esa-sceva/satcom-chunk-collection huggingface dataset:
# Programmable Organic Chipless RFID Tags Inkjet Printed on Paper Substrates
## 5 Tag Re-programmability and Industrial Scaling-Up  
As mentioned before, the tags are linear chains of identical resonators, where each resonator provides a bit of information when it is interrogated by the harmonic signal tuned to the resonance frequency. Such tags can be programmed either by cutting the resonant elements associated with the logic state '0' (making them inoperative as shown in Figure 7a, or erased, by short-circuiting the cut resonator through inkjet (thus adding conductive ink in order to set the corresponding bit to '1', as shown in Figure 7b. The main advantage of tag programming/erasing is the possibility of mass-fabrication of all-identical tags, thus reducing fabrication costs. Once fabricated, the tags can be programmed at a later stage, and erased and re-programmed as many times as needed. However, multiple tag programming/erasing cycles may give rise to tag degradation. In typical applications, tags should be programed only once in order to provide a unique ID code after being fabricated (multiple erasing and reprogramming is not expected in most applications).  
A late-stage codification process can be implemented by additive inkjet printing (selective short-circuits) or laser ablation (physical destruction) procedures to be incorporated to the chipless RFID tag design and into the digital workflow standards already settled in the printing industry. For this purpose, Intense Pulse Light (IPL) post-processing of the printed structures on the millisecond time scale enable single line-scan functionalization of the required area, i.e., selective sintering of the light absorbing structures on the full print layout width, in an R2R compatible manufacturing process, thus bringing a whole different response to the interrogation signal.  
Watermarks, serial numbers, holograms, threads, encoding, specific molding, etc., are used to prevent copying and counterfeiting of numerous valuable documents, such as ID cards, banknotes, medical prescriptions, certificates, diplomas, etc., making the list of coding applications for chipless RFID paper enormous.  
For security paper applications, the proposed near-field chipless RFID system provides secure and low-cost encoders with unprecedented high data capacity (comparable to that of chipped tags), maintaining recyclability. Data storage resides on the printed tags, not in any further programming step. The tag design and the packaging graphics are printed simultaneously, only resulting in a slight increase in the cost of the package or tag. From the manufacturer's side, the substrate has no visible change and no further adaption of the production process needs to be implemented. In addition, chipless RFID solutions are printed on the inside, or are hidden below different layers, inaccessible for intentional manipulation.  
Figure 7: (**a**) Identical resonators pre-programmed all to a logic ‘0’ and (**b**) re-connected resonators (programmed to a logic ‘1’).
## 2 Energy Storage System Modeling  
The energy storage systems are modeled in this research in the form of rates of charge and discharge in equal intermissions, normally a period of one day (24 h). Those 24 h are partitioned into \(m\) equal intervals from which the rates of charge and discharge ( \(C_{iT}\)) are calculated at \(m\) value to include 24, 48 and 96, respectively.  
\[C_{iT}=\begin{bmatrix}E_{B}(1)\\ \vdots\\ E_{B}(m)\end{bmatrix} \tag{1}\]  
where the energy in the energy storage system is \(E_{B}(t)\) at time interval ranges from \(1\) to \(m\).  
To calculate \(E_{B}(t)\) at each interval of time, Equation (2) is used based on Fourier Series.  
\[E_{B}(t)=a_{0}+a_{1}\cos(\frac{2\pi t}{T})+b_{1}\sin(\frac{2\pi t}{T})+\ldots+ \ a_{n}\cos(\frac{2\pi t}{T})+b_{n}\sin(\frac{2\pi t}{T}) \tag{2}\]  
where \(a_{0},\ a_{n}\) and \(b_{n}\) are Fourier coefficients, while \(T\) is the total period.  
