esa-sceva/satcom-chunk-collection huggingface dataset:
# Unit Cell Optimization of Groove Gap Waveguide for High Bandwidth Microwave Applications
## 2 Materials and Methods
### Design of Mushroom Groove Gap Waveguide (MGGW)  
After the refinement of the unit cell parameters, the design of the MGGW was modeled, targeting S-band frequencies while adhering to the standard dimension of the WR284 waveguide adapter. To analyze the behavior and effect of bends for the MGGW structure, unit cell structures were parametrically optimized for a straight structure and single right  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Category of Unit Cell** & \begin{tabular}{l} **Bandgap between the First and Second** \\ **Propagation Modes** \\ \end{tabular} \\ \hline Bed-of-nails-based EBG & \
\begin{tabular}{l} First mode cut-off frequency = 1.91533 GHz \\ Second mode cut-off frequency = 4.2251 GHz \\ Bandgap = 2.309 GHz \\ \end{tabular} \\ \hline Proposed mushroom-unit-cell-based EBG &
\begin{tabular}{l} First mode cut-off frequency = 1.6049 GHz \\ Second mode cut-off frequency = 4.1056 GHz \\ Bandgap = 2.50 GHz \\ A significant increase of 191 MHz (8\%) is \\ achieved for the proposed new unit cell \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dispersion profile comparison of unit cells with an air gap width (g1) of 4.036 mm.  
Figure 3: (**a**) Proposed mushroom unit cell for MGGW, air gap g1 = 4.036 mm, w1 = 10 mm, w2 = 15 mm, h1 = 25 mm, h2 = 34.036 mm, h3 = 5 mm, and \(p\) = 20 mm; (**b**) Dispersion characteristics of proposed periodic unit cell, resembling mushroom-type EBG, are presented according to the dimensions delineated in (**a**).  
angle bend (90\({}^{\circ}\)) and double right-angle bend transitions. These bend transitions were added to observe wave propagation and losses for the S-band frequency spectrum. The transitions of 90\({}^{\circ}\) bend were achieved using only a single pin structure for both types of simulated MGGW structures. In the literature, such transitions are achieved by using multiple pins or shaping the physical geometry of the structure [21]. A pictorial view of the proposed MGGW designed with straight and bent configurations is shown in Figure 4a,b, respectively. The cross-sectional view of straight MGGW is depicted in Figure 4a. The waveguide was designed for an electrical length of 5\(\uplambda\) for the center frequency, and it had a period of 20 for unit cells. The dimensions of the periodic cells were kept the same as mentioned in Figure 3a. In Figure 4b, the top view of single 90\({}^{\circ}\) and double 90\({}^{\circ}\) transitions along the waveguide is shown. The electrical lengths of the waveguides were taken as 5\(\uplambda\) and 6.5\(\uplambda\) for the two waveguide designs, respectively.
# Robust Template-Based Watermarking for DIBR 3D Images
## 3 Proposed Method
### Proposed Watermark Extraction Method
#### 3.3.1 Template Decoding  
1. Forward curvelet transform: The curvelet transform is applied to the test image.
2. Extract template points using corner detection: A peak was inserted into the \(l_{1}\) and \(l_{2}\) pair in the embedding step, with a \(90^{\circ}\) difference at each template point. Due to curvelet filter characteristics, the peak point spreads in a straight line in the corresponding direction. Therefore, the peak point is represented by an \"X\" shape when only coefficients of \(l_{1}\) and \(l_{2}\) are extracted, as shown in Figure 9, and can be found by corner detection. This paper employed Harris corner detection, but similar approaches would also suffice.
3. Estimate the degree of geometric deformation by exploiting modified ICP: The ICP method estimates the parameters of geometric distortion when there is no matching information of two point clouds. This method assumes that the closest points between the two point clouds match each other and repeats the process of minimizing the error [46]. This paper modifies the ICP to suit the problem in need of solving. Since the DIBR image has an error in the horizontal information, the weight of the horizontal distance error is set to \(1/2\). Geometric distortion parameters are estimated using the modified ICP method, comparing detected corner points with the template point ground truth. The template ground truth can be generated using the key as in the embedding step.
