esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 5 Simulation Results and Analysis  
### Simulation Environment and Parameter Configuration  
We validated the anti-jamming performance of the IMSFH system and compared its Bit Error Rate (BER) performance under reactive jamming with conventional FH/BSFK, the MPFH proposed in [25], and WGMPFH proposed in [26]. Additionally, we compared the BER performance of IMSFH and IDFH, proposed in [19], under complex jamming environments. There are two sets of M = 32 orthogonal hopping sequences with available frequency-hopping points. The bandwidth of the user signal at each frequency-hopping point is set to 0.6 MHz. The frequency hopping rate is 5000 times per second, with each hop transmitting 1 bit. In this setting, there are 2560 bits of training data and 15,360 bits of communication data, so the frequency hopping is used during the training stage 2560 times, while the frequency hopping is used during the communication stage 15,360 times. Setting up complex jamming environments includes reactive jamming, multi-tone jamming, and sweep frequency jamming. As mentioned earlier, IDFH shows a sharp decline in communication performance in complex jamming environments. We have also designed relevant simulation experiments to verify that IMSFH maintains good communication performance using reactive jamming in complex jamming environments.
## 4 Discussion
### PVC Impacts Nutrient Cycling  
Measured ecosystem function in sediments containing PVC was most distinct from both the control and other plastic treatments, particularly in the enhancement of benthic nutrient uptake (Figure 1E,F). Greater uptake of ammonium in the presence of microplastic is consistent with findings in intertidal systems [116], but not other marine sediments [66]. PVC separates from other treatments on the second component of the PCA (Figure 2), where GPP and Chl \(a\) are positively loaded and phosphate and ammonium flux are negatively loaded (Figure 2). This suggests a link between benthic microalgal abundance and GPP and sediment nutrient uptake, which is further corroborated by the greater ammonium uptake in the light and release in the dark (Table 2). Combined, these results suggest a PVC-enhanced microalgal production and ammonium uptake. Alternatively, photosynthesis may create oxic zones that facilitate nitrification and also lead to the observed pattern in ammonium uptake. The increased uptake of ammonium and slight release of nitrate to the water column (relative to the control) is consistent with an increase in nitrification activity in freshwater sediment in the presence of PVC [35]. A third potential pathway for ammonium uptake is via anammox (anaerobic ammonium oxidation) in the anoxic layer, which was not assessed here. One route for increased microalgal production is through the provision of substrate for microalgal growth. Other explanations may include the role of additives leaching from PVC to the environment.  
Prior studies also suggest that plastics have direct impacts on nitrogen cycling in sediments and soils [117; 118; 57; 119] where the presence of PVC and accompanying suite of additives may further disrupt nitrogen removal. PVC is typically produced with a number of additives that have been shown to leach into the environment [120; 121], presenting the potential for additional ecotoxicological impacts to microbial communities. For example, titanium dioxide is a common UV stabilizer and coloration used in plastic manufacturing [122], and a concerning emerging contaminant [123]. Titanium dioxide has negative impacts on nitrification and denitrification activity in soils [124], and may have contributed to the slightly suppressed denitrifying activity in the sediment containing PVC here. Our analysis of other additives (see Supplemental Information) did not uncover additional clues to support a clear explanation of drivers, suggesting a need for further evaluation of the impact of this very common material on aquatic ecosystems.
## 2 ALBI Grade Related to Treatment Modalities
### CitATIVE Treatments
#### 2.1.3 RFA  
In addition to RFA cases, the ALBI grade has been reported to be a useful assessment tool for predicting prognosis. [PERSON] found that ALBI grade 2 or 3 was an independent risk factor associated with poor RFS (HR 1.825, \(p=0.001\)) as well as OS (HR 2.191, \(p=0.005\)) in 271 patients with BCLC-0 [31]. In addition, [PERSON] et al. reported that the five-year OS rate of patients with ALBI grade 1 was better than that of those with ALBI grade 2 (88.5% vs. 73.8%, \(p<0.001\)) and showed the ALBI grade to be an independent factor associated with OS (HR 2.32, \(p=0.002\)) [32]. In another study, [PERSON] et al. stated that the ALBI grade may serve as an objective and feasible surrogate for prognostic prediction in HCC patients undergoing RFA, based on the results of comparisons of prognostic performance among 10 liver function models, in which the AIC of ALBI was superior to that of the others [33]. In addition to these studies, the therapeutic effect of RFA on prognosis was unexpectedly small in HCC patients with ALBI grade 3, because the prognosis of patients with ALBI grade 3 (\(n=27\)) was worse that of those with ALBI grade 1 or 2 (\(n=232\)) (median survival 2.7 vs. 4.8 years, \(p=0.0168\)) [34].  
