esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To discriminate the aroma-active compounds in dried jujube slices through microwave-dried treatments and understand their sensory attributes, odor activity value (OAV) and detection frequency analysis (DFA) combined with sensory analysis and analyzed through partial least squares regression analysis (PLSR) were used collaboratively. A total of 21 major aromatic active compounds were identified, among which 4-hexanolide, 4-cyclopentene-1,3-dione, 5-methyl-2(5H)-furanone, 4-hydroxy-2,5-dimethyl-3(2H)furanone, 3,5-dihydroxy-2-methyl-4-pyrone were first confirmed as aromatic compounds of jujube. Sensory evaluation revealed that the major characteristic aromas of dried jujuube slices were caramel flavor, roasted sweet flavor, and bitter and burnt flavors. The PLSR results showed that certain compounds were related to specific taste attributes. 2,3-butanedione and acetoin had a significant positive correlation with the roasted sweet attribute. On the other hand, \(\gamma\)-butyrolactone, 4-cyclopentene-1,3-dione, and 4-hydroxy-2,5-dimethyl-3(2H)furanone had a significant positive impact on the caramel attributes. For the bitter attribute, 2-acetylfuran and 5-methyl-2(5H)-furanone were positively correlated. Regarding the burnt flavor, 5-methyl-2-furanorbackaldehyde and 3,5-dihydroxy-2-methyl-4-pyrone were the most influential odor-active compounds. Finally, 2-furanmethanol and 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one were identified as the primary sources of the burnt and bitter flavors. Importantly, this work could provide a theoretical basis for aroma control during dried jujube slices processing.  
a 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
## 4 Design and Implementation Results
### FPGA Hardware Verification of the XOR-Padgen Algorithm  
Simulations of the proposed design were conducted in the Altera Quartus II design environment. The verified Verilog code was then downloaded on an Altera Cyclone II EP2C70F896C6 FPGA running with a 50 MHz clock in the Altera DE2 board to verify the hardware. The output waveforms from the FPGA are displayed using the HP 16702A logic analysis system for real-time verification. Figure 7 shows eight seven-segment LEDs representing the input number for the Padgen function. Subsequently, the decoded message is exhibited on the first line of the LCD, while the most- and least-bit output cover-coded messages are produced and shown on the second line of the LCD. In addition, Figure 7 shows that the Altera DE2-70 board (left-hand side of Figure 7) switches variation from Sw = 2'b00 to Sw = 2'b11. The four states command the data to be sent, thus obtaining pin measurements on the HP 16702A logic analysis system (right-hand side of Figure 7); its outputs in sequence are UserID = 12345678, CCPWD = 59 BF2F62, Out_CCMsgM \(\parallel\) CRC (Out_CCMsgM) = 3174273B (hex), and Out_CCMsgL \(\parallel\) CRC (Out_CCMsgL) = 4B353 ABB (hex) (Figure 7a-d).  
Figure 6: Simulation Results of the XOR-Padgen function.