The charging and discharging rates are calculated in Equations (3)-(5) [4].  
\[\Delta E_{B}=E_{B}(t)-E_{B}(t-1) \tag{3}\]  
\[P_{B}(t)=\frac{\Delta E_{B}}{\Delta t\times\eta_{charging}} \tag{4}\]  
\[P_{B}(t)=\frac{\Delta E_{B}}{\Delta t\times\eta_{discharging}} \tag{5}\]  
where the charging efficiency (\(\eta_{charging}\)) is assumed to be the same as the discharging efficiency (\(\eta_{discharging}\)). Equation (4) represents the charging power \(P_{B}(t)\) (usually positive), while Equation (5) represents the discharging power \(P_{B}(t)\) (usually negative). \(\Delta t\) is the sampling time interval in both equations. In this study, we assumed the energy storage system efficiency to be 85%.  
The optimal size of the energy storage system is shown in Equation (6).  
\[\textit{Storage system size}=\frac{\left|E_{B}^{max}-E_{B}^{min}\right|}{ \textit{DOD}_{max}} \tag{6}\]where \(DOD_{max}\) is assumed to be 80% as the maximum depth of discharge, while \(E_{B}^{max}\) and \(E_{B}^{min}\) are the maximum and minimum values of energy, respectively.
## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing for Controller
#### 3.2.1 Learning and Optimizing for Control Precision
In order for the influence of uncertain environmental disturbances on parameterized objective functions and constraint definitions to be overcome, the components of the controller are learned and adjusted to adapt the environment, and an improved predictive control strategy is established. The combination of the high-level model predictive path following controller (MPFC) and the low-level learning-based feedback linearization controller (LB-FBLC) is used for nonlinear systems under uncertain disturbances [110], as shown in Figure 7. The LB-FBLC uses GP to learn the uncertain environmental disturbances online and accurately track the reference state on the premise of probability stability. The MPFC uses the linearized system model and the virtual linear path dynamics model to optimize the evolution of path reference targets and provides reference states and controls for LB-FBLC. The deep neural networks with a large number of hidden layers significantly improve the learning process of NMPC control law as compared to shallow networks [111]. The integrated design of model learning and model-based control design obtains the advanced prediction model for MPC cost function, the disturbance state space model satisfying robust constraints, and the robust MPC law [112]. Finally, the data-driven controller effectively deals with the constraints and tracks the desired reference output.
## 4 Decentralized Energy-Optimal Control Framework for Solving TOP
### Arrial Time Recursion Algorithm for CAV Platoons  
Since CAV \(sq_{i-1}\) and CAV \(sq_{i}\) inevitably enter the MZ consecutively, \(t_{sq_{i}}^{m}\) is always determined recursively through \(t_{sq_{i-1}}^{m}\) and \(C_{sq_{i},sq_{i-1}}\), [8]. Therefore, obtaining \(t_{sq_{i}}^{m}\) is crucial for deducing the arrival time of subsequent CAVs at the MZ. For the CAV \(sq_{1}\) that enters the MZ first, it is evident that the optimal solution to problem (13) is \(u_{sq_{1}}^{*}(t)=0\) because CAV \(sq_{1}\) does not need to evade any other CAVs. To address the new demand for CAV platoons, we have made corresponding improvements to the algorithm. Since the CAVs in platoon \(P_{1}\) travel on the same lane, once the arrival time \(t_{sq_{1}}^{m}\) of the lead vehicle CAV \(sq_{1}\) in \(P_{1}\) is determined, the entry times \(t_{sq_{i}}^{m}\) of the other CAVs \(sq_{i}\) in \(P_{1}\) can be easily obtained by shifting them on the time scale \(t_{sq_{i}}^{0}-t_{sq_{1}}^{0}\). Similarly, for adjacent platoon \(P_{j}\) and \(P_{j+1}\), only the collision avoidance cost between the lead vehicle of \(P_{j+1}\) and the trailing vehicle of \(P_{j}\) needs to be considered. Ultimately, through the Algorithm 3, we can obtain the controllable arrival times at the MZ for all CAVs.  
```
1.Input:\(PQ=\{\mathbf{p}_{1},\ldots,\mathbf{p}_{m}\}\), the number of CAVs within each platoon vector \(\mathbf{p}_{j}\), denoted as \(np_{j}\), along with the collision avoidance cost \(C_{sq_{i},sq_{i-1}}\) between any two CAV \(sq_{i}\) and \(sq_{i-1}\).
