esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## 4 Simulation Results
Figure 7 examines the secrecy throughput of the three proposed schemes versus the number of antennas at the jammer. It is clear that the secrecy throughput of the three proposed schemes increases as the number of antennas at the jammer increases. Therefore, we conclude that increasing the number of antennas at the jammer can compensate for the performance loss from short packet transmissions. In particular, the TAS scheme can achieve more significant performance gains by adding more antennas at the jammer. This is attributed to the fact that increasing the number of antennas at the jammer will make it easier to select a better antenna, which reduces the jamming interference at the legitimate user. Figure 5: Secrecy throughput of the ZFB, NAN, TAS, and benchmark schemes versus \(P_{a}\) for \(B=200\), \(N=300\), and \(P_{j}=10\) dBm. Figure 8 depicts the optimal blocklength and the corresponding maximum secrecy throughput versus the transmit power \(P_{a}\) subject to the latency and reliability constraints. When the optimization problem in terms of maximizing the secrecy throughput is infeasible, we set the optimal blocklength to zero and the maximum secrecy throughput is set to zero. We first observe that one critical point exists and when \(P_{a}\) does not exceed this point, the maximum secrecy throughput is zero. This is not surprising, since when \(P_{a}\) is small, it is unable to meet the reliability constraint. We further observe that both the optimal blocklength and the maximum secrecy throughput converge to nonzero constants  
Figure 6: Secrecy throughput of the ZFB, NAN, TAS, and benchmark schemes versus \(P_{j}\) for \(B=200\), \(N=300\), and \(P_{a}=10\) dBm. Figure 7: Secrecy throughput of the ZFB, NAN, and TAS schemes versus \(N_{j}\) for \(B=200\), \(N=300\), \(P_{a}=10\) dBm, and \(P_{j}=10\) dBm. as \(P_{a}\rightarrow\infty\). This is because the secrecy throughput of the considered system is independent of \(P_{a}\) in the high SNR regime. Figure 9 illustrates the optimal blocklength and the corresponding maximum secrecy throughput versus the jamming power \(P_{j}\) subject to the latency and reliability constraints. We first observe that when \(P_{j}\) is either extremely small or large, the maximum secrecy throughput of the TAS scheme approaches zero. This is because the decoding error probability of the TAS scheme is large in the two extreme cases. Therefore, the TAS scheme can only achieve its best performance for in-between values of \(P_{j}\). We further observe that the  
Figure 8: **(a)** The optimal blocklength and (b) the corresponding maximum secrecy throughput versus \(P_{a}\) for \(B=100\), \(N_{j}=8\), \(P_{j}=10\) dBm, \(\epsilon_{\text{max}}=0.35\), and \(N_{\text{max}}=400\). optimal blocklength of the TAS scheme first decreases and then increases as \(P_{j}\) increases. This is because there exists a tradeoff between decoding error and transmission latency.
## 2 Second configuration: The Improved ADRC (IADRC)
### Comparison between the Proposed Decentralized Scheme and the Decoupled Control [23]  
For controlling nonlinear MIMO systems, the nonlinear couplings between different subsystems were considered as the most significant difficulty. Therefore, it was necessary to adopt a control technique that was both simple and robust. A control scheme was suggested in this paper, which makes use of the IADRC because of its robustness and model-independent features. The nonlinear coupling, along with other uncertainties, were considered as a part of the generalized disturbances \(\xi_{1,3}\) and \(\xi_{2,3}\) that needed to be estimated and rejected using the ADRC configuration.  
In the decoupled ADRC scheme [23], the decoupling process was accomplished through the decoupler unit, where the control input for each channel was composed by gathering several control signals from each ADRC controller. For this reason, the energy consumed by each input was larger than the energy consumed in the decentralized ADRC scheme, in which the controlled system was decomposed into several SISO subsystems and an ADRC controller was responsible for controlling each SISO subsystem individually. The chattering in the control signals \(u_{1}\) and \(u_{2}\) in the decoupled ADRC scheme using IADRC lasted for a shorter amount of time than in the case of CADRC, because the proposed NHOESO needed more time for its output to become settled. The decentralized control scheme achieved a significant control energy reduction, as compared to the decoupled control scheme; this is evident from Table 7, shown below.  
