esa-sceva/satcom-chunk-collection huggingface dataset:
# Spoofing Detection Algorithm Based on Pseudorange Differences
## 3 Simulation Tests
### Dynamic Whole-Time Duration Spoofing Scenario  
Further, we select the spoofing part of the dynamic position spoofing scenario in the TEXBAT datasets to construct the dynamic whole-time duration spoofing scenario to verify the performance of the algorithm. The simulation results are shown in Figure 11.  
The traditional least squares method's result at the first epoch is used as the initial value in the calculation. From Figure 11, we can see that the position deviations are larger than the threshold, and this indicates the existence of the spoofing signal. In this scenario, the missed detection rate when the signal is spoofing is 0.82%. This proves that the proposed spoofing detection algorithm is effective for the dynamic whole-time duration position spoofing scenario.  
For the spoofing scenarios, the positions obtained by the traditional least squares method and the spoofing detection algorithm are different, and the position deviations will always be larger than the threshold value as long as the spoofing signal exists. The authenticity of the signal can then be determined. The spoofing detection algorithm's results do not represent the receiver's real position, thus the position deviations do not represent the position bias caused by the spoofing signal. Therefore, we cannot see the effect of the 600-m offset. If it is only solved by the traditional least squares method, we can observe that the receiver is slowly driven by the spoofing signal and reaches its final offset of 600 m.  
Figure 10: Position deviations of the two algorithms (dynamic position spoofing scenario).  
Figure 9: Position deviations of the two algorithms (static position spoofing scenario).
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 8 Deployability
### Deployability Results on the MCU  
To deploy, program, run, and validate the model's performance on the MCUs, several AI tools were used by this work:  
* STM32 CubeIDE ([[https://www.st.com/en/development-tools/stm32](https://www.st.com/en/development-tools/stm32) cubeide.html]([https://www.st.com/en/development-tools/stm32](https://www.st.com/en/development-tools/stm32) cubeide.html) (accessed on 13 November 2024)), an integrated development environment (IDE) that provides comprehensive facilities for MCU programming and debugging;
* STM32 CubeMX ([[https://www.st.com/en/development-tools/stm32](https://www.st.com/en/development-tools/stm32) cubemx.html]([https://www.st.com/en/development-tools/stm32](https://www.st.com/en/development-tools/stm32) cubemx.html) (accessed on 13 November 2024)), a graphical tool that helps in configuring MCU peripherals and generating initialization code;
* STM32 CubeProgrammer ([[https://www.st.com/en/development-tools/stm32](https://www.st.com/en/development-tools/stm32) cubeprog.html]([https://www.st.com/en/development-tools/stm32](https://www.st.com/en/development-tools/stm32) cubeprog.html) (accessed on 13 November 2024)), a tool used for programming the MCU with the binary generated by the IDE;
* ST Edge AI Core Technology ([[https://www.st.com/en/development-tools/stedgeai-core.html](https://www.st.com/en/development-tools/stedgeai-core.html)]([https://www.st.com/en/development-tools/stedgeai-core.html](https://www.st.com/en/development-tools/stedgeai-core.html)) (accessed on 13 November 2024)), a command-line interface (CLI) tool to optimize and compile edge AI models for multiple ST devices, including MCU, MPU, and MEMS sensors.  
Table 12 shows the results achieved in terms of energy consumption, memory footprint, and inference time to run the models (defined in Sections 5.1 and 5.2) deployed in the MCU. FP32 means that the model's weights were represented in 32-bit floating-point precision.  
The watermarked models obtained according to methods explained in Sections 6.1 and 6.2 required no additional energy, execution time, and memory resources. This was expected since the methods impact how the models are trained and, therefore, the weight's values and not the model's topology and footprint. This finding is significant from an embedded feasibility perspective since it underlines the efficiency and practicality of deploying WMd models in real-world applications without incurring extra costs. This efficiency bolsters the case for utilizing WMd models in various embedded systems, further enhancing the utility of MCUs in practical scenarios.