## 7 Optimized Rectangular-Shaped Piezoelectric Energy Harvester
The mechanical quality factor is suggested to be 100 for the optimized structure so that the mechanical damping ratio is \(\zeta_{m}=0.005\). For optimization of the power output, it is recommended that the piezoelectric energy harvester be applied to a load resistance in the range of \(R_{d}\) and an excitation frequency of \(\omega_{d}\). For values of the mass ratio around or larger than 2.5, the magnitude of \([(0.375+\beta)/(0.2357+\beta)]^{2}\) tends to approximately equal 1. By substituting the mass ratio of \(\beta=2.5\) in Equation (11), the power output at \(R_{d}\) and \(\omega_{d}\) is obtained as expressed by Equation (13a), which can be rearranged to Equation (13b). The power output at these conditions is directly proportional to the square power of the coupling factor and inversely proportional to the capacitance. The optimized PEH is simple and has many possible applications. For example, the optimized PEH can be considered for the designed and fabricated PEH in [27] and the PEHs fabricated in [21]. Figure 12: Optimized structure of a rectangular-shaped cantilevered PEH. \[P|_{R_{d},\omega_{d},\beta=2.5} \simeq\frac{2\alpha^{2}\omega_{d}^{4}u^{2}}{\omega_{d}C_{p}\left(4 \zeta_{m}\omega_{n}+\frac{\alpha^{2}}{m_{d}\omega_{d}\omega_{p}}\right)^{2}}, \tag{13a}\] \[P|_{R_{d},\omega_{d},\beta=2.5} \simeq\frac{\alpha^{2}\omega_{d}^{2}u^{2}}{2C_{p}\omega_{d}\left(1 +2\zeta_{m}\frac{\omega_{n}}{\omega_{d}}-\frac{\omega_{n}^{2}}{\omega_{d}^{2}} \right)^{2}}. \tag{13b}\]  
Because the volume of motion is important to consider for optimization of the power normalized to the total occupied volume, an expression for the transfer function at \(R_{d}\) and \(\omega_{d}\) is derived. The transfer function between a mass ratio of \(\beta=2.5\), an applied load resistance of \(R_{d}\), and an excitation frequency of \(\omega_{d}\) is obtained as follows:  
\[\left|H_{xu}\right|_{R_{d},\omega_{d},\beta=2.5}\simeq\frac{1}{\left(1+2\zeta _{m}\frac{\omega_{n}}{\omega_{d}}-\frac{\omega_{n}^{2}}{\omega_{d}^{2}}\right)}. \tag{14}\]
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
## 4 Design Methodology for Quantization Selection
7:Initialize set \(E\) of experiments
8:for\(l\in L\)do
9: Quantize \(M\)'s input with \(i\) bits
10: Quantize \(M\)'s activation functions with \(a\) bits
11:for\(w\in W\)do
12:for\(p\in L\wedge p\
eq l\)do
13: Quantize layer \(p\) of model \(M\) with \(\max[W]\) bits
14:endfor
15: Quantize layer \(l\) of model \(M\) with \(w\) bits
16: Apply quantize-aware training to \(M\) and use dataset \(DS\)
17:\(Accuracy\{l,w\}\leftarrow\) Calculate accuracy of \(M\)
18:\(NICS\{l,w\}\leftarrow\) Calculate NICS of \(M\)
19:\(Experiment\{l,w\}\leftarrow[NICS\{l,w\},\,Accuracy\{l,w\}]\)
20:\(E=E\cap Experiment\{l,w\}\)
21:endfor
22:endfor
23:Return \(E\)
```  
**Algorithm 2** Stage 2: Layered evaluation for weights. So far, we have analyzed the impact of only one layer in the trade-off. However, we may obtain a better model's configuration by quantizing different layers using different quantization levels. Using the results from the previous stage, we analyze the data dispersion using the mean, the median, and the main quartiles per layer, per variable of interest (i.e., model performance metric and inference cost). At this point, the layer with higher dispersion is the layer that influences the trade-off the most. This allows us to analyze the behavior of each layer, but we still need to determine its level of quantization. To answer this question, we must correlate the information using Spearman. Since the Spearman's correlation ranges from \(1\) to \(-1\), we can obtain an equivalent scale for the quantization level. During the search, we identify the quantization level that, in general terms, offers a better trade-off. This quantization level is regarded as the highest Spearman's correlation coefficient. Then, it is possible to obtain the quantization level and the direction, e.g., a \(1\) as correlation coefficient means that the layer must be quantized at the highest quantization level possible. In contrast, a \(-1\) as correlation coefficient means that the layer must be quantized with the lowest level possible. In the last stage (stage four), we can select the level of quantization