In the decentralized ADRC scheme, given that the generalized disturbances \(\xi_{1,3}\) and \(\xi_{2,3}\) will have more terms to be rejected, for instance, unwanted control inputs, exogenous disturbances, undesirable dynamics, uncertainties, etc., the accuracy of the decentralized ADRC scheme, as compared to the decoupling ADRC scheme, was reduced further. This reduction was apparent in the differences between the ITAE values of Table 3; Table 6 and Figure 10c,d and Figure 11c,d, where large chattering was seen in the control signals \(u_{1}\), \(u_{2}\) and the generalized disturbances \(\xi_{1,3}\), \(\xi_{2,3}\) at the very start of the simulation time and vanished quickly due to the reasons mentioned earlier.  
Finally, looking back at Figure 8, Figure 9, and Figure 12 and Equation (13), the diagonalization in the output response in both schemes was due to the off-diagonal terms cancellation (input cross-couplings) of the nonlinear system, using the B-matrix in the decoupled ADRC scheme [23] and considering other inputs from different channels as unwanted dynamics to be cancelled within the generalized disturbance \(\xi_{1,3}\) and \(\xi_{2,3}\) in the decentralized ADRC scheme. The other reason for the diagonal output response was because of the state couplings cancellation between different individual channels within the ADRC framework inside each proposed scheme.
## 4 Reconstruction of Wind Pressure Field on Roof Surface
### Statistical Value Analysis  
Based on Equation (12), different eigenmode orders were selected to decompose the wind pressure field. Four feature points (named C1, E1, F1, and K1) were selected from the four zones C, E, F, and K, respectively, and analyzed by the root mean square (RMS) value. The results are shown in Tables 1 and 2.  
Figure 6: Cumulative energy ratio of eigenmodes in each zone: (**a**) zone C; (**b**) zone E; (**c**) zone F; (**d**) zone K.  
It can be seen from the tables that with the increase of the selected eigenmode order, the wind pressure field reconstruction result is closer to the true value, and when 20 eigenmode orders are selected, a good approximation to the actual wind field can be achieved in terms of statistical value. On the other hand, the reconstruction results based on fluctuating wind pressure converge much faster than those based on mean wind pressure, and a good approximation of the wind field can be achieved in the 10 th order, which is consistent with the energy ratio analysis in the previous section. In addition, combined with the analysis of the cumulative energy ratio of each zone, it is found that the reconstruction effect of the wind pressure field based on the POD method is related to the spatial position of the predicted point, and the overall performance is that the wind field reconstruction effect on the windward folded plate (zone C), the leeward folded plate (zone F), and the roof flat plate (zone K) is obviously better than that on the side folded plate (zone E).
## 3 Materials and Methods
### Determination of Volatile Organic Compounds  
SPME was used to extract the aroma components from durian pulp. The experimental methods and instrument conditions were based on the literature and some optimizations were made [26]. A total of 5 g durian pulp, 3 mL deionized water, 1 g NaCl and 1 mL 10 mg/L thiophene were added to a 15 mL headspace bottle. The fiber coating (57328-U, SUPELCO, Bellefonte, PA, USA) type was carboxen/divinybenzene/polydimethylsiloxane (CAR/DVB/PDMS). The SPME syringe was inserted into the headspace bottle in a 30 \({}^{\circ}\)C water bath and the fiber coating exposed for 30 min. Finally, the SPME syringe was inserted into the GC injector for desorption for 5 min at 250 \({}^{\circ}\)C. GC-MS (7890 GC-5973C MSD, Agilent Technologies, Santa Clara, CA, USA) was used for identification of the durian volatile compounds. HP-Innowax and DB-5 columns (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA) were used for dual-column analysis. The durian volatiles were separated under the following instrumental conditions: electron ionization voltage of 70 eV, helium as carrier gas at a constant flow of 2 mL/min, inlet temperature of 250 \({}^{\circ}\)C, ion source temperature of 230 \({}^{\circ}\)C and quadrupole temperature of 150 \({}^{\circ}\)C. The heating program was set as follows: the initial temperature was 40 \({}^{\circ}\)C which was held for 6 min, the temperature was increased to 100 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min and held for 2 min, and then to 230 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min and held for 20 min. The splitless mode was chosen. The quantification of the volatile compounds was conducted by comparing the peak areas of the detected volatile organic compounds to the peak area of internal standard thiophene. Different durian samples were studied using the same analysis methods and analysis conditions. The compounds were identified by matching retention times of authentic standards, retention indices (RIs), and mass spectra in the NIST 17 database. The RIs of unknown compounds were determined by alkanes (C\({}_{6}-\)C\({}_{30}\)). The parallel experiments were performed three times.
