esa-sceva/satcom-chunk-collection huggingface dataset:
# Trends and Challenges in Network Covert Channels Countermeasures
## 4 Covert Channel Detection
### Packet Length and Inter-Protocol Interdependencies  
Modulation of the length of packets is another popular approach used to implement network covert channels and can be applied to different protocols (e.g., IPv6 datagrams [30] or packets containing audio conversations [31]). Additionally in this case, there is not a one-fits-all approach, but the development of some high-level indicators can be the basis to design universal detectors, e.g., by exploiting ubiquitous machine learning classifiers [32] or frameworks able to spot statistical anomalies [33]. In this perspective, the major challenge concerns the ability of engineering an effective dataset for training and tuning the warden.  
Additionally, advanced channels exploiting inter-protocol relationships are emerging [34]. Even if the work addresses real-time services, such principles can be also extended to embedding schemes leveraging packet lengths. To perform detection of such a complex method using multiple/different protocols at the same time, a promising mechanism exploits traffic coloring.
# Arabidopsis Plastid-RNA Polymerase RPOTp Is Involved in Abiotic Stress Tolerance
## 2 Results
### Knock-Down of RPOTp Alters NaCl, Mannitol and ABA Responses
To confirm the role of RPOTp in abiotic stress tolerance, we also calculated, from 0 to 14 DAS, the Col-0/_sca3-2_ and Col-0/_sca3-3_ ratios for both percentages of germination and seedling establishment, under control conditions and in response to 150 mM NaCl, 350 mM mannitol, or 1.5 or 3 \(\upmu\)M ABA (Table S1). In non-supplemented growth media, the Col-0/_sca3-2_ and Col-0/_sca3-3_ values were very similar (usually 1, or very close to 1). In the presence of NaCl, mannitol or ABA, the Col-0/_sca3_ ratios for germination and seedling establishment yielded higher values than they did in non-supplemented growth media, which is in line with the hypersensitivity of _sca3_ mutants to these stress conditions (Table S1; Figures 1 and 2). Interestingly, the Col-0/_sca3-2_ ratios were usually higher than those of Col-0/_sca3-3_, which is consistent with the enhanced sensitivity of _sca3-2_ to stress (Table S1). Taken together, our results showed that the _sca3_ mutants were hypersensitive to not only ionic and osmotic stresses, but also to ABA during germination and seedling establishment, and _sca3-2_ was more sensitive than _sca3-3_. Figure 2: Effects of ABA on germination and seedling establishment in the wild-type Col-0 and mutants _sca3-2_ and _sca3-3_. Each value corresponds to the mean \(\pm\) standard deviation (SD) of the percentage of germination (**a**,**c**,**e**) and seedling establishment (**b**,**d**,**f**) in growth media, either without supplementation (**a**,**b**), or supplemented with 1.5 \(\upmu\)M ABA (**c**,**d**) or 3 \(\upmu\)M ABA (**e**,**f**), for four replicates of at least 50 seeds each per genotype. We also evaluated the response of the _sca3_ plants to salt and ABA later in development, without the detrimental effect that NaCl and ABA exert on germination and seedling establishment. To this end, Col-0 and mutant plants were transferred, at 7 DAS, from non-supplemented agar medium to MS-control media, or media supplemented with NaCl (125 or 150 mM NaCl) or ABA (5 or 10 \(\upmu\)M). Root length was determined at 14 DAS (see Materials and Methods). The roots of the _sca3_ individuals were significantly shorter than those of the Col-0, in the MS-control medium or in the presence of NaCl or ABA (Figure S1). With salt stress, the _sca3_ mutants were significantly less sensitive than Col-0 to the root growth inhibition caused by either 125 mM or 150 mM NaCl (Figure 3a). With the ABA-supplemented media, no significant differences in root length inhibition were found between the _sca3_ and Col-0 plants, except for the _sca3_-3 mutant in response to 10 \(\upmu\)M ABA, which was slightly more sensitive than Col-0 (Figure 3b).