that every model layer should have. Thus, having the [PERSON]'s correlation results per layer, we map the correlation coefficient with the quantization level as described above and following Equation (7), where \(M\) is the median, and \(D\) is the variable of interest. If there is more than one variable of interest, Equation (7) should be applied per variable, and the quantization level of each model layer can be selected as a weighted sum of the quantization levels per variable of interest. In this way, the experimenter can choose which variable of interest to care for the most. In Section 5, we derive a detailed procedure to realize stages 3 and 4 with a concrete set of experimental results on a well-known DNN architecture for AMR. \[quantization_{layer}= \begin{cases}4+\frac{(4+M)+4}{2}&\text{ if }\rho\geqslant 1- \frac{6\sum_{i=1}^{m}D_{i}^{2}}{n(i^{2}-1)}\\ -4+\frac{(4+M)+4}{2}&\text{ Otherwise }\end{cases}  \tag{7}\]
## 5 Demonstration of Machine Learning Model
### Shear Demand Curve
Table 6 presents the initial stiffness, maximum strength, strength reduction rate, and energy dissipation capacity for each mode of failure in the experimental and analysis results. The error rate between the analysis and experimental results was calculated, and a good overall agreement was observed. The initial stiffness is determined from the early-stage load application data. The maximum strength represents the peak shear force in the results, and the strength reduction ratio is defined as the proportion of residual strength to the maximum strength. The calculation of energy dissipation capacity presents the area within the hysteresis loop. The maximum error in the strength reduction rate occurred in [PERSON] and [PERSON]'s study [1] for C3-1 at 16%. The strength reduction rate was calculated as the ratio of the residual shear strength (\(V_{res}\)) to the maximum shear strength (\(V_{max}\)). Adjusting the bonding failure variable (\(SF_{slip}\)) to match the initial stiffness led to reduced strength degradation, tuned through strain hardening. 3 SLH18 in the study by [PERSON] et al. [2] and Specim 1 in the study by [PERSON] and [PERSON] [3] showed the maximum errors in the energy dissipation rate at 14% and 22%, respectively. Energy is represented by the area of the hysteresis loop; the error in this case was relatively high due to the difference in the number of loops in the hysteresis loops of the experiments and analysis results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{2}{c}{**Initial Stiffness**} & \multicolumn{2}{c}{**Maximum Strength**} & \multirow{2}{*}{\begin{tabular}{c} **Strength** \\ **(kN/mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Energy** \\ **Strength** \\ **(kN)** \\ \end{tabular} } \\ \cline{2-2} \cline{4-6}  & Experiment & 16 & 235 & 0.70 & 818.2 \\ \cline{2-6} C3-1 & Simulation & 18 & 229 & 0.81 & 751.9 \\ \cline{2-6}  & Error (\%) & 13\% & 3\% & 16\% & 8\% \\ \hline \multirow{3}{*}{3 SLH18 ([PERSON] et al., 1996) [2]} & Experiment & 14 & 270 & 0.26 & 268.7 \\ \cline{2-6}  & Simulation & 15 & 260 & 0.27 & 306.9 \\ \cline{2-6}  & Error (\%) & 7\% & 4\% & 4\% & 14\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Specim 1 \\ ([PERSON] and [PERSON] 2002) [3] \\ \end{tabular} } & Experiment & 19 & 303 & 0.22 & 251.3 \\ \cline{2-6}  & Simulation & 20 & 309 & 0.25 & 307.4 \\ \cline{2-6}  & Error (\%) & 5\% & 2\% & 14\% & 22\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between experiment and simulation.
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 2 Structure of the Portable Smart Spectrometer  
PSS-1.0 consists of seven parts: a light source, a spectroscopic module, a photoelectric detection module, a temperature measurement module, a data transmission module, a digital signature module, and a power supply module. It is a small-volume visible-near infrared spectrometer. It adds digital signatures to spectral data, and transmits the data to smart devices for uploading to the network and performing rapid on-site spectral analysis. Figure 2 shows the structure of PSS-1.0. It uses a 32-bit microcontroller STM32F407 as a central processor to drive a charge-coupled device (CCD), temperature sensor, and Bluetooth. In addition, the chosen microcontroller supports analog-to-digital conversion and encryption [7].  
