esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The SRL-Based Control Strategy for CACC
### The SRL Control Algorithm
#### 3.2.2 The Critic Network  
The inputs of the critic network are the system state \(x(t)\) and the composite action \(u(t)\). The output of the critic, the \(J\) function, approximates the future accumulative reward-to-go value \(R(t)\) at time \(t\). Specifically, \(R(t)\) is defined as  
\[R(t)=\sum_{k=0}^{T}\alpha^{k}r(t+k+1) \tag{10}\]  
in which \(\alpha\) is the discount factor for the infinite-horizon problem (\(0<\alpha<1\)). The discount factor determines the present value of future rewards: a reward received \(k\) time steps in the future is worth \(\alpha^{k-1}\) times what it would be worth if it were received immediately [30]. \(T\) is the final time step. \(r(t)\) is the reward provided from the environment given by  
\[r(t)=-(k_{1}e_{d}^{2}(t)+k_{2}v_{r}^{2}(t)+k_{3}(a_{h}(t)-a_{h}(t-1))^{2}) \tag{11}\]  
in which \(k_{1}\), \(k_{2}\), and \(k_{3}\) are positive weighting factors. For the sake of control accuracy and driving comfort, \(r(t)\) is formulated as the negative weighted sum of the quadratic forms of the distance error, the relative velocity, and the fluctuation of the host vehicle's acceleration. Thus, a higher accumulative reward-to-go value \(R(t)\) indicates the better performance of the action.  
In the critic network, the hyperbolic tangent and linear-type activation function are used in the hidden layer and the output layer, respectively. The output \(J(t)\) has the following form:  
\[u_{c}(t)=\sum_{i=1}^{N_{hc}}\omega_{c_{i}}^{(2)}(t)f_{i}\left(\sum_{j=1}^{n+1 }\omega_{c_{i,j}}^{(1)}(t)x_{j}^{(c)}(t)\right) \tag{12}\]  
in which \(x_{j}^{(c)}\) denote the inputs of the critic network, \(\omega_{c_{i,j}}^{(1)}\) represent the weights connecting the input layer to the hidden layer, \(\omega_{c_{i}}^{(2)}\) represent the weights connecting the hidden layer to the output layer, and \(N_{hc}\) is the number of neurons in the hidden layer of the actor network.
## 6 Conclusions  
This paper proposed a CNN-SNN conversion method with a new threshold balancing technique. The proposed threshold balancing technique attempts to flexibly assign the firing threshold for spiking neurons in a layerwise and channelwise manner for SNN models with convolutional layers. For the CNN-SNN conversion method that uses soft-reset IF neurons and the proposed threshold balancing technique, the experiment results showed that the method could produce converted SNN models with competitive accuracy even with low latency. From the experiments for the VGG-model-based SNN conversion on the CIFAR-10 dataset, it was observed that the conversion method could be applied to deep SNN models with comparable accuracy with relatively low latency. With the ablation study changing the spiking neuron type and the threshold balancing technique, the experiments showed that the soft-reset IF neuron type and the proposed threshold balancing technique combination give the best performance among all the examined combinations. The ANN-SNN conversion approach is a good choice for building deep SNN models. The proposed method is expected to be an excellent choice for building an SNN model from a trained CNN model. Most SNN training works are mainly focused  
Figure 12: Performance of “SNN activation-threshold balancing technique” combinations for the MNIST dataset.  
on classification tasks. Further work remains to find some efficient method for regression SNN models with the ANN-SNN conversion approach. The source code for the developed method is made publicly available at [[https://github.com/nguhcv/cnn_snn_conversion](https://github.com/nguhcv/cnn_snn_conversion)]([https://github.com/nguhcv/cnn_snn_conversion](https://github.com/nguhcv/cnn_snn_conversion)).  
Conceptualization: H.C.V.N.; methodology: H.C.V.N. and K.M.L.; writing--original draft: H.C.V.N.; writing--review and editing: K.M.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2022-2020-0-01462) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), and the support of the \"Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ016247012022)\" Rural Development Administration, Korea.  
Not applicable.  
The authors declare no conflict of interest.