2.For\(i\) in range (1, \(np_{1}\))do
3.If\(i=1\)then \(t_{sq_{1}}^{m}=t_{sq_{1}}^{0}+\frac{\gamma_{0}}{L}\). \(\text{arrival time of the lead vehicle in }\mathbf{p}_{1}\) at the MZ \(t_{pq_{1}}^{m}=t_{sq_{1}}^{m}\). \(\text{arrival time of the lead vehicle in }\mathbf{p}_{1}\) at the CZ \(t_{pq_{1}}^{0}=t_{sq_{1}}^{0}\). \(\text{{Else}}\) \(t_{sq_{i}}^{m}=t_{pq_{1}}^{m}+t_{sq_{i}}^{0}-t_{pq_{1}}^{0}\). \(\text{{Endif}}\) \(\text{{Endfor}}\) \(\text{{3. arrival time of the trailing vehicle in }\mathbf{p}_{1}\) at the MZ \(t_{pq_{1}b}^{m}=t_{sq_{i}}^{m}\). \(\text{{4.For}}\)\(j\) in range (2, \(m\)) do
4.For\(i\) in range(\(\sum_{a=1}^{j-1}np_{a}+1\), \(\sum_{a=1}^{j}np_{a}\))do
5.If\(i=\sum_{i=a}^{j-1}np_{a}+1\)then \(t_{sq_{i}}^{m}=t_{pq_{1}b}^{m}+C_{sq_{i},sq_{i-1}}\). \(\text{arrival time of the lead vehicle in }\mathbf{p}_{j}\) at the MZ \(t_{pq_{j}}^{m}=t_{sq_{i}}^{m}\). \(\text{arrival time of the lead vehicle in }\mathbf{p}_{j}\) at the CZ \(t_{pq_{j}}^{0}=t_{sq_{i}}^{0}\). \(\text{{Else}}\) \(t_{sq_{i}}^{m}=t_{pq_{1}}^{m}+t_{sq_{i}}^{0}-t_{pq_{1}}^{0}\). \(\text{{Endif}}\) \(\text{{Endfor}}\) \(\text{{arrival time of the trailing vehicle in }\mathbf{p}_{j}\) at the MZ \(t_{pq,b}^{m}=t_{sq_{i}}^{m}\). \(\text{{Endfor}}\) \(\text{{5.Output: The arrival time of all CAVs at the MZ. }}\)
```  
**Algorithm 3** Arrial Time Recursion Algorithm for CAV Platoons
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Ideal Number of Clusters
#### 5.3.2 Second Method: Clusters with at Least 25% Attack Data as Attack Clusters  
Table 6 presents the results for runs with various seed values at 300 clusters, for clusters with at least 25% attack data (Second Method).  
Analysis of the Second Method:  
* Impact of Seed Value on Clustering Performance
* When the attack data in the clusters were increased, on average, lower seed values produced higher results.
* On average, higher stability was observed compared to the first method, with less variability in results across different seed values.
* With regard to computational time, as in the First Method, there was no definite pattern.
* Best Performing Seed Values
* Strong performing seed values included 1, 2, 10, 200, and 100, all of which maintained precision and recall above 98.5%.
* Seed 200 provided the highest overall precision (98.75%) and recall (98.73%), making it the optimal seed for this labeling method. The computational time was also on the lower side. These best results are bolded in Table 6.  
* For attack clusters with at least 25% attack data, the seed of 200 was determined to be the most consistent and stable choice.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 2 Materials and Methods
### ExtractEO Architecture
#### 2.3.2 ExtractEO Generic Workflow  
EEO is a software implementing fully automated, end-to-end satellite data processing chains. Its plugin-oriented design enables easy maintenance (module-by-module) and easy evolution (removing and adding modules shouldn't affect others). Every module should implement core functions that are run in a predefined order by the EEO runner. EEO first splits the work into areas (AOI, Sentinel-2 tiles, etc.) which are themselves split into the individual constituent satellite products. This workflow is summarized in Figure 2. The design paves the way for parallelized and distributed computing. The chains are configured through a configuration file, enabling the operator to use predefined chains or create/customize chains, in order to fine-tune module configuration according to the monitored area. So even if the chains are fully automated, the operator can still bring expertise to bear that ensures better results, thanks to fine-tuning.  