Recently, a randomized controlled trial was designed to prospectively compare the efficacy of resection and RFA as the first approach for primary HCC, but it found no significant difference in OS between them [35]. However, [PERSON] et al. reported that surgical resection offered superior OS and disease-free survival (DFS) results as compared to RFA in an ALBI grade 1 cohort after propensity score matching (\(p=0.0002\) and \(p<0.0001\) respectively), while there were no significant differences for those between surgical resection and RFA in the ALBI grade 2 cohort (\(p=0.7119\) and \(p=0.3266\), respectively) [36]. From the viewpoint of the hepatic reserve function (ALBI grade vs. Child-Pugh classification), it is necessary to conduct further investigations on the strategy for the selection of a curative therapeutic modality.
## 2 Background and Challenges
### Non-GPS Outdoor Localization  
Exactly due to the unstable performance of GPS in urban areas, many have considered supplementary solutions [15; 16; 26; 27]. Essentially, due to the proliferation of mobile and wireless communications, urban areas have been densely covered by both cellular networks [26; 27] and WiFi hotspots [15; 16], and these communication facilities have provided extra signal sources for location estimation. Experience tells us that fusing GPS and non-GPS signal sources properly could yield higher localization accuracy, but this can be achieved only with an adequate profiling of location context.
## 2 Literature Review of Network Intrusion Detection
### Evolution of Intrusion Detection Systems  
Intrusion detection systems (IDS) have been a vital component of network security since the 1980s. These systems were created to monitor and analyze network traffic to detect potential security threats and malicious activities. Over time, IDS have evolved significantly to keep up with the rapidly changing cybersecurity environment [33; 34; 35].  
Initially, intrusion detection systems primarily relied on signature-based and rule-based methods, which can be broadly classified into signature-based IDS and anomaly-based IDS [36]. Signature-based IDS use predefined rules or patterns to identify known attacks by comparing network traffic to a database of known attack signatures. While signature-based systems have been effective against known threats, they struggle to detect novel or sophisticated attacks that do not match established patterns. This limitation led to the development of anomaly-based IDS, which identify potential intrusions by detecting deviations from normal behavior [37]. These systems use statistical and machine learning techniques to establish a baseline of typical network activity and flag deviations from this baseline as potential security threats.  
Despite advancements in anomaly-based IDS, they still face challenges in detecting complex and evolving cyber threats. This has prompted researchers to investigate alternative approaches, such as machine learning and deep learning, to improve the adaptability and accuracy of intrusion detection systems. Machine learning-based IDS analyze network traffic data to learn and recognize patterns of normal and abnormal behavior without depending on predefined rules or signatures. These approaches have demonstrated potential in identifying previously unknown threats and adapting to changes in network behavior over time.
## 3 Study Results
### Delay Results
#### 3.1.2 Simulated Average Delay Results for Highway Speed Limit of 100 km/h  
For the speed limit of 90 km/h, Table 4 shows the simulated average network delay for all vehicles from the 10 runs. For the base scenario (i.e., TT speed limit of 70 km/h), the simulation results show that by increasing the traffic volume, the average network delay increases. The maximum network delay happens at 2000 vehicle/h/lane at TTs of 15%. These results are intuitive and are expected, which means that the simulation results are fine. As shown from Table 4, increasing the TT speed limit to 80, 90, and 100 km/h would result in a reduction in the average network delay for all traffic volumes (500 to 2000 vehicle/h/lane) and for all percentages of TTs, compared with the base condition (i.e., TT speed limit of 70 km/h). For the 90 km/h speed limit, all the reduction cases were found to be statistically significant at the 5% significance level. The relative reduction, from 70 to 80 km/h, was between 23.10% and 41.94%, with respect to the base case (70 km/h). When increasing the TT speed limit from 70 to 90 km/h, the relative delay reduction was between 27.15% and around 60.33%, and from 70 to 100 km/h, the relative delay reduction was between 29.28% and 58.83%. The delay reduction increases with increasing traffic volume and with increasing TT percentage, and the highest reductions occur at 500 vehicle/h/lane with 15% TTs. It is worth noting that increasing the speed limit for TTs from 80 to 90 km/h would result in delay reduction between 5.27% and 31.68%, while increasing the speed limit for TTs from 80 to 90 km/h would result in the lowest delay reductions, which would be between 0.23% and 7.82%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Volume (vphpl)**} & \multirow{2}{*}{**\%TTs**} & \multicolumn{4}{c}{**Tractor-Trailer Speed**} \\ \cline{3-6}  & & **60 km/h** & **70 km/h** & **80 km/h** & **90 km/h** \\ \hline