# Monitoring Pediatric Head CT Scan Dose Levels:
###### Abstract  
Diagnostic reference levels (DRLs) are critical operational standards for monitoring radiological imaging parameters and ensuring patient safety in hospitals. A regular review of DRLs is necessary to optimize scanners and protocol parameters. This retrospective study collected data on the CT dose index volume and dose-length product for 250 children who underwent head CT scans in the region of Abu Dhabi, United Arab Emirates. Descriptive statistics were used to present median, range, and interquartile range values for each pediatric age group, based on region and protocol. The 75 th percentile was used as the reference point for local DRLs. Proposed values of DRLs were suggested per age groups; 304 mGy-cm (children \(<\) 1 year), 385 mGy-cm (children \(\geq\) 1 year to \(<\)5 years), 441 mGy-cm (children \(\geq\) 5 years to \(<\)10 years), and 568 mGy-cm (patients \(\geq\) 10 years to \(<\)16 years). A comparison of the local DRLs with previously established ones was carried out, and differences were discussed. To the best of our knowledge, this is the first study on pediatric head CT scans in this region. We believe the results can be used as a baseline for establishing local DRLs in the Emirate of Abu Dhabi and national DRLs in the United Arab Emirates.  
pediatric computed tomography; diagnostic reference levels; radiation protection; Abu Dhabi; UAE 2023334662[[https://doi.org/10.3390/app13084662](https://doi.org/10.3390/app13084662)]([https://doi.org/10.3390/app13084662](https://doi.org/10.3390/app13084662))
# Emerging Technologies for Automation in Environmental Sensing: Review
## 5 Future of Automation in Environmental Remote Sensing  
The adoption of IoT technology and smart sensors in agriculture has revolutionized farming practices, empowering farmers to monitor environmental conditions, crop health, and soil quality in real time. This advancement has resulted in heightened productivity and sustainability [116]. A review on wearable devices for environmental monitoring [117] discusses key pollutants, sensor types, and uses, emphasizing the move toward fully wearable tech to link pollution levels with personal data for estimating individual exposure, despite challenges in standardizing air quality assessments. By 2032, it is expected to reach $508.64 billion, showing a big increase in how much it is used and how innovative it becomes. The market for IoT sensors is expected to grow fast. It is supposed to go from being worth $11.1 billion in 2022 to $29.6 billion by 2026 [118]. This quick growth is because more people are using IoT technology to watch the environment.  
Advancements in technology, particularly the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) with machine learning (ML) algorithms, have revolutionized environmental monitoring and management [119]. UAVs equipped with sophisticated sensors collect high-resolution data over vast areas, enabling comprehensive surveillance of environmental parameters such as air and water quality, biodiversity, and land use. These data are then processed using AI and ML algorithms to perform predictive modeling for environmental changes. By analyzing historical and real-time data, these algorithms can forecast future environmental trends, anticipate potential threats such as natural disasters or habitat degradation, and inform proactive mitigation strategies [120]. This synergy between UAVs, AI, ML, and predictive modeling represents a powerful tool for addressing environmental challenges and promoting the sustainable stewardship of our planet.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
###### Abstract  
The ever-growing number of applications for satellites is being compromised by their poor direct positioning precision. Existing orthoimages, such as enhanced thematic mapper (ETM+) orthoimages, can provide georeferences or improve the geo-referencing accuracy of satellite images, such ZY-1-02C images that have unsatisfactory positioning precision, thus enhancing their processing efficiency and application. In this paper, a feasible image matching approach using multi-source satellite images is proposed on the basis of an experiment carried out with ZY-1-02C Level 1 images and ETM+ orthoimages. The proposed approach overcame differences in rotation angle, scale, and translation between images. The rotation and scale variances were evaluated on the basis of rational polynomial coefficients. The translation vectors were generated after blocking the overall phase correlation. Then, normalized cross-correlation and least-squares matching were applied for matching. Finally, the gross errors of the corresponding points were eliminated by local statistic vectors in a TIN structure. Experimental results showed a matching precision of less than two pixels (root-mean-square error), and comparison results indicated that the proposed method outperforms Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and Affine-Scale Invariant Feature Transform (A-SIFT) in terms of reliability and efficiency.  