# Mitigation of Nonlinear Distortions for a 100 Gb/s Radio-Over-Fiber-Based WDM Network
## 5 Conclusions  
In this paper, a WDM-RoF model is considered as a solution to support high data rates, and the main challenge of channel distortions and instability of RoF network is addressed. The prominent performance limiting factors for the RoF transmission like NLIs, LDs and high PAPR of OFDM signals are studied and treated in the presented model. A Mathematical model is presented and investigated to explore the methodology and effectiveness of the proposed system in mitigation of the NLIs and LDs. The presented model is analyzed with the help of Optisystem simulation software for 10 km of transmission range, input power levels from \(-20\) to 0 dBm, received power with \(-40\) to \(-18\) dBm values and normalized signal to noise ratio. An aggregate data rate of 100 Gb/s is achieved by using 16 WDM channels by transporting each channel at RF of 5 GHz and bandwidth of 1.25 GHz. The work involves analysis with different WDM spacings to explore the capabilities of the proposed solution in mitigation of crosstalk between WDM channels due to the inter-channel mixing. The BER analysis for non-OFDM and OFDM signals with 16-QAM and 64-QAM modulation formats is shown using a maximum number of 16 channels, to show the performance improvement by suppression of NLIs and LDs using the proposed system for high capacity fronthaul networks.  
The future directions include use of RoF transport to support anticipated key technologies for the future mobile systems such as massive MIMO and millimeter-wave transmission. Further optimization of the proposed DSP receiver is also desirable to reduce the overall complexity of the user equipment.  
Conceptualization, S.R., F.A., U.H., F.M. and M.I.; methodology, F.A. and U.H.; software, F.A., U.H. and F.M.; validation, F.A. and U.H.; formal analysis, F.A., S.N.F.M. and U.H.; investigation, F.A., U.H. and F.M.; data analysis, F.A. A.K., and A.S. ([PERSON]); writing--original draft preparation, S.R. and F.A.; writing--review and editing, F.M., M.I., A.G., A.S. ([PERSON]), S.A. and S.N.F.M.; visualization, U.H.; supervision, U.H., M.I. and A.S. ([PERSON]); project administration, F.M., M.I., A.G., A.S. ([PERSON]) and S.A.; obtaining financing, A.S. ([PERSON]), M.I. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Silesian University of Technology, grants No. BK-200/RM0/2020 and BK-261/RM4/2020; and by the program of Polish Ministry of Science and Higher Education \"Doktorat wdrozeniowy\" for the article processing charges payment of the journal.  
The authors acknowledge the support from the Ministry of Education and the Deanship of Scientific Research, Najran University. Kingdom of Saudi Arabia, for funding the project under code number NU/ESCI/17/015.  
The authors declare no conflict of interest.
## 3 Results
### Seasonal Dynamics of Climate, Vegetation Indices, and Carbon Fluxes (NEE, GPP)
#### 3.1.2 Seasonal Dynamics of Vegetation Indices  
At the Brazil site (Figure 3c), the EVI and LSWI rose rapidly in December, reaching the maximum value in May 2015. The EVI and LSWI gradually decreased, and by October the EVI reached below 0.1 and the LSWI below 0. The field was harvested in October 2016. The VI-based start of the sugarcane growing season (SOS) at this site was November 2015 for the first cycle and early December 2016 for the second cycle. The end of the growing season (EOS) was in late October 2016, according to the thresholds used in our previous study [35], which were EVI < 0.1 and LSWI < 0. The results indicated a \(\sim\)12 month sugarcane growing season length (GSL).  
At the USA site (Figure 3d), the EVI and LSWI started to rise in late April and early May and reached their highest values between July and August. The EVI and LSWI gradually dropped, reaching LSWI < 0 and EVI < 0.1 by early November. The VI-based  
Figure 3: Seasonal variation in estimated GPP\({}_{\rm EC}\) and measured NEE\({}_{\rm EC}\) at 8-day intervals over the study period. Seasonal variation in vegetation indices (land surface water index (LSWI) and enhanced vegetation index (EVI)) derived from 8-day MODIS data. (**a,c**) FAYS Brazil site, 2015–2017. (**b,d**) Chachoula, USA site (2018–2020).  
Figure 2: Seasonal variation in daily temperature, daily mean daytime temperature, 8-day average photosynthetically active radiation (PAR), and accumulated rainfall (8-day interval) of the sugarcane EC flux tower sites. (**a**) FAYS-Brazil site 2015–2017. (**b**) Chachoula, USA site 2018–2020.  
start and end of the sugarcane growing season (SOS and EOS) at this site were May and November, respectively, with a growing season length of 7 months.