500 & & 2.61 (0.21)  & 1.62 (0.06)  \\ \hline
1000 & & 6.78 (0.52)  & 4.36 (0.11)  \\ \hline
1500 & & 13.44 (0.80)  & 8.84 (0.22)  \\ \hline
2000 & & 29.56 (2.52)  & 19.10 (0.70)  \\ \hline
500 & & 3.48 (0.26)  & 1.66 (0.08)  \\ \hline
1000 & 5 & 8.43 (0.44)  & 4.60 (0.10)  \\ \hline
1500 & & 16.32 (0.70)  & 9.21 (0.20)  \\ \hline
2000 & & 36.41 (1.59)  & 20.62 (0.42)  & 19.69 (0.52) [FOOTNOTE:]Footnote : The difference between the TT speed in the 70 and 90 km/h scenarios is significant at the 5% significance level. [ENDFOOTNOTE] \\ \hline
500 & & 4.06 (0.25)  & 1.63 (0.08) [FOOTNOTE:]Footnote : The difference between the TT speed in the 70 and 90 km/h scenarios is significant at the 5% significance level. [ENDFOOTNOTE] \\ \hline
1000 & 7.5 & 9.48 (0.31)  & 11.88 (0.20)  & 11.88 (0.29)  & 9.84 (0.91)  & 25.64 (0.03) [FOOTTE:]Footnote : The difference between the TT speed in the 60 and 70 km/h scenarios is significant at the 5% significance level. [ENDFOOTNOTE] & 22.32 (0.74) [FOOTNOTE:]Footnote : The difference between
# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 3 Results
### HADA Proposed Prototype  
M5 Stack is a module that uses the ESP32 microcontroller (dual-core, 240 MHz). It features a two-inch LCD with a resolution of 320 \(\times\) 240 pixels for real-time data visualization, and connectivity is achieved via Wi-Fi and Bluetooth for data transmission. The internal battery is a 1400 mAh Li-ion. Within this project, M5 Stack is configured to collect data from an accelerometer and a pulse sensor, ensuring detailed monitoring of the elderly person.  
Figure 6 presents the prototype in an extended form for demonstration purposes. The display was also used for presentation purposes. Since the display is a significant battery consumer, it is usually disabled.  
* The MPU-6050 Accelerometer and Gyroscope record acceleration and rotation on the three axes, Ox, Oy, and Oz;
* The MAX30100 Pulse Sensor is an optical sensor that measures heart rate and blood oxygen levels. It is useful for measuring the user's physiological parameters.  
The architecture diagram presented in Figure 7 illustrates the data flow of the IoT and cloud-based elderly health monitoring system. The IoT bracelet collects sensor data and transmits it to Azure IoT Hub. Next, the data is processed by Azure Functions and analyzed by the HADA. The results are stored in Azure SQL Database. Azure Notification Hubs and Azure Communication Services notify the caregivers in the event of an alert. This architecture ensures continuous monitoring. Using this architecture, the monitoring system alerts the caregivers.  
Connecting the sensors directly to the M5 Stack's dedicated ports achieves their physical integration. The M5 Stack device was programmed using the Arduino IDE development environment, with C/C++ as the primary language. The source code includes the implementation of the functions described earlier.  
Figure 6: Proposed HADA setup.  
Several tests were conducted to evaluate battery life based on the measurement frequency and display activity. The obtained results are as follows:  
* When the measurement frequency is 1 s and the display is active, the battery life is approximately 1 h.
* When the display was turned off, but the measurement frequency remained at 1 s, the battery life increased to approximately 4 h.
* When the measurement frequency was reduced to 1 h, and display output was eliminated, the battery life increased to approximately 12 h;
* The implementation of the energy-saving mode, using the esp_sleep_enable_timer_wakeup function to allow the ESP32 microcontroller to enter deep sleep mode between measurements, significantly impacted battery life. This increased battery life to 22 h, as the microcontroller consumed much less energy during inactive periods.  
These results suggest that optimizing the measurement frequency and employing energy-saving techniques, such as deep sleep, can significantly extend battery life in a portable system.