# The Current Development of Structural Health Monitoring for Bridges: A Review
## 5 Damage Identification Methods
### Finite Element Model Updating Methods
#### 5.2.2 Bayesian Methods-Based FEMU  
[PERSON] et al. [138] studied the vibration-based model updating method for the damage detection of a steel truss bridge. Field tests were carried out under five operating conditions, and they used the fast Bayesian FFT method to identify the modal characteristics. In the three bridge models established, the modal characteristics updated by this method are consistent with the actual data. With more prior information, the damage detection capability of this method was verified. [PERSON] [139] introduced a transfer learning (TL) technique into the Bayesian model updating (BMU) method, which can bridge the gap between the numerical model and the real structure. In addition, numerical and experimental studies showed that TL enables BMU to recognize injury severity despite modeling errors. [PERSON] et al. [140] proposed a two-stage method for bridge FEMU. The proposed method combined a radial basis function (RBF) neural network and Bayesian theory. The feasibility of this method was verified by a series of numerical and laboratory experiments.
## 4 Experimental Results
### Discussion
In most of the studies, feature selection is not performed by generating the image as a binary read of the file itself, and it is thought that it is aimed to achieve high classification success since the file itself is processed. In this study, unlike other studies, four features were selected, textual expressions were converted into embedding vectors with BERT, image files were created with these embedding vectors, and results were obtained by classifying them with the CNN model. When compared with the studies using the same dataset used in our study, it was seen that [PERSON] et al. [49] obtained the most successful result classification results, our study was the second, and [PERSON] et al. [71] was the last.
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 2 Numerical Approach
### Bulk Solid-Wall Interaction  
The contact between the walls and bulk material is modelled using a surface-to-surface discretization model option in the Abaqus software [24]. The program provides many methods for defining the contact between two or more surfaces. For defining contact, Abaqus/Standard specifies two kinds of interaction approaches:  
1. Contact pair interaction.
2. General contact interaction.  
A contact pair is used to simulate the interaction of stored granular materials with the silo wall. The friction coefficient between the granular materials and the silo wall is considered to be constant along the silo height, as given in Table 1. This model applies the Coulomb friction model for frictional interaction, which employs a simple coefficient, \(\mu\), to connect shear stress \(\left(\tau_{xy}\right)\) during sliding to normal stress \(\left(\sigma_{x}\right)\), as shown in Equation (2).  
\[\frac{\tau_{xy}}{\sigma_{x}}=-\mu \tag{2}\]  
The constraint enforcement approach used to define the model's tangential behavior is the penalty friction formulation, which imposes a coefficient of friction between surfaces. The sliding formulation used is finite sliding. It allows for arbitrary contact surface relative separation, sliding, and rotation. Based on the relative tangential motion of the contact surfaces, the connectivity of the presently active contact restrictions is updated.
# Voltage-Based Braking Controls for Electric Vehicles Considering Weather Condition and Road Slope
## 3 Braking Controls
### Derive the Braking Distance  
The armature voltage \(v_{a}[k]\) is controlled using PID controls in (13). Then, \(w[k]\) converge to \(w^{o}\).  
We present how to derive the braking distance under the armature voltage \(v_{a}[k]\) in (13). Let \(v[k]\) denote the vehicle speed \(v\) at time step \(k\).  
Considering the case where there is no tire slip before time 0, \(v=R*w\) is satisfied before time step 0. Thus, one assumes that \(v[0]=R*w[0]\). One can measure \(w\) in real time using encoders. Hence, it is assumed that \(v[0]\) can be measured at time step 0.  