EEO is managed through Gitlab, and was released through Docker to ensure up-to-date software that works the same way on every computer.  
Figure 1: Optical band mapping in EOReader (version 0.15.0). Available in full resolution at [16].  
Figure 2: EEO generic workflow.
## 4 Discussion
Thus, further studies are required on age-matched individuals with hearing loss to validate the sensitivity and specificity of the versatile Arabic word-in-noise hearing screening tool used in this study. Additionally, the recruited sample size was relatively small and included only male participants. However, several studies have indicated a significant gender effect on speech intelligibility [44; 45]. Moreover, the stimulation paradigm used in this study used a binaural approach in which words were delivered to both ears simultaneously. This technique might be a disadvantage in hearing screening if one ear has a hearing deficiency. That is, the binaural paradigm might mask a single-ear deficiency. Monaural stimulation for each ear separately could enhance the performance and detection of a unilateral hearing difficulty. Furthermore, although the performance of the uHear mobile application to assess hearing sensitivity has been validated in previous studies [29; 46], performing a clinical pure-tone audiometry test and an ontological examination prior to the screening with the Arabic WIN test would eliminate the possibility of existing of hearing issues.
## 7 Performance Evaluation
### Linear Antenna Array
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**36 Sensors**} & \multicolumn{2}{c}{**64 Sensors**} & \multicolumn{2}{c}{**100 Sensors**} & \multicolumn{2}{c}{**256 Sensors**} \\ \cline{2-9}  & **min** & **max** & **min** & **max** & **min** & **max** & **min** & **max** \\ \hline CPSO & \(-\)38 dB & 20 dB & \(-\)38 dB & 20 dB & \(-\)20 dB & 20 dB & \(-\)53 dB & 18 dB \\ Convex & \(-\)35 dB & 18 dB & \(-\)33 dB & 19 dB & \(-\)39 dB & 22 dB & \(-\)150 dB & 18 dB \\ Conventional & \(-\)20 dB & 20 dB & \(-\)28 dB & 20 dB & \(-\)18 dB & 21 dB & \(-\)22 dB & 18 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Geometry of the linear antenna array consisting of 36, 64, 100, and 256 elements using three different algorithms. Figure 7: Linear. (**a**) Linear Sensors 36, (**b**) Linear Sensors 64, (**c**) Linear Sensors 100, (**d**) Linear Sensors 256.
## 4 Experiment and Results  
### Dataset and Data Collection  
In this study, we collected data using the OPEN API of Korea's Open Public Data Portal (DATA.GO.KR). The real-time data is retrieved through API requests, and the data is converted and stored in CSV file format. Figure 7 shows the process of data collection.  
We removed outliers before entering the data, and due to the nature of the OPEN API, which imposes daily transportation limits, we observed data imbalances over various time periods, including missing data at certain times of day. Unbalanced data can cause the model to make biased predictions for certain times or conditions, so it is essential to  
Figure 7: Data-collection and -conversion process using the Public Data Portal API.  
keep the data balanced. To address this imbalance, the study used both upsampling and downsampling to balance the data. Upsampling increases the data of underrepresented instances, while downsampling reduces the data of overrepresented instances. As shown in Figure 8, the blue graph shows the number of data points per time zone before resampling and the red graph shows the number of data points after resampling. Specifically, the number of data points from 6 p.m. to 11 p.m. was almost three times lower than the peak hours of 9 a.m. to 10 a.m. To address this imbalance, we applied data-resampling techniques and used upsampling techniques to increase the values from 6 p.m. to 11 p.m. because we only needed to adjust the values for RecordedAtTime, trip_time, and DistanceFromStop, while the other values remained constant. For the densely populated hours of 9 a.m. and 10 a.m., we applied downsampling to reduce the data imbalance.  
