C.C. (Clay Carper) and S.O.; Supervision, C.C. (Clay Carper) and M.B.; Project administration, C.C. (Clay Carper) and M.B.; Funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: The data presented in this study are openly available in FigShare at 10.6084/m9.figshare.24650373.  
The research team would like to acknowledge and thank the Secure Systems Collaborative for their assistance in the revisions and presentation of this information. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any agency, sponsor, or corporate entity.