Using (4), we obtain  
\[S[k]=1-\frac{R*w[k]}{v[k]}. \tag{15}\]  
At time step \(k\), the vehicle's acceleration is updated using  
\[a[k]=-\frac{\rho*C*v[k]^{2}}{2m}-g*s(\theta[k])-\mu(S[k])*g*c(\theta[k]). \tag{16}\]  
Here, (1)-(3) and (5) are used. Here, \(\theta[k]\) denotes the slope angle measured at time step \(k\). Moreover, \(S[k]=1-\frac{R*w[k]}{v[k]}\) denotes the slip ratio \(S\) at time step \(k\). In (16), \(a[k]\) depends on \(v[k]\), \(\theta[k]\), and \(w[k]\), which vary as \(k\) changes. Recall that \(\mu(S[k])\) depends on the road conditions (e.g., dry, ice, snow, etc.), and the graph of \(\mu(S)\) with respect to \(S\) is addressed in [17; 19].  
Algorithm 1 presents the algorithm for deriving the braking distance \(d\). Algorithm 1 simulates the time step, say \(n\), forward in time, for deriving \(d\). One explains Algorithm 1 in detail. Initially, one assumes that \(v[0]=R*w[0]\). One can measure \(w\) using encoders. Hence, one assumes that \(v[0]\) can be measured at time step 0.  
Using (16), one updates the vehicle's speed from \(v[n]\) to \(v[n+1]\) using  
\[v[n+1]=v[n]+a[n]*T. \tag{17}\]  
At time step 0, one initializes \(d=0\). Considering (6), one updates the braking distance \(d\) by iterating the following equation.  
\[d=d+v[n]*T. \tag{18}\]  
While increasing \(n\) from 0, \(v[n]\) in (17) decreases, since \(\mu(S[n])\) is positive, as plotted in Figure 1. Thus, one has a moment when \(v[n]\) becomes less than \(\epsilon\approx 0\), while increasing \(n\) under simulations. In our MATLAB simulations, we use \(\epsilon=0.1\) in (m/s). Then, Algorithm 1 is complete.  
Considering some PID gains, one may not have a moment \(v[n]\) becomes less than \(\epsilon\approx 0\), while increasing \(n\) under simulations. In order to avoid the case where Algorithm 1 runs for an infinite amount of time, Algorithm 1 is complete as \(d>1000\) (m).  
```
\(d=0\);  Using the wheel encoder, \(w[0]\) is measured; \(v[0]=R*w[0]\); \(n=0\); \(Done=FALSE\); repeat if\(v[n]\leq e\approx 0\)then \(Done=TRUE\); endif if\(d>1000\)then \(Done=TRUE\); endif \(v[n+1]=v[n]+a[n]*T\); \(d=d+v[n]*T\); \(n=n+1\); until\(Done==TRUE\);  The braking distance is \(d\);
```  
**Algorithm 1** Derive the braking distance \(d\)
# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 4 Performance and Evaluation Results  
The prototype was evaluated using three categories of trained objects: plastic bottles, metal cans, and paper. The control system, which integrates the YOLOv8 deep learning algorithm for object detection, a PLC for control, and a stepper motor with a conveyor for actuation, demonstrated consistent and reliable performance. Using the YOLOv8 algorithm, the system achieved precise and rapid detection and classification of objects with minimal processing delays, ensuring uninterrupted and efficient sorting operations. Objects were sorted with high accuracy and minimal false readings, validating the prototype's ability to handle real-time waste sorting. The system also maintained its effectiveness during continuous operation, demonstrating its ability to sustain high-throughput performance.  
Figure 13: Experimental setup of the conveyor sorter, illustrating the conveyor belt, sorting mechanism, and bins designated for the recycling of cans, plastic bottles, and paper.  
The results highlight the functionality of the prototype and its scalability for faster sorting operations and adaptability to additional object categories, such as organic waste. The integration of the YOLOv8 deep learning algorithm with the PLC was pivotal in achieving these results. While the YOLOv8 algorithm ensured accurate and efficient object classification, the PLC seamlessly managed the conveyor, motor, and actuator, enabling smooth and reliable sorting based on classification results. This real-time coordination directed each identified object to its designated category, plastic, metal, or paper, without disrupting conveyor flow. The continuous sorting capability underscores the potential of the system for industrial-scale implementation.  