Figure 1: Portable smart spectrometer PSS-1.0 and related systems.
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
## 4 State-of-the-Art Energy Harvesting Methods for Transmission Lines
### Radio-Frequency (RF) Energy Harvesting
WuR power consumption falls in the \(\upmu\)W domain, but the IoT device often consumes several mW [114], although fractions of mW can be achieved in some cases [28]. Different works have integrated the capabilities of WuR technology into BLE devices [115; 116; 117]. In [118], a passive RF WuR device for a batteryless WSN is proposed.
# Explaining Deep Learning-Based Driver Models
## 4 Experimental Setup and Results
### Datasets
#### 4.1.2 New Datasets  
It is useful to test the models over data that has not been used for training, since it will prove the ability of the model to generalize and be used in a real, unknown environment. By changing the dataset used for testing, we will force the model to work with images where there are people that it has never seen, and where there can be different illumination conditions, backgrounds, angles, and so on. As with the training datasets, we have chosen two datasets to analyze, one for each model.  
To test the emotions model, we decided to use the _Radbound Faces Database (RaFD)_[70]. The Radbound Faces Database consists of photographs taken of adults and children, both male and female, and mostly Caucasian, from five different angles (as _KDEF_) and representing eight different emotions (it adds the \"contemptuous\" emotional expression, which we will not use in this work). We use images wherein subjects are looking forward, taken at three camera angles: straight, half left profile, and half right profile.  
As we mentioned before, to our knowledge, there is only one dataset that records drivers with a camera located in front of them, so there are some limitations to the datasets that we can use. _State Farm Distracted Driver Detection_[71], a dataset from a Kaggle competition, and the _AUC Distracted Driver Dataset_[72; 73] are two existing datasets that show images of people doing activities while driving, quite similarly to _3 MDAD_. These dataset images show one of 10 possible activities, 7 of which our model should be able to predict: safe driving, drinking, reaching behind, sending a message with the right hand, sending a message with the left hand, talking on the phone with the right hand, and talking on the phone with the left hand. However, there is a main difference with the dataset used for training, which is that these photos have been taken from the driver's side, and not from the front.  
Because of this, we tested the activity model with our own images, for which we recreated the angle used in the dataset used for training. We took photographs of seven different subjects performing the distractions considered by the model, for a total of 200 images.
## 6 Conclusions  
In the ISDB-T system, the hierarchical configuration differs from which modulation mode is used in OFDM segment. For reliable frequency synchronization, it is crucial to accomplish IFO estimation without a priori knowledge on the segment format. To address this requirement, we presented an effective IFO detection method independent of the transmitted modulation mode in the ISDB-T system. For this purpose, the redundantly transmitted TMCC signal was used for IFO detection. Because of the information-bearing feature of the TMCC signal, the existing IFO detection strategy cannot be directly used for the ISDB-T system. The proposed approach, using the TMCC signal as the pilot symbol, was found to facilitate a modulation-mode, independent IFO detection. Like the pilot-aided method, the performance of the blind method was greatly influenced by the phase rotation due to the existence of residual FFO, whereas the detection accuracy of the proposed scheme was insensitive to residual FFO. The performance was assessed in terms of theoretical probability of erroneous estimation. The simulation results have shown that such a design relieves computational load and provides good detection performance in the presence of residual FFO and frequency selective channel distortion. It has also been found that the ISDB-T system contains rich structural information, enabling it to effectively detect the IFO.  
[PERSON]-[PERSON] designed the detection scheme and carried out computer simulations. [PERSON]-[PERSON] performed numerical analysis of the detection method and verified simulated results.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2018R1D1A1B07048819).  
The authors declare no conflict of interest.
## 3 Methodology
### Data Preparation
#### 3.3.4 Slope  
The slope feature represents the rate of change in packet throughput over time and captures the dynamic behavior of traffic flow. It is mathematically defined as the first derivative of throughput, reflecting how rapidly the data volume increases or decreases  
Figure 8: Moving average of YouTube traffic.  