## 3 Results
### Models for Predicting ANPP from NDVI Imagery  
For all images which had a simple linear regression R\({}^{2}\) > 0.5 (Table 4), the relationship between ln(ANPP) and NDVI was derived using a linear mixed model, for predicting ANPP outside the field survey plots from NDVI imagery. The model included a random effect to account for the clustering of the plots within the survey squares. The form of the model was:  
\[ln(ANPP)=m\times NDVI_{\text{LS\,TOA}}+c, \tag{2}\]  
where \(NDVI_{\text{LS\,TOA}}\) is the NDVI value calculated from Landsat 8 top-of-atmosphere reflectance, and \(m\) and \(c\) are the model coefficients, which are given in Table 5.  
Figure 7 shows the ANPP maps produced by applying the derived models to the NDVI imagery. Since the NDVI-ANPP relationships are image-date specific, different model coefficients were applied  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**R\({}^{2}\)**} \\ \hline
**Date** & **All Data** & **2013 Only** & **2014 Only** \\ \hline
19 May 2013 & 0.648 & **0.703** & 0.607 \\
13 April 2014 & 0.65 & 0.507 & **0.706** \\
6 October 2014 & 0.529 & 0.492 & **0.566** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coefficient of determination, R\({}^{2}\), for the regression between single date NDVI and in situ ANPP (g dry mass m\({}^{-2}\) year\({}^{-1}\)) using in situ data from both years, 2013 only and 2014 only. Bold values show the best R\({}^{2}\) value achieved for each image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Gradient, m** & **Intercept, c** & **Variance Explained by the Square** & **Residual Variance** & **No. of Field Plots** \\ \hline
19 May 2013 & 1.10 & 5.42 & 0.0103 & 0.0210 & 128 \\
13 April 2014 & 1.31 & 5.32 & 0.0150 & 0.0168 & 109 \\
6 October 2014 & 1.81 & 4.85 & 0.0130 & 0.0316 & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model coefficients and variance determined using a linear mixed-effects model, and the number of field plots used to calibrate each model.  
Figure 6: Time series of R\({}^{2}\) values for the regression of single date NDVI and in situ ANPP (g dry mass m\({}^{-2}\) year\({}^{-1}\)) using in situ data from both years, 2013 only or 2014 only.  
depending on the date the image was captured, as shown in Table 5. These maps illustrate the problem of cloud cover, as large portions of the image were obscured by cloud.
# Implementation of an Obstacle Recognition System for the Blind
## 3 Obstacle Recognition Model and System Architecture  
To recognize various obstacles, we used a Google Object Detection application program interface (API) [12] based on TensorFlow [13]. The API had various pre-trained object detection models, including single shot multibox detector (SSD) MobileNet and Faster R-CNN. These models could recognize 90 classes (e.g., cat, dog, car, bus). If we used the original pre-trained models, which were not specialized in obstacle data, we could not recognize any bollards or crosswalk lights. For this reason, the models needed to be customized using retraining. We collected obstacle data for bollards and crosswalk lights and retrained the models. The models were applied to the obstacle recognition module and recognized obstacles were learned in real time. According to the recognized results, the obstacles were input into the crosswalk discrimination module and the location calculation module, and a customized algorithm was applied. Each result of the algorithm was passed on to the client, and then, the client could monitor the location of a bollard and the state of the crosswalk light. The guidance was remarkably close to real time and was effective at keeping the client safe.
## 3 Results and Discussion
### Toxicity Investigations  
Microtox acute toxicity tests using _Vibrio fischeri_ bioluminescencement bacteria are often applied to characterize water samples. The method is based on the evaluation of light emission changes caused by the chemicals tested.  
No toxicity to _V. fischeri_ was observed in nonirradiated solutions (see in Figure 5). When the irradiated solution was introduced into the test mixture, high toxicity was observed, growing up to 100% luminescence inhibition at a 1-2 kGy dose. This high inhibition was certainly due to the effect of H\({}_{2}\)O\({}_{2}\) forming during radiolysis [22; 23; 24]. However, when H\({}_{2}\)O\({}_{2}\) was removed by using the enzyme catalase (recommended by [PERSON] and [PERSON], [38]),  
Figure 4: Inhibition zone diameters in 0.1 mmol dm\({}^{-3}\) irradiated and nonirradiated ofloxacin solutions in pure water (**A**), tap water (**B**), and purified wastewater (**C**) matrices.  
Figure 3: OUR values in 0.1 mmol dm\({}^{-3}\) irradiated and nonirradiated ofloxacin solutions in pure water (**A**), tap water (**B**), and purified wastewater (**C**) matrices. The dry matter content of sewage sludge was the same for all measurements.  
no or only minor inhibition was detected. These experiments show that the degradation products were either nontoxic or exhibited just a small toxicity to the test bacteria. The toxicity can be eliminated by properly selecting the absorbed dose [3; 12].  