The system architecture also supports scalability, allowing for the classification of new material categories by retraining the YOLOv8 algorithm and making minor adjustments to the PLC programming. This adaptability is critical to address the evolving requirements of waste management facilities as recycling standards and regulations continue to advance.  
The performance of the YOLOv8 deep learning algorithm was rigorously evaluated using several metrics to assess its classification accuracy and operational robustness. These metrics included the confusion matrix, which provided insight into prediction errors, and precision and recall, which quantified the quality of positive class predictions. Furthermore, the Mean Average Precision (mAP) was calculated to evaluate the overall predictive performance at varying thresholds. Training and validation losses, including box loss, classification loss, and Distribution Focal Loss (DFL), were systematically analyzed to optimize the algorithm's performance. These metrics demonstrated the high precision, scalability, and effectiveness of the YOLOv8 algorithm in real-time waste sorting scenarios. The subsequent sections provide a detailed discussion and analysis of these findings.
## 3 Results
### Relationship between CH\({}_{4}\) Emission and Environmental Factors  
All CH\({}_{4}\) emission fluxes were significantly (\(p\) < 0.05) and positively correlated with air and water temperatures (Figure 6a,b). [PERSON] et al. (2019) proposed that for the computational formula of temperature quotient (\(Q_{10}\) = \(e^{10b}\)) in Figure 6b, the coefficient \(b\) is 0.137, the Q\({}_{10}\) value is 3.94, and the increase rate of CH\({}_{4}\) is 0.06 mol m\({}^{-2}\) h\({}^{-1}\) when the water temperature increases by 10 \({}^{\circ}\)C. When two values of the CH\({}_{4}\) emission flux (57.72 and 59.75 mg m\({}^{-2}\) h\({}^{-1}\)) were removed, the CH\({}_{4}\) emission fluxes were significantly (\(p\) < 0.05) and negatively correlated with the DO concentration in the water (Figure 7).  
Figure 5: Emission fluxes and plant transport for CH\({}_{4}\) in two sites in Hong Lake, including (**a**) the contribution of plant-mediated CH\({}_{4}\) emission to total emissions and (**b**) mean CH\({}_{4}\) emissions.  
Figure 6: The correlation between CH\({}_{4}\) emission fluxes and temperatures of air and water in Hong Lake, including (**a**) the relationship between the CH\({}_{4}\) emission and air temperature and (**b**) the relationship between the CH\({}_{4}\) emission and water temperature.
###### Abstract  
Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. In this paper, to make full use of HSI information, we combined spectral and spatial information into a two-dimension image in a particular order by extracting a data cube and unfolding it. Prior to the step of combining, principle component analysis (PCA) is utilized to decrease the dimensions of HSI so as to reduce computational cost. Moreover, the classification block used during the experiment is a convolutional neural network (CNN). Instead of using traditionally fixed-size kernels in CNN, we leverage a multi-scale kernel in the first convolutional layer so that it can scale to the receptive field. To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. Extensive experiments on the datasets from Pavia University and Salinas demonstrate that the proposed method significantly improves the accuracy in HSI classification.  
hyperspectral image; spectral-spatial fusion; principle component analysis; multi-scale kernel; residual networks +
Footnote †: journal: Applied Sciences
## 3 EV Emulator Design
### DC/DC Converter  
#### 3.2.1 Hardware Design  
Figure 2 shows the schematic of the DC/DC converter. A three-branches in parallel bridge topology to generate the battery output voltage emulation has been designed. Switching frequency of the IGBTs (SEMIKRON SEMIX302 GB12E4s) are 20 kHz, working in the non audible spectrum. A carrier phase-shift scheme is adopted to make the equivalent switching frequency up to 60 kHz, reducing the output voltage ripple, and also the selection of IGBTs with less current capability but better switching efficiency.  