Figure 9: Downlink/uplink throughput ratio.  
between successive time intervals. This rate of change is particularly significant for distinguishing between traffic types with different temporal characteristics: In low-latency traffic (e.g., video conferencing), the slope shows more gradual or consistent trends, reflecting larger but steadier data flows. In bulk transfers or streaming (e.g., FTP or YouTube), the slope often exhibits frequent, sharp fluctuations due to small but rapid bidirectional packet exchanges.  
By including the slope, the model becomes more sensitive to temporal transitions in traffic behavior, allowing it to detect subtle bursts or dips in data transmission. When combined with wavelet transform, the slope's frequency-domain representation helps highlight transient events that are characteristic of real-time traffic, thus improving classification accuracy. Slope is represented by \(S_{sent}\) for sent packets and \(S_{received}\) for received packets, defined in Equations (5) and (6):  
\[S_{sent}[i]=\frac{N_{sent}[i]-N_{sent}[i-1]}{t[i]-t[i-1]}, \tag{5}\]  
\[S_{received}[i]=\frac{N_{received}[i]-N_{received}[i-1]}{t[i]-t[i-1]}, \tag{6}\]  
where \(N_{sent}[i]\) represents the number of sent packets at time \(i\), and \(t[i]\) is the corresponding time stamp.  
Positive slope values indicate an increasing trend in byte activity, whereas negative slope values indicate a decrease. This feature is particularly useful for identifying traffic types characterized by distinctive patterns over time, such as video streaming. Video streaming traffic, for example, often shows a consistent and predictable pattern of data transmission due to buffering and playback mechanisms. A representation of it can be seen in Figure 10. In addition, this characterization of trends offers crucial discriminatory information for the classification of various classes of low-latency Internet traffic.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 2 Background
### Botnets  
A botnet is defined as a network of infected computers that perform malicious activities, such as information theft, performing DDoS, sending spam, and so on. Botnet architecture is shown in Figure 1. It consists of a botmaster, bot clients, C&C communication protocols, and a C&C server [5].  
1. _Botmaster_. The botmaster manages and regulates communication with the bot clients. It controls the botnet army.
2. _Bot clients_. Bot clients, also called bots, are compromised computers that get added to the botnet and perform malicious activities in synchronization when commanded by the botmaster.
3. _C&C communication protocols_. These are communication protocols that the botnet uses to send and receive commands from the botmaster to the bots. The communication protocols can be, for example, Internet Relay Chat (IRC), Peer-to-Peer (P2P), Hypertext Transfer Protocol (HTTP), or hybrid, such as a combination of HTTP and P2P protocols.
4. _C&C servers_. The botmaster sends commands through C&C servers, and the bot clients periodically connect to the C&C servers to receive the commands from the botmaster.  
Figure 1: Botnet architecture overview.  
The botnet life cycle is shown in Figure 2. It consists of multiple phases, including initial infection, secondary infection, connecting to C&C servers, rallying, malicious commands, and maintaining/updating the malware [5].
###### Abstract  
There are many advantages to adhesively bonding stiffeners onto aircraft structures rather than using traditional mechanical fastening methods. However there is a lack of confidence of the structural integrity of adhesively bonded joints over time. Acousto-ultrasonic Lamb waves have shown great potential in structural health monitoring applications in both metallic and composite structures. This paper presents an experimental investigation of the use of acousto-ultrasonic Lamb waves for the monitoring of adhesively bonded joints in metallic structures using 3D scanning laser vibrometry. Two stiffened panels were manufactured, one with an intentional disbonded region. Lamb wave interaction with the healthy and disbonded stiffeners was investigated at three excitation frequencies. A windowed root-mean-squared technique was applied to quantify where Lamb wave energy was reflected, attenuated and transmitted across the structure enabling the size and shape of the defect to be visualised which was verified by traditional ultrasonic inspection techniques.  
lamb waves; vibrometry; adhesive bonding; ultrasonics; non-destructive testing; structural health monitoring