## 2 Results  
### Dynamic Changes of SIF and GPP  
#### 2.1.1 Diurnal Variations  
Figure 1 shows the diurnal variations of SIF, GPP and local environmental variables in different seasons during the study period of the subtropical evergreen coniferous forest in Qianyanzhou. It can be seen that in each season, the diurnal variation of GPP showed a single peak trend which reached the maximum value at 13:00 in winter, 12:30 in spring and 10:00 in summer. In addition, the time span of active GPP in a day gradually increased from winter to summer. The diurnal variation of SIF in each season was similar to that of  
GPP. The maximum value was close in spring and summer, and both were significantly higher than that in winter. The maximum value of SIF appeared at 10:00 in winter, 12:30 in spring and 12:30 in summer.  
For the diurnal variation of the environmental factors, Ta also showed a single peak trend, while the time when Ta reached its maximum value apparently lagged behind that of SIF and GDP in each season. PAR also appeared a similar single peak trend with that of GPP and SIF. In terms of SWC, although an apparent variation was presented in each season due to the data standardization, the measured SWC was quite stable in a day and the average SWC in summer was 0.35 m\({}^{3}\) m\({}^{-3}\) which was slightly lower than that in spring (0.38 m\({}^{3}\) m\({}^{-3}\)) and fall (0.39 m\({}^{3}\) m\({}^{-3}\)).  
From the general diurnal variation from January to August, both SIF and GPP showed a trend of increasing first and then decreasing (Figure 1d). The vegetation began to produce SIF from the morning, and the maximum value was reached at about 12:30; then it began to decline, and SIF stopped being produced under low PAR in late afternoon. GPP began to increase at about 5:00 a.m. and reached the maximum value at about 12:30; then it began to decline and dropped to 0 when PAR was quite low. It can be seen that the daily maximum values of SIF and GPP were close to the time of decline.  
characteristics, implying that SWC probably has a small impact on diurnal variation of both SIF and GPP at Qianyanzhou Station.
## 3 The Results of the Numerical Experiment
### Residual Stress Analysis
As seen in Figure 7c, the initial temperature and milling speed have little effect on the \(Y\) direction stress of the milling surface. Figure 7d shows the comparison of the \(Z\) direction stress of the milling surface. It can be seen from the figure that the initial temperature has little effect on the \(Z\) direction stress of the milling surface. When the initial temperature increases, the compressive stress increases accordingly. The milling speed has a remarkable effect on the \(Z\) direction stress. As the milling speed increases, the \(Z\) direction compressive stress of the milling surface gradually increases. It can be seen from the above that appropriately increasing the initial preheating temperature and increasing the milling speed are beneficial in reducing the residual stress and improving the surface quality of the workpiece.
# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
## 3 Proposed Solution Based on Alternating Optimization
 \tag{13}\]  
Meanwhile, the formula of the global optimum position is updated as:  
\[\mathit{gbest}= \begin{cases}s=\arg\underset{1\leq i\leq N_{p}}{\max}\{f[ \mathit{pbest}_{i}]\}\\ \mathit{gbest}(i)=\mathit{pbest}_{s}\end{cases}  \tag{14}\]  
where \(\mathit{gbest}\) is the global optimum position and \(N\) is the size of the particle population [51]. The individual attractor update equation is  
\[attract_{j}=r_{1}\cdot\mathit{pbest}_{j}+(1-r_{1})\cdot\mathit{gbest} \tag{15}\]  
where \(attract_{j}\) denotes the individual attractor of particle \(j\) and \(r_{1}\) is a random factor with uniform distribution \(r_{1}\sim u(0,1)\). In the QPSO algorithm, the attractors integrate the individual optimum position and the global optimum position to represent the directive effect on particle motion and ensure algorithm convergence. The particle position is  
\[X_{j}(i)=attract_{j}\pm\frac{L_{j}}{2}\ln\left(\frac{1}{r_{2}}\right) \tag{16}\]where \(r_{2}\) is a random factor with uniform distribution \(r_{2}\sim u(0,1)\). Within the particle swarm, the global best positions are centered around the mean best position (mbest). The average of the individual best positions for _mbest_ is used to calculate the next iteration for particle \(j\) of feature length \(L_{j}\) and to control the feature length. Thus, it can be defined as follows:  
\[mbest=\frac{1}{N}\sum_{j=1}^{N}pbest_{j} \tag{17}\]  
According to [54], the feature length is calculated as  
\[L_{j}=2\gamma\cdot|mbest-X_{j}(i)| \tag{18}\]  
where \(\gamma\) is the contraction-expansion coefficient as given in Algorithm 1, so the evolution function of particle \(j\) is written as  
\[X_{j}(i+1)=attract_{j}\pm\gamma\cdot|mbest(i)-X_{j}(i)|\cdot\ln\left(\frac{1} {r_{3}}\right) \tag{19}\]  
where \(r_{3}\) is a random factor with uniform distribution, \(r_{3}\sim u(0,1)\) and \(\gamma\) is the contraction-expansion coefficient used to control convergence behavior and provide good balance between exploration and exploitation search mechanisms. By modifying the value of the  
Figure 3: The proposed QPSO-based solution’s flowchart. contraction-expansion coefficient, which affects the search range of the particles, we can control the algorithm's convergence rate. A larger contraction-expansion coefficient can expand the search range, thereby improving the global search capability [55].
# Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System
## 2 Materials and Methods
### Field Site Description  
The dual spectro-radiometer AMSPEC-III system was installed in April 2013 at the Southern Old Black Spruce (SOBS) site; CO\({}_{2}\) measurements were taken with EC as part of the Boreal Ecosystem Research and Monitoring Sites (BERMS) initiative [34]. The site is located \(\sim\)100 km NE of Prince Albert, Saskatchewan (Canada) at 598 m above sea level (latitude 53.9872\({}^{\circ}\) N, longitude 105.1178\({}^{\circ}\) W)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **AMSPEC 1 [7]** & **AMSPEC II [8]** & **AMSPEC-III** \\ \hline Spectro-radiometer & Unispec-DC & JAZ-COMBO \\ Spectrum (nm) & 350–1200 & 200–1100 \\ Resolution (nm) & 3.3 & 0.145 \\ Repeatability (nm) & 0.1 & 0.23 at 730 nm \\ Integration time (s) & 0.004–3.28 & 0.001–65 (20 typical \\ Averaging number of scans & 1000 at 0.4 s (less for longer ITs) & 100 scan/s \\ Operation temperature (\({}^{\circ}\)C) & 0–40 & 0–55 \\ Scan time (s) & 2–6 & 2–6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the third generation Automated Multiangular SPectro-radiometer for Estimation of Canopy reflectance system (AMSPEC-III).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Provider** & **Qty** & **Cost (USD)** \\ \hline \multirow{3}{*}{Unispec-DC} & PP-Systems, 110 Haverhill Rd, Suite & \multirow{3}{*}{1} & \multirow{3}{*}{6860} \\  & 301, Amesbury, MA 01913, USA & & \\ \cline{3-4} \multirow{3}{*}{JAZ-COMBO} & Ocean Optics, 830 Douglas Ave, & & \\  & Dunedin, FL 34698, USA & & \\ \cline{3-4} \multirow{3}{*}{NetCam SC, 5 MP} & \multirow{3}{*}{Stardot Tech., 6820 Orangebrore Ave, & \multirow{3}{*}{1} & \multirow{3}{*}{1340} \\  & Buena Park, CA 90620, USA & & \\ \cline{3-4} \multirow{3}{*}{PTU-D46-17} & Directed Perception, 890 C Cowan Rd, & & \\ \cline{3-4} \multirow{3}{*}{Computer (ARK-1122H-S6A1E)} & Burlingame, CA 94010, USA & & \\ \cline{3-4} \multirow{3}{*}{External hard drive (840 PRO SSD, 128 GB)} & \multirow{3}{*}{Advantech, 380 Fairview Way, & \multirow{3}{*}{1} & \multirow{3}{*}{600} \\  & & & \\ \cline{3-4} \multirow{3}{*}{External hard drive (840 PRO SSD, 128 GB)} & \multirow{3}{*}{Samsung 2-ro, Giveung-gu, Yongin-si, & \multirow{3}{*}{1} & \multirow{3}{*}{150} \\ \cline{3-4} \multirow{3}{*}{Box} & \multirow{3}{*}{–} & & \\  
at the southern edge of the boreal forest, and it is dominated by black spruce (_Picea mariana_ ([PERSON])) up to 11 m high (average height 7.2 m; [35]), with occasional tamarack, 10-16 m high, and jack pine, 13 m high. The canopy is approximately 135 years old and with leaf area index 5.6 [35]. SOBS was selected among other BERMS sites because showing the highest photosynthetic capacity under optimal conditions [36]. The tower module was mounted on a scaffold tower at 25 m height, approximately 18 m above the canopy. The final setup of the tower module at SOBS is shown in Figure 1.