The output filter is an LC filter, with three coils connected in parallel to the output capacitor. It is a second-order low-pass filter with a resonance frequency \(\omega_{res}\) given by Equation (1):  
\[\omega_{res}=\sqrt{\frac{3}{LC_{out}}} \tag{1}\]  
The resonance frequency of the filter needs to be placed at least one tenth of the switching frequency in order to have a sufficient rejection of the switching components. To avoid resonance problems of the filter, \(\omega_{res}\) is also placed lower than the control frequency of the system. It allows the control to compensate the resonance current implementing a virtual resistance (\(R_{virtual}\)), which is placed in series with each inductor. This control method improves the overall efficiency of the system, avoiding physical resistance in the filter to dampen the resonance. However, due to the lower \(\omega_{res}\), the overall dynamic is reduced but is still enough to guarantee the stability and fidelity of the test. In order to  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **EV Emulator** \\ \hline Nominal Power & 50 kW \\ Efficiency & \textgreater{}96\% \\ Internal DC-link voltage & 700 V \\ Switching frequency & 20 kHz \\ Control frequency & 20 kHz \\ \hline \hline  & **Input** \\ \hline Nominal RMS phase voltage & 230 V \\ Nominal RMS phase current & 80 A \\ I ripple & 1\% Inom \\ Nominal frequency & 50 Hz \\ \hline  & **Output** \\ \hline Output voltage range & 50–500 V \\ Output current range & \(-\)100 A to 100 A \\ Maximum voltage ripple & \textless{}2\% Vmax \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main electrical parameters of the designed EVE.  
Figure 2: Topology of the DC/DC converter of the proposed EVE: a three-branches two-level in parallel bridge topology.  
decide the \(R_{virtual}\), Figure 3 shows the bode plot of the LC filter (Equation (2)) with different resistance values.  
\[G_{DCfilter}(s)=\frac{1}{\frac{LC_{out}}{3}s^{2}+\frac{R_{virtual}C_{out}}{3}s+1} \tag{2}\]  
Ideally, it can be seen in Figure 3 that with a higher R the system is damper. However, a high \(R_{virtual}\) will also increase the measurement noise of the current, decreasing the steady state performance. A trade-off between dampening and performance has been chosen, selecting a \(R_{virtual}=1\)\(\Omega\). The frequency response of the filter with the selected resistance is shown in Figure 4, obtaining resonance gain close to 10 dB.
# A Multitask Learning Framework for Abuse Detection and Emotion Classification
## 4 Experiment
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models/Datasets** & **HatEval** & **Davids** & **OffEval** & **FNUC** & **StormW** & **Average** \\ \hline Word-level BiSLTM & 60.59 & 92.42 & 76.77 & 66.91 & 89.18 & 77.174 \\ Word-level BiSLTM Attention & 61.90 & 92.99 & 79.73 & 66.19 & 88.94 & 77.95 \\ Character-level BiLSTM & 52.07 & 93.82 & 75.59 & 66.02 & 88.39 & 75.178 \\ Hyper-RNN [49] & 57.85 & 95.55 & 80.04 & 70.78 & 90.13 & 78.87 \\ \hline Word-level CNN & 60.31 & 91.54 & 77.27 & 71.31 & 89.09 & 77.904 \\ Character-level CNN & 53.40 & 93.56 & 73.73 & 66.58 & 85.88 & 74.63 \\ Hyper CNN [48] & 56.36 & 93.72 & 76.44 & 69.88 & 88.95 & 77.07 \\ \hline Character-level Transformer [50] & 63.28 & 92.33 & 79.09 & 70.40 & 89.68 & 78.956 \\ \hline MTL [1] & & & & & & \\ MTL\({}_{Hard}\) & 62.59 & 92.93 & 80.12 & 71.13 & 89.15 & 79.184 \\ MTL\({}_{DEnocder}\) & 62.62 & 92.57 & 76.74 & 67.62 & 89.71 & 77.852 \\ MTL\({}_{GatchDLEncoder}\) & 60.17 & 92.65 & 79.79 & 70.49 & 89.18 & 78.456 \\ \hline BERT [9] & 62.08 & 95.88 & 83.59 & 69.36 & 90.71 & 80.324 \\ MFAE & **64.65** & **96.49** & **84.43** & **73.08** & **91.60** & **82.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weighted F1 score (%) for different models on five datasets. The optimal results are indicated in bold. The table lists the character-level methods, word-level methods, MTL, BERT, and our models, respectively, and the results show that our model (MFAE) achieves the best performance.