In the experiments in tap water and purified wastewater matrices, the effect of H\({}_{2}\)O\({}_{2}\) was somewhat smaller, probably due to the transition metal content of these matrices, which catalyzes peroxide decomposition in Fenton-type reactions. We have to note that under practical conditions, H\({}_{2}\)O\({}_{2}\) decomposes quickly after the irradiation, since the water/wastewater to be treated always contains transition metal ions (e.g., Fe\({}^{2+}\)) in a higher concentration. As was discussed before, with the increase in dose, the oxacillin molecules are fragmented and transformed into small molecular mass carboxylic acids as suggested by AOS values. The fragmented molecules exhibit very low toxicity to _V. fischeri_ bacteria [39].
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 5 Experiment  
### Experimental Settings  
Three different types of datasets, _Dino_, _ZhangLan_, and _Simu_ were used in our experiment. _Dino_ is a public-accessed dataset with an image size of \(480\times 640\). _ZhangLan_ has an image size of \(338\times 600\) and was captured by us on our own campus. _Simu_ is a human-made simulation of camera projection with an image size of \(702\times 496\).
# A Simple Direct Method to Obtain Kinetic Parameters for Polymer Thermal Decomposition
## 3 Materials and Experimental Setup  
The polymers selected for study were poly(hexamethylene adjamide) (PA 6.6 or nylon 6.6), acrylonitrile/butadiene/styrene copolymer (ABS), poly(vinyl chloride) (PVC), linear low-density poly(ethylene) (LLDPE), poly(methyl methacrylate) (PMMA), and poly(methaphenylene isophthalamide) (NOMEX).  
Figure 4: Reactions in the NOMEX mass defined by the second derivative method.  
Figure 5: Degree of conversion vs. T for each defined reaction.  
Figure 3: Second derivative method to define the decomposition reactions applied to NOMEX. \(dm/dT\) black line and \(dm^{2}/dT^{2}\) grey line.  
PA 6.6 was analyzed at heating rates of 2.5, 5, 10, 20, and 40 K min\({}^{-1}\). Although heating rate of 40 K min\({}^{-1}\) is high enough to not be recommended by the ICTAC, it is included in the validation analysis as it is allowed by the standard [39] and higher values of heating rate are used by several authors for specific applications [33; 34]. The other polymers were studied at a single heating rate of 10 K min\({}^{-1}\) (Table 1) to examine decomposition patterns.  
A Netzsch STA 449 F3 instrument was used to carry out thermogravimetric experiments. The apparatus can operate at temperatures between 303 and 1773 K in oxidative or inert atmospheres. Its temperature and mass resolutions were 0.001 K and 0.1 \(\mu\)g, respectively. Because we are concerned with pyrolysis, all measurements were conducted under an inert atmosphere with a nitrogen gas flow of 40 mL min\({}^{-1}\). The percentage mass losses and \(dm/dT\) responses for the six materials at a heating rate of 10 K min\({}^{-1}\) for the temperature range where decomposition processes also took place, as shown in Figure 6a,b.  
The results indicate that the thermal decomposition processes exhibit different patterns of behavior depending on the material in question. PA 6.6, LLDPE, and ABS undergo a single abrupt decomposition reaction, PMMA and NOMEX participate in a lengthier combined process, and PVC undergoes distinct stepwise reactions.
## Appendix A Derivation of the Depth of the Tree-Like Information Bottleneck Graph  
### Number of Look-Up Stages  
The tree-like information bottleneck graph exploits the fact that the look-up tables are equivalent if the probability distributions of the inputs are equivalent. Hence, the vector \(\mathbf{y}=[y_{1},\ldots,y_{M}]^{\mathrm{T}}\) has to be split such that a balanced tree is obtained. This tree has the minimum depth (i.e., the minimum number of distinct look-up tables are required). If \(M\) (i.e., the number of incoming discrete messages) is a power of two, clearly an optimum split will result in \(\log_{2}M\) stages (cf. Figure 11a). The number of levels required if \(M\) is no power of two can be obtained from the binary representation of \(M\) (i.e., \(\mathbf{b}_{M}=[b_{msb},\ldots,b_{lsb}]^{\mathrm{T}}\)). Here, \(b_{\mathrm{msb}}\) denotes the most-significant bit and \(b_{\mathrm{lsb}}\) is the least-significant bit. Clearly, \(\log_{2}b_{M,\mathrm{msb}}=\lfloor\log_{2}M\rfloor\) gives a lower bound on the required stages. The approach to determine the number of stages \(s\) is similar to the decimal-to-binary conversion algorithm. In the first step, \(M\) is divided by two, since always two inputs shall be combined. The remainder is the least-significant bit indicating if an extra look-up table is needed (e.g., if \(M\) is odd). The quotient is again divided by two. Its remainder becomes the next bit again indicating if an extra look-up table is needed. This process repeats until a quotient of one is reached. The number of extra tables, which is equivalent to the number of ones following the most significant bit, is added to \(\lfloor\log_{2}M\rfloor\). Thus, at most \(2\cdot\lfloor\log_{2}M\rfloor\) stages are needed if \(M\) is not a power of two.