# Study on Degradation of Benzothiazolium-Based Ionic Liquids by UV-H\({}_{2}\)O\({}_{2}\)
## 3 Results
### Effect of Initial pH of IL Solution on Degradation  
The pH of the [C\({}_{4}\)Bth]PF\({}_{6}\) aqueous solution was adjusted to 1, 3, 5.06 (natural pH), 7, 9, and 11. The result is shown in Figure 6. The experimental results show that when the pH is in the lower interval (pH = 1-7), the higher the pH, the faster the IL degradation yield. In contrast, when the pH is in a high range (pH = 7 to 11), the higher the pH, the slower the degradation yield. In fact, changes in pH produce two relative effects. On the one hand, at higher pH, the H\({}_{2}\)O\({}_{2}\) molar absorption coefficient is larger, which is conducive to promoting the reaction and producing \({}^{\bullet}\)OH. On the other hand, it is also possible to remove \({}^{\bullet}\)OH by the following reaction.  
\[\text{HO}_{2}{}^{-}+\text{`OH}\rightarrow\text{HO}_{2}{}^{-}+\text{OH}^{-}, \tag{11}\]  
Figure 5: Effect of chloride ion concentration on degradation (**a**) and carbon chain length of ILs (**b**) on degradation; [IL] = 0.1 mM, t = 45 \({}^{\circ}\)C, [H\({}_{2}\)O\({}_{2}\)] = 100 mM.  
When the pH is at a higher level (pH = 7\(\sim\)11), the removal yield of *****OH is greater than the production yield, and the ILs degradation efficiency is reduced. Therefore, a neutral or weakly acidic solution is more suitable for the UV-H\({}_{2}\)O\({}_{2}\) oxidative degradation reaction. When the concentration of [C\({}_{4}\)Bth] PF\({}_{6}\) is 0.1 mM, its value of pH is 5.06, which is weakly acidic, and the pH is not further adjusted in consideration of the economy. However, according to the existing literature, in the process of photocatalytic degradation of ILs by UV-H\({}_{2}\)O\({}_{2}\), alkaline solution is more favorable for this process than an acidic solution. In the process of pH changing from acid to alkaline, the degradation rate of ionic liquid rises in a straight line. The degradation rate first increased slowly, then decreased, and then increased rapidly. The degradation rate of methylimidazolium ILs under natural pH is much lower than that under alkaline conditions [30], but the degradation rate of benzothiazolium ILs may be different from that of methylimidazolium ILs.
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 3 Proposed Noise-Level Detection
### Noise-Level Estimation  
The standard deviation is used to measure the level of Gaussian noise in video frame, and the existing methods can accurately estimate the standard deviation of a noisy frame, e.g., [PERSON] et al. [16] used the Laplace operator and adaptive edge detection, and [PERSON] et al. [17] estimated the noise level based on the scale invariance of a natural image. The high-dimensionality of video requires that noise level estimation should be fast and effective, and the median absolute deviation (MAD) [18; 19] based on wavelet transform meets that requirement. It is indicated by [19] that the standard deviation of  
Figure 2: Flow chart of noise-level detection.  
Gaussian noise is proportional to the MAD value of image wavelet coefficients. According to this conclusion, the wavelet coefficients of the \(t\)-th frame are first calculated as follows,  
\[\mathbf{y}_{t}=\mathbf{\psi}^{\mathrm{T}}\mathbf{f}_{t}=[\mathbf{\psi}_{1},\mathbf{\psi}_{2},\ldots, \mathbf{\psi}_{L}]\ ^{\mathrm{T}}\mathbf{f}_{t} \tag{10}\]  
where \(\mathbf{f}_{t}\) is the column vector of the \(t\)-th frame \(\mathbf{f}[t]\) through raster scanning, \(\mathbf{\psi}_{l}\) is Daubechis-4 orthogonal wavelet basis and \(L\) is the total number of wavelet bases, \(\mathbf{\psi}\) is the representation matrix whose l-th column is \(\mathbf{\psi}_{l}\), and \(\mathbf{y}_{t}\) is the column vector composed of wavelet coefficients. The MAD value of wavelet coefficients can then be calculated as follows,  
\[m_{t}=\mathrm{median}(|\mathbf{y}_{t}-\mathrm{median}(\mathbf{y}_{t}\ )|) \tag{11}\]  
where \(\mathrm{median}(\cdot)\) denotes the median of input vector. Finally, the standard deviation of the video is generated as follows,  
\[\sigma[t]=\left\{\begin{array}{c}|m_{t}-m_{t+1}\ ||m_{t}-m_{t+1}|>10^{-4}\\ 1|m_{t}-m_{t+1}|\leq 10^{-4}\end{array}\right. \tag{12}\]  
Equation (10) can be implemented by fast algorithm [20], so the noise-level estimation has a high execution speed.  
FR forgery inserts interpolated frames periodically, which makes the standard deviation of the forged video change periodically. As shown in Figure 3, we up-convert the _Foreman_ video with CIF format from 15 _fps_ to 30 _fps_, and use the proposed method to estimate the standard-deviation curves of the original video and its forged version. It can be seen that the original standard-deviation curve changes slowly and smoothly, while the forged one changes periodically. Therefore, the periodicity of the standard-deviation curve can be used as evidence to identify FR forgery.