image matching; ZY-1-02C; ETM+; positioning precision Article
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods
### Modulation  
Before transmission, data \(d\) is multiplexed onto \(N_{\text{lo}}\) sub-bands of information. The \(N_{\text{lo}}\) sub-bands are then modulated through the Chirp Slope Keying (CSK) block. The slope sign of the reference up and down chirps are up-converted by the Digital Up-Converter (DUC) into the transmission bands, see Figure 2. The DUC is often omitted in acoustic communication due to the relatively low frequency of the transmission bands compared to radio communication, but allows for a more efficient use of storage and reduces the computational effort, which is why we include it.
# Designing Dual-Function Nanostructures for Water Purification in Sunlight
###### Abstract  
The current study aims at combining two building blocks together into well-designed nanostructures to act as dual-function materials; active photocatalysts in sunlight and effective adsorbents for increasing the efficiency of water purification. By these nanostructures, we could avoid the drawbacks of the existing technologies for water purification and remove the industrial pollutants by a dual process; adsorption and photocatalytic degradation. In this trend, Zn-Al layered double hydroxides (LDHs) are combined with graphene oxide to produce a series of nanolayered structures. These nanolayered structures are effective for converting Zn-Al LDHs to be photo-active in sunlight through decreasing its band gap energy from 5.5 eV to 2.5 eV. In addition, these nanolayered structures caused complete decolorization and mineralization of green dyes in sunlight through accelerating the reaction rate of the photocatalytic degradation of dyes seven times higher than that of the pure Zn-Al LDHs. In the same time, they improved the adsorption process of green dyes through creating new micro- and meso-porous structures and high surface area for Zn-Al LDHs. Finally, the well-designed nanostructures between Zn-Al LDHs and graphene oxide led to converting non-photoactive materials to be active in the visible light in addition to a complete and fast removal for organic pollutants.  
adsorption process; degradation of green dyes; nanostructures; solar light; water purification Article
# Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters
###### Abstract  
This paper presents a trajectory control model using finite state machines for a single-stage soft-switching grid-tied inverter designed with a fast dynamic response. The targeted application is a module-integrated inverter for a single photovoltaic (PV) panel which interfaces distributed energy sources with the grid. To minimize switching lossd provide advanced grid-connected functionality, the soft-switching operation is achieved through a resonant filter using a trajectory control scheme. In recent years, controllers based on digital signal processing platforms have been able to handle complex and high-speed control algorithms with precision for real-time control. In real-time control applications, the finite state machine (FSM) approach enhances responsiveness by minimizing latency with limited memory resources by executing rapid state transitions. The proposed model effectively manages the switching states of the single-stage soft-switching inverters during complex DC/AC bidirectional operations. By directly controlling the energy within the series resonant circuit, the model delivers a fast transient response while minimizing switching actions across all quadrants of operation. The control scheme has been digitally implemented on a Texas Instruments (TI) digital signal processor and validated through Hardware-In-the-Loop (HIL) testing.  