Because of the wide range of input feature values, scaling was required before the data were fed into the model. While binary features like rush hour conditions are represented as 0 or 1, features like distance or time can vary from hundreds of meters to various scales. Differences in feature ranges can affect the model, so we used StandardScaler to convert all input feature values, which ensured that the data ranges were consistent, making it easier to train the model. Scaling the data in this way allows the model to learn in a balanced way without relying on any particular feature.  
The entire dataset was used 80 percent for training and 20 percent for validation. Out of about 1.5 million data instances, approximately 1.2 million were used for training, and the remaining 300,000 were used for validation. This split helps to evaluate the model's performance and validates its generalization capabilities. To ensure the model's generalization performance and avoid overfitting, we also used k-fold cross-validation to evaluate the model by dividing the data into k folds, using each fold as the test set and the remaining folds as the training set.
## 2 Transmission Mode Analysis and System Modeling
### Orthogonal Experiment Design  
Orthogonal tests transform engineering problems into mathematical problems and simplify the calculation of statistical analysis through the two steps of \"test design\" and \"data processing\", thus saving a lot of test time and economic costs. Orthogonal testing encompasses range analysis and variance analysis methods. The range analysis method is suitable for a situation where the simulation results are relatively accurate and can intuitively judge the influence degree of each factor on the evaluation index. The analysis of variance is applicable to situations with large test errors and can quantify the importance of various factors to the test results [19].  
The switching process from hydraulic mode to hydraulic mechanical compound transmission mode involves the switching of four clutches, namely clutches C1, C2, C4, and C5. The four clutches are considered as factors in the range analysis of the orthogonal test and are set as A, B, C, and D. The output shaft speed reduction amplitude, output shaft dynamic load coefficient, impact strength, and switching time are set as evaluation indicators I, II, III, and IV, respectively. Then, 20 s is taken as the on-time switching point of the simulation, and the two levels are set as 19.5 s for the switching clutch and 20 s for the just-in-time switching clutch. We then considered the pairwise interaction between the four factors. According to the orthogonal table commonly used in orthogonal experiments, the L\({}_{16}\)(\(2^{15}\)) orthogonal table was used to carry out the experiment, and a total of 16 simulations needed to be completed. The orthogonal table generated by the MINITAB 17 software is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline  & **A** & **B** & **A**\(\times\)** & **B** & **C** & **A**\(\times\)** & **C** & **B**\(\times\)** & **C** & **Empty** & **D** & **A**\(\times\)** & **D** & **B**\(\times\)
# A Jug-Shaped CPW-Fed Ultra-Wideband Printed Monopole Antenna for Wireless Communications Networks
It is very hard to achieve a UWB band with compact size; however, in this design, the UWB band is achieved through a CPW technique and the design optimization. The total size of the designed UWB antenna is 25 mm \(\times\) 22 mm \(\times\) 1.6 mm. This printed broadband monopole antenna is manufactured using a low-cost FR-4 Duroid \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Ref.** & **Frequency Range** & **Area** & **Electrical Size** & **Antenna Type** & **Substrate** & **Efficiency** & **Gain** \\