###### Abstract  
As photoplethysmographic (PPG) signals are comprised of numerous pieces of important physiological information, they have been widely employed to measure many physiological parameters. However, only a high-quality PPG signal can provide a reliable physiological assessment. Unfortunately, PPG signals are easily corrupted by motion artifacts and baseline drift during recording. Although several rule-based algorithms have been developed for evaluating the quality of PPG signals, few artificial intelligence-based algorithms have been presented. Thus, this study aims to classify the quality of PPG signals by using two two-dimensional deep convolution neural networks (DCNN) when the PPG pulse is used to measure cardiac stroke volume (SV) by impedance cardiography. An image derived from a PPG pulse and its differential pulse is used as the input to the two DCNN models. To quantify the quality of individual PPG pulses, the error percentage of the beat-to-beat SV measured by our device and media\({}^{\circledR}\) CS 2000 synchronously is used to determine whether the pulse quality is high, middle, or low. Fourteen subjects were recruited, and a total of 3135 PPG pulses (1342 high quality, 73 middle quality, and 1720 low quality) were obtained. We used a traditional DCNN, VGG-19, and a residual DCNN, ResNet-50, to determine the quality levels of the PPG pulses. Their results were all better than the previous rule-based methods. The accuracies of VGG-19 and ResNet-50 were 0.895 and 0.925, respectively. Thus, the proposed DCNN may be applied for the classification of PPG quality and be helpful for improving the SV measurement in impedance cardiography.  
photoplethysmography (PPG); deep convolution neural network (DCNN); signal quality index (SQI); impedance cardiography (ICG); stroke volume (SV) +
Footnote †: journal: Accepted: 30 June 2020; Published: 3 July 2020
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
## 5 Performance Evaluation
### Performance Metric Modeling
#### 5.1.3 Storage and Memory Footprint  
In this subsection, the increases in the permanent storage and memory footprint generated by the proposed access control model are considered. Specifically, the storage and memory footprints of the HidraR module, which handles up to seven messages, as described in Section 4.1.2, are considered. Such messages imply the exchange and processing of data blocks, such as identifiers, nonces, lifetimes, symmetric keys, tickets, authenticator blocks, MACs, and the most variable block, which is the provisioned policy instance.  
On the one hand, regarding the permanent storage, which is usable after any reboot, the considered additional minimum entities, along with some original resources and sensing applications, are the following: the symmetric key shared with the ACS; the code used to run HidraR; the service ticket, including the records of active sessions; the last one-way key chain value; the access control policy; the session key shared with the subject; the session key, if any; and the logs pending the acknowledgment.  
On the other hand, regarding the memory footprint, in addition to the permanently stored entities, some additional instantaneous data are required for the validations and freshness guaranties during the message exchanges. Some of these values are loaded and erased during the reception, processing and transmission of consecutive messages.  
The measurement of the code, data storage and memory footprint is performed using a particular command, _size_, which provides both static and dynamic occupied memory amounts.  
In this experimental implementation, the _size_ command is executed in two cases, i.e., when HidraR is implemented and when connectivity has just been implemented, and the difference is measured.
# Multilayered Network Model for Mobile Network Infrastructure Disruption
## 6 Simulations and Results
### Simulation Scenarios
#### 6.1.3 Simulation Scenario 3  
The third simulation scenario is focused on the ability of the MNM concept to collect data by MANET nodes from the WSN network. The scenario consists of 2000 WSN sensors, which are static. Mobile MANET nodes were also placed in the simulation area, but the number of those nodes will be changed from 50 to 75 and 100. The movement of nodes was based on Random-Way Point mobility model with the speed of nodes set at 5 km/h, or 1.4 ms respectively, which is the speed of human walk. Radio ranges of MANET nodes were set to 50 m. This simulation scenario aims to observe the time that MANET nodes needs to cover all WSN nodes compared to WSN gateway sensors with its radio range. If the WSN sensor or WSN gateway sensor are within radio ranges of MANET nodes, we assume that contact is negotiated and data transfer can occur. The simulation begins with the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Variable** & **Value** \\ \hline Area size [m] & 100 \(\times\) 100 \\ WSN sensors number & 400 \\ WSN sensors radio range [m] & 10 \\ WSN sensors data rate [Kbps] & 30 \\ MANET nodes number & 20 \\ MANET nodes radio range [m] & 40 \\ MANET nodes data rate [Mbps] & 100 \\ AP number & 1 \\ AP radio range [m] & 40 \\ AP data rate [Mbps] & 100 \\ Number of UAV & 4 (based on clustering algorithm) \\ DRONET radio range [m] & 200 \\ DRONET data rate [Mbps] & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario 2 simulation variables  
Figure 17: Example of simulation scenario 2 with the positions of AP, WSN, MANET and UAV nodes.  