g ridd-tied inverter; trajectory control; finite state machine; hardware-in-the-loop; soft-switching +
Footnote †: journal: Applied Sciences  
1
Footnote 1: institutetext: Department of Electronic Engineering, College of Engineering, Keimyung University,  
Daegu 42601, Republic of Korea; [EMAIL_ADDRESS]
# IOb-Cache: A High-Performance Configurable Open-Source Cache
## 3 Results
### Open-Source Caches  
The IOb-Cache was compared with the configurable PoC.cache design included in the PoC-Library [15] library of open-source cores. PoC.cache is the most competitive open-source cache one could find, so the other caches are not evaluated here; clearly, they cannot compete with IOb-Cache or PoC.cache. The comparison between the two caches is available in Table 6. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ways** & **R.Policy** & **Lines** & **Words/Line** & **LUT** & **LUTRAM** & **FF** & **RAMB36** & **RAMB18** \\ \hline \hline \multicolumn{10}{c}{**100 MHz (10 ns)**} \\ \hline \multicolumn{10}{c}{**4 KB**} \\ \hline
1 & & 128 & 8 & 431 & 0 & 249 & 1 & 33 \\ \hline
4 & PLRUm & 16 & 16 & 1727 & 1068 & 2407 & 1 & 0 \\ \hline \multicolumn{10}{c}{**8 KB**} \\ \hline
2 & PLRUt & 128 & 8 & 1025 & 0 & 509 & 1 & 66 \\ \hline \multicolumn{10}{c}{**16 KB**} \\ \hline
4 & PLRUm & 128 & 8 & 1940 & 0 & 1154 & 1 & 132 \\ \hline \multicolumn{10}{c}{**32 KB**} \\ \hline
4 & PLRUm & 256 & 8 & 2961 & 0 & 2187 & 1 & 132 \\ \hline
1 & & 1024 & 8 & 1638 & 0 & 1145 & 1 & 33 \\ \hline \multicolumn{10}{c}{**250 MHz (4 ns)**} \\ \hline
1 & & 128 & 8 & 510 & 40 & 269 & 1 & 32 \\ \hline
4 & PLRUm & 16 & 16 & 1730 & 1068 & 2407 & 1 & 0 \\ \hline \multicolumn{10}{c}{**8 KB**} \\ \hline
2 & PLRUt & 128 & 8 & 1084 & 80 & 549 & 1 & 64 \\ \hline \multicolumn{10}{c}{**16 KB**} \\ \hline
4 & PLRUm & 128 & 8 & 1974 & 160 & 1234 & 1 & 128 \\ \hline \multicolumn{10}{c}{**32 KB**} \\ \hline
1 & & 1024 & 8 & 1714 & 272 & 1162 & 1 & 32 \\ \hline
4 & PLRUm & 256 & 8 & 2981 & 304 & 2289 & 1 & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: IOb-Cache (Native) resource and timing analysis. In addition to the information in Table 6, the following remarks are important. The PoC.cache back-end data-width is fixed to the cache line size, and therefore not configurable to be smaller such as in IOb-Cache. The PoC.cache tag and valid memories are always implemented with distributed LUTRAM and registers, respectively, to combinatorially check for a hit and achieve one read per clock cycle. Lastly, despite using the Write-Though policy, PoC.cache does not have a buffer and accesses the main memory for write transfers, which is comparatively slower. Based on the information in Table 6, there are two main points where PoC.cache is better than IOb-cache: (1) the cache invalidate function and (2) the support for a fully-associative cache. PoC.cache can invalidate individual lines, whereas IOb-Cache can only invalidate the entire cache. PoC.cache can be configured as fully associative (single set) cache and IOb-Cache needs at least two sets. However, besides its theoretical interest, fully associative caches are seldom used in practice. In the remaining features, IOb-Cache is better than PoC.cache: configurable back-end size with AXI4 interface as an option; write-through buffer and independent controller for
## 2 Related Work
### Video-Level Detection  
Existing forged videos are typically generated in a frame-by-frame manner without inter-frame smoothing, resulting in discrepancies between frames. Prominent video-level detection techniques employ recurrent neural networks (RNNs) to model temporal inconsistencies and diagnose forgeries. [PERSON] et al. [14] pioneered an adaptive timing method using CNN frame features and RNN timing modeling. [PERSON] et al. [15] introduced LRNet to extract inter-frame timing cues. [PERSON] et al. [16] aligned faces and learned bidirectional timing networks. [PERSON] et al. [17] encoded physiological face/head signals over time and expanded feature correlations via Pearson coefficients before Support Vector Machine (SVM) classification. [PERSON] et al. [18] designed a 3D CNN (TD-3 DCNN) to sample temporal inconsistencies. While video-level methods can capture rich inter-frame dynamics, they require extensive video preprocessing and are sensitive to compression artifacts and lighting changes. Moreover, single-frame detection remains an open challenge. Advances in robust video-level architectures and generalized frame diagnostics would further push the frontier.