**No.** & **(GHz)** & **(mm\({}^{3}\))** & **(\(\mathbf{\lambda_{0}}^{2}\))** & **Antenna Type** & **Material** & **(\%)** & **(dB)** \\ \hline [11] & 3.4–7, 8–11.4 & 40 \(\times\) 30 & 0.94 \(\times\) 0.705 & Split Ring Resonator Patch & FR-4 & \textless{}95 & \textless{}5 \\ \hline [12] & 3.1–10.6 & 38.31 \(\times\) 34.52 & 0.82 \(\times\) 0.74 & Monopole & FR-4 & \textless{}95 & \textless{}5 \\ \hline [13] & 4.05–5.1, 6–13 & 32 \(\times\) 36 & 0.89 \(\times\) 1.01 & Circular Patch & FR-4 & - & \textless{}4 \\ \hline [14] & 2.5–19.8 & 36 \(\times\) 25 & 0.62 \(\times\) 0.43 & Slotted Patch & FR-4 & - & \textless{}3 \\ \hline [15] & 2.8–18 & 50 \(\times\) 38 & 0.96 \(\times\) 0.73 & Tapered Slotted Patch & FR-4 & - & \textless{}4.32 \\ \hline [16] & 1.9–5, 6–10.6 & 48 \(\times\) 55 & 0.63 \(\times\) 0.72 & Monopole & FR-4 & - & \textless{}5 \\ \hline [17] & 1.2–9.8 & 53 \(\times\) 63.5 & 0.21 \(\times\) 0.25 & Anti-Spiral Shaped Patch & FR-4 & \textless{}85 & \textless{}5.2 \\ \hline [18] & 2.6–10.58 & 38.3 \(\times\) 34.5 & 0.33 \(\times\) 0.3 & Sharp triple notched & FR-4 & - & \textless{}5 \\ \hline [19] & 1.5–10.4 & 64 \(\times\) 37.4 & 0.32 \(\times\) 0.19 & Planar patch & F4 BM & - & \textgreater{}2 \\ \hline [20] & 2–5 & 100 \(\times\) 78 & 0.67 \(\times\) 0.52 & Leaf Shaped Patch & Taconic TLV-5 & - & \textgreater{}3 \\ \hline [21] & 0.83–9.8 & 161 \(\times\) 140 & 0.45 \(\times\) 0.39 & Ex-potential Strip Arms & \(\epsilon_{r}\) = 2.3 & - & \textgreater{}2.5 \\ \hline
**This Work** & **3–11** & **25 \(\times\) 22** & **0.25 \(\times\) 0.22** & **Printed Monopole** & **FR-4** & **\textgreater{}85** & **\textless{}4.1** \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison with the previous research. material. The antenna presents good efficiency with suitable gain. This article is organized as follows: the proposed antenna design is presented in Section 2. Results and discussion are presented in Section 3, and the conclusion is given in Section 4.
# Junction Temperature Optical Sensing Techniques for Power Switching Semiconductors: A Review
## 3 Junction Temperature Optical Sensing Techniques
### Summary  
The three popular OBS techniques, IRC, TSOP, and FBG, discussed so far, have been extensively tested in the literature for SiC MOSFET and Si IGBT, and are reviewed in Tables 2, 4 and 6, respectively. They are noninvasive and offer accurate measurements if professionally installed and calibrated. Furthermore, it is worth noting that the FBG-based techniques require low attenuation fibers with negligible bending radius and insertion losses (i.e., connector losses) and hence should be considered during installation. A comparison of various characteristics of the three OBS techniques is shown in Table 8, suggesting that no single approach excels over others. Hence, the selection of the technique invariably depends on the application and the surrounding constraints.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **IRC** & **TSOP** & **FBG** \\ \hline Operation principle & Infrared radiation & Photon extraction & Wavelength shift \\ Physical contact & No & Yes & Yes \\ Accuracy & High & Moderate & High \\ Signal processing & Moderate & Complex & Simple \\ Noise & High & Moderate & Low \\ Temperature range & \(-\)40 to 250 \({}^{\circ}\)C & 25 to 140 \({}^{\circ}\)C & \(-\)50 to 956 \({}^{\circ}\)C \\ Calibration & One-time & System-dependent & One-time \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between IRC, TSOP, and FBG sensing techniques.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Key Benefits** & **Challenges** \\ \hline \(\bullet\) & FBG has high resolution and & \(\bullet\) \\ accuracy up to 0.1 \({}^{\circ}\)C. & \(\bullet\) \\ \(\bullet\) & FBG sensors have good stability & \(\bullet\) \\ and a large temperature & \(\bullet\) \\ measurement range. & \(\bullet\) \\ \(\bullet\) & FBG can be stuck to the device even & \(\bullet\) \\ when the chip is under its operation & \(\bullet\) \\ condition without disturbance. & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of key benefits and underlying challenges of FBG sensing technique.