initial position of MANET nodes that starts to move. The simulation ends when all WSN sensors or WSN gateway sensors are covered by MANET nodes at least one time. The simulations were run 1000 times with the same initial positions of WSN sensors, WSN gateway sensors and MANET nodes. This simulation with the position of WSN sensors, WSN gateway sensors and MANET nodes is depicted in Figure 18. The simulation variables are set according to Table 3.
## 5 Evaluation and Translation Results
### Selection of Thresholds \(\theta_{1}\), \(\theta_{2}\) and Weight \(w\)  
Following, we discuss the selection of thresholds \(\theta_{1}\), \(\theta_{2}\) and weight \(w\) described in Section 4.  
To determine the threshold \(\theta_{1}\), we have conducted an experiment to investigate the changes in the number of generated partial sentence pairs with 300k sentences. As shown in Figure 4, we found that the number of the generated partial sentence pairs had a peak when \(\theta_{1}\) was 0.5.  
We also did the experiments for the threshold \(\theta_{2}\) and the weight \(w\) and manually evaluated and observed the error rate of the aligned sentences, with 300k sentences as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**The Error Rate of**}} & \multicolumn{2}{c}{\(\theta_{2}\) = 0.3} & \multicolumn{2}{c}{\(\theta_{2}\) = 0.5} & \multicolumn{2}{c}{\(\theta_{2}\) = 0.7} \\ \cline{2-7}
**Aligned Sentences (\%)** & **Without cc** & **With cc** & **Without cc** & **With cc** & **Without cc** & **With cc** \\ \hline \(w=0.3\) & 8.8 & 6.8 & 7.6 & 5.6 & 9.2 & 9.0 \\ \(w=0.5\) & 2.1 & 1.7 & 1.7 & 0.8 & 3.3 & 2.2 \\ \(w=0.7\) & 7.0 & 3.0 & 6.0 & 3.8 & 7.6 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment results of the error rate of aligned sentences with the threshold \(\theta_{2}\) and the weight \(w\) for 300k training data. “Without cc” indicates without using the common Chinese character information; “With cc” indicates using the common Chinese character information.  
Figure 4: Threshold \(\theta_{1}\) and number of generated partial sentences (300k).  
In order to determine the threshold \(\theta_{2}\) and the weight \(w\), we manually checked the alignment of the generated partial sentence pairs with 9000 (= 500 \(\times\) 18) partial sentence pairs randomly extracted from the 300k sentences. Table 2 shows the error rates. To confirm the effectiveness of the common Chinese character information, we compared the error rates with and without using the information. The error rate of aligned sentences had the lowest value when the weight \(\theta_{2}=w=0.5\) with using the common Chinese character information. The common Chinese character information was helpful for reducing the error rate of aligned sentences.  
Due to the above results, for the experiments described later, we set \(\theta_{1}=0.5\) for the Proposed 1 method and \(\theta_{2}=w=0.5\) for the Proposed 2 method.
## 3 Materials and Methods
### Meta Evaluator, Human Oversight, and Score Integration  
A specialized **Meta Evaluator**, implemented as GPT4o [48] with strict evaluation policies, assigns ground-truth (reference) scores for each subdimension in Tasks 1-3 by referencing the original course materials. It also reviews each agent's justifications and produces a rating for explanation quality, emphasizing coherence, factual integrity, and logical clarity. Human experts resolve domain-specific nuances to ensure that all finalized ground-truth scores accurately reflect the intended learning objectives. Task 1 comparisons rely on an absolute error metric between the agent's subgrades and the meta-evaluator's ground-truth, whereas Tasks 2 and 3 treat the meta-evaluator's subgrades as _true_ for measuring how closely the agent's improved or newly generated question aligns with course standards. The structured dataset ultimately captures all numeric outputs (agent and meta scores) plus explanation-quality ratings, providing a transparent record of agent performance and justifications across a range of question types.  
Senior faculty members and domain experts from each course oversaw all critical stages, from data pre-processing (Markdown conversion, chunking, question selection) to the final meta-evaluation. Their role was to (1) review the correctness and fairness of auto-generated or refined questions, (2) validate the performance of the Fine-Tuned LLM during the iterative FFT process, and (3) supervise the meta-evaluator's final scoring to mitigate biases and address any missing context. The final meta scores (averaged or sub-dimensional) were thus the product of LLM-based evaluation plus domain-expert supervision, ensuring both consistency and reliability.