# Seismic Observations in Bucharest Area with a Raspberry Shake Citizen Science Network
## 2 Bucharest Raspberry Shake Seismic Network
As Bucharest is one of the most affected cities by earthquakes in Europe and has suffered much damage due to strong Vrancea intermediate-depth earthquakes, such a network, the Bucharest Raspberry Shake Seismic Network (BRSSN), was developed starting in June 2020 when 15 4D RSs were installed in five buildings [33] in Bucharest city to study their structural behavior during earthquakes. In the present study, we use the data from the seismometers listed in Table 1, which are installed in the building's basement and/or ground floor. In the case of one building with two RSs (R7A63 and R8 BAF, see Figure 3) installed in the basement and on the ground level, both seismometers were utilized. The BRSSN was upgraded in November 2020 with a 1D RS installed in the NIEP Seismolaboratory and a 4D RS installed in September 2022 at the Bucharest French School. These two seismometers are part of the Romanian Educational Seismic Network (ROEDUSEIS [22]), which comprises 24 Raspberry Shake seismometers integrated into the global citizen science Raspberry Shake network. Even though all these seismometers (Figure 3) are under the care of the National Institute for Earth Physics in Romania, they are integrated into the global citizen science Raspberry Shake network [4]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS Code** & **Latitude** & **Longitude** & **Type** \\ \hline R3 BC5 & 44.3423423 & 26.0276728 & 1D \\ R1784 & 44.5135135 & 26.0672459 & 4D \\ R13 FF & 44.4234234 & 26.0237325 & 4D \\ R7A63 & 44.4324324 & 26.1066105 & 4D \\ R8 BAF & 44.4324324 & 26.1066105 & 4D \\ RD1 CA & 44.4234234 & 26.1250955 & 4D \\ RB536 & 44.4324324 & 26.156008 & 4D \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information about the Raspberry Shake seismometers installed in Bucharest and used in this study. Figure 2: Data-flow transmission scheme used for RS sensors.
## 1 Introduction
Six fuzzy MPPT algorithms, based on different input-variables, are considered in the study including: (i) P-V slope (\(S=\Delta P_{PV}\) / \(\Delta V_{PV}\) ) and changes of the slope (\(\Delta S\) ); (ii) P-V slope and variationof the power (\(\Delta P_{PV}\)); (iii) variation of power and variation of voltage (\(\Delta V_{PV}\)); (iv) variation of power and variation of current ( \(\Delta I_{PV}\) ); (v) sum of conductance and increment of the conductance (\(I_{PV}\) / \(V_{PV}\) + \(dl_{PV}\) / \(dV_{PV}\)) ; and (vi) sum of angle of conductance and angle of increment of the conductance \(\left(\tan^{-1}(I_{PV}\) / \(V_{PV}\) ) + \(\tan^{-1}(dl_{PV}\) / \(dV_{PV}\)) \(\right)\). Algorithms (i)-(iv) used two input variables while algorithms (v) and (vi) used a single input variable. A MATLAB-based computer simulation program was developed to verify the fuzzy MPPT algorithms, with the algorithm verification results presented in this paper as well. A summary of the advantages and disadvantages of the six algorithms were also provided. Amongst these algorithms, algorithm (vi) had a well-defined input variable with finite range, as well as a well-defined MPP condition in stable conditions. Thus, algorithm (vi) could be formulated as a means of feedback control and applied in multipurpose controller designs.