## 5 Experimental Scenarios and Results
### ADR-Det Stage 2 Detection Validation  
The experiment described in Section 5.1 is part of the ADR-Det validation process, examining the number of vehicles exiting the ADR-Det stage 2. Photos potentially contain (including false positives) vehicles carrying ADR signs. Table 3, column 3 presents the percentage of the stage 1 detected big vehicles (Table 3, column 2) with detected ADR signs. As part of the validation process, the authors manually checked each detected image. The last column shows the photos containing either fully or partially visible ADR signs from the authors' manual control. Again, the difference between columns 3 and 4 is attributed to false positives (\(\approx\)73.4% of the total dataset's images), meaning trucks with no ADR sign.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Toll Station**} & **Number of Photos** & **Number of Photos Exiting ADR-Det** \\  & **Processed** & **Stage 1 (\% Percentage)** \\ \hline MAL-1H & 6596 & 3302 (50.1\%) \\ MAL-1K & 8837 & 2986 (33.8\%) \\ \hline Total: & 15,433 & 6288 (40.7\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the validation process of the 1 st stage of ADR-Det algorithm, during March 2021, using two toll posts input data.  
Figure 5: Erroneous detection outputs from toll posts cameras. (**a**) Photo of the second worst example of what is a large vehicle (truck, bus, other non-car or motorcycle), with no other usable details. (**b**) Snow is covering almost the entire camera lens of what is also apparently a large vehicle (truck, bus, other non-car or motorcycle).
## 2 Bayesian Model for Mixtures
### Hierarchical Model in General  
As explained in the previous subsection, there are three unknown components, \(k\), \(w\), and \(\theta\). In the Bayesian framework, these three components are drawn from the appropriate prior distribution [16]. The joint distribution of all variables can be written as in Equation (3):  
\[\Pr(k,w,z,\theta,t)=\Pr(k)\Pr(w|k)\Pr(z|w,k)\Pr(\theta|z,w,k)\Pr(t|\theta,z,w,k), \tag{3}\]  
where \(w=\big{(}w_{j}\big{)}_{j=1,2,\cdots,k^{\prime}}\), \(z=(z_{i})_{i=1,2,\cdots,n^{\prime}}\), \(\theta=\big{(}\theta_{j}\big{)}_{j=1,2,\cdots,k^{\prime}}\), \(t=(t_{i})_{i=1,2,\cdots,n^{\prime}}\), and \(\Pr(\cdot|\cdot)\) are the generic conditional distributions [16]. Then, Equation (3) may be forced naturally, so that \(\Pr(\theta|z,w,k)=\Pr(\theta|k)\) and \(\Pr(t|\theta,z,w,k)=\Pr(t|\theta,z)\)[16]. Therefore, for the Bayesian hierarchical model, the joint distribution in Equation (3) can be simplified into Equation (4):  
\[\Pr(k,w,z,\theta,t)=\Pr(k)\Pr(w|k)\Pr(z|w,k)\Pr(\theta|k)\Pr(t|\theta,z). \tag{4}\]  
Next, we add an additional layer to the hierarchy, namely hyperparameters \(\gamma\), \(\delta\), and \(\xi\) for \(k\), \(w\), and \(\theta\), respectively. These hyperparameters will be drawn from independent hyperpriors. The joint distribution of all variables is then given in Equation (5):  
\[\Pr(\gamma,\delta,\xi,k,w,z,\theta,t)=\Pr(\gamma)\Pr(\delta)\Pr(\xi)\Pr(k| \gamma)\Pr(w|k,\delta)\Pr(z|w,k)\Pr(\theta|k,\xi)\Pr(t|\theta,z) \tag{5}\]