## 1 Introduction
In our previous research, we proposed a novel PDM-asymmetric-twin-SSB CADD (PDM-ATSSB CADD) scheme to realize the optical field recovery of PDM CV-DSB signals, which does not utilize the spectrum [26]. In this paper, we propose a dual-polarization (DP) field recovery scheme to fulfill polarization-fading-free detection, which is compatible with the detection of DSB signals and increases the SE. At the transmitter, a DSB signal and optical carrier at X-polarization is transmitted. At the receiver, the carrier is extracted using an OBPF. Polarization diversity is achieved by constructing a pair of orthogonal carriers using an FRM. The channel information is estimated based on the LS criterion using training sequences and optimized by ISFA. Moreover, the scheme achieves polarization recovery using MIMO equalization. In the proof-of-concept simulation, a 28 GBaud PDM-OFDM-16-quadrature amplitude modulation (QAM) DSB signal is successfully recovered after 320 km single-mode fiber (SMF) transmission.
# Broadband Millimeter-Wave Front-End Module Design Considerations in FD-SOI CMOS vs. GaN HEMTs
## 3 Simulation and Measurement Results  
### 24 GHz Patch Antenna Array Design  
Using Cadence AWR with AXIEM EM simulations, 2 \(\times\) 2 and 4 \(\times\) 4 patch antenna arrays are designed to work at 24 GHz, as shown in Figure 3. The antennas are designed using a perfect electrical conductor (PEC) and EM modeled over an infinite ground plane to simplify the necessary system-level analysis. The antennas are designed using a substrate with a relative permittivity (\(\xi_{R}\)) of 2.2, and the patch antenna area and feed size/position are carefully designed to match the patch antenna closely to 50 \(\Omega\) at 24 GHz with \(\lambda\)/2 spacing to avoid grating lobes. Using AXIEM, EM structures are generated that take parasitic capacitances and inductances into account at mm-Wave frequencies. The far-field radiation patterns of the two antenna arrays show that the HPBW of the antenna array approximately decreases by a factor of two when the number of elements (\(N\)) quadruples, and the gain thus increases by approximately 5-6 dB.  
Figure 3: Uniform square antenna array simulations in Cadence AWR showing the antenna radiation patterns for (**a**) 2 \(\times\) 2 and (**b**) 4 \(\times\) 4 patch antenna arrays with spacing = \(\lambda\)/2, designed to work at 24 GHz using PECs, a dielectric with \(\xi_{R}\) = 2.2, and an infinite ground plane.  
Figure 2: Various circuit and system simulation tool options and set-ups for antenna array and PA design available to us at TTU.
## 4 Hazards Analysis
### Desertification Hazard Assessment
#### 4.4.2 The Climate Quality Index (CQI)  
The Climate Quality Index (CQI) evaluates the influence of climatic conditions on land degradation and vegetation potential. It is calculated as the geometric mean of three key parameters: annual precipitation, aridity index, and slope orientation. The formula used is:  
CQI = (Precipitation \(\times\) Aridity index \(\times\) Slope aspect)'(1/3)  
Each parameter captures a distinct climatic factor that contributes to land vulnerability. The geometric mean provides a balanced aggregation of the inputs, reducing the impact of any extreme values.  
Based on the resulting CQI score, land is classified into three categories:  
* High quality: CQI \(\leq\) 1.15;
* Medium quality: 1.15 \(<\) CQI \(<\) 1.38;
* Low quality: CQI \(\geq\) 1.38.  
In the case of Lefkas Island (Table 5, Figure 15), most areas receive annual precipitation above 650 mm, which supports vegetation growth and minimizes erosion risk. These zones are assigned a precipitation index value of 1. The aridity index, calculated using the Bagnouls-Gaussen method, reflects moderate drought stress, with values of 1.4 and 1.8, indicating a relatively dry but not extreme climate.  
Figure 14: Spatial distribution of factors used to evaluate the Soil Quality Index (SQI).  
Slope orientation also plays a role in microclimatic variation. North-facing slopes, which are less exposed to direct sunlight and evaporation, dominate the northern part of the island and are assigned a value of 1. In contrast, south-facing slopes, which receive more sunlight and are more susceptible to drying and erosion, are prevalent in the southern regions, receiving a value of 2.  
Taken together, these parameters suggest that Lefkas Island exhibits a mix of moderate and low climate quality, primarily due to its drier southern areas and sun-exposed slope aspects, which increase vulnerability to degradation.