4. Recover the test image using the estimated geometric distortion parameters. If the degree of geometric transformation is large, the direction in which the template was inserted may have changed. For example, if the image was significantly rotated, the template inserted in the direction pairs \(l_{1}\) and \(l_{2}\) is moved to the direction pairs \(l_{1}+1\) and \(l_{2}+1\). In this case, the template decoding process is repeated for all direction pairs, and the estimated geometric parameters with the lowest ICP error are used.
Foliar application of Nano-MeJA (1 mM MeJA) on the Monastrell variety during two consecutive seasons also improved the amino acid composition in grapes and wines to a similar extent as the conventional treatment using a 10 times higher MeJA dosage (10 mM) [28]. This considerable MeJA dosage reduction is due to the role of the amorphous calcium phosphate nanoparticles, which provide a sustainable release and protection against thermal degradation of the elicitor, ensuring elicitor activity over longer period of times on the surface of the leaves [25]. These amorphous nanoparticles have also been proposed as potential nanocarriers of nitrogen containing compounds such as urea (U-ACP). Field experiments on Tempranillo grapevines demonstrated that grapes treated with U-ACP contained similar yeast assimilable nitrogen (YAN) and amino acids beneficial to wine aroma and taste as those treated with pure urea using a 15 times higher nitrogen dose [29]. Another study on wheat revealed a N dosage reduction of 40% compared to conventional urea treatments through foliar application of U-ACP [30]. In this work, we evaluated the effect of pre-harvest methyl jasmonate (MeJA) treatment applied in two different ways (conventionally or as nanoparticles) on fruit quality parameters and phenolic composition of the grape during the period between veraison and harvest. In addition, two types of calcium phosphate nanoparticles with the same composition but different morphology, i.e., nanocrystalline apatite (Ap) and its ephemeral precursor, amorphous calcium phosphate (Nano), were used as nanocarriers of MeJA (Ap-MeJA and Nano-MeJA, respectively) in order to verify if their function was the same or not during the ripening period.
# From Iris Image to Embedded Code: System of Methods
## 8 Error Correction Cascade
### Bit-Majority Coding  
The error rate \(p=0.35\) is too big for most error correction codes. Practically, the only possibility here is the majority coding of single bits. It is applicable for \(p<0.5\). At the coding stage, the bit is repeated \(n\) times. At the decoding stage, the sum of \(n\) successive bits is counted. If it is below \(n/2\), zero bit value is decoded, otherwise a unit. It is easy to see that odd values of \(n\) are preferable. If \(p\) is the error probability of a single bit and bits are altered independently the error probability of decoded bit is  
\[p_{D}(p)=1-\sum_{l=0}^{(n-1)/2}\binom{n}{l}p^{n-l}(1-p)^{l}=1-(1-p)^{n}\sum_{l=0 }^{(n-1)/2}\binom{n}{l}\left(\frac{p}{1-p}\right)^{l}. \tag{37}\]  
If the error probability of one bit of the code is \(p=0.35\), then bit majority coding with \(n=7\) will transmit bits with error probability \(p_{D}=0.2\). This value is below 0.25 and allows the use of Hadamard codes. Majority coding with \(n=15\) will give \(p_{D}=0.12\) for \(p=0.35\). A larger duplication is possible, but results in a larger code size.  
The parameter of this method, affecting its size and error probabilities is the bit repetition rate \(n\).
# On Transparency and Accountability of Smart Assistants in Smart Cities
## 2 Related Work
### Privacy and Security Issues in Smart Assistants  
[PERSON] et al. [26] studied the factors affecting people's adoption or rejection of smart speakers, their related privacy concerns, and privacy-seeking behaviors around these devices. They found that a lack of trust towards smart speaker companies was one of the main reasons for non-adoption among non-users. They also found that users of these devices did not understand their privacy risks, rarely used their privacy controls, and the privacy controls in these devices were not well-aligned with user's needs.  
[PERSON] and [PERSON] [49] demonstrated that voice-controlled assistants could be easily manipulated while bypassing Android's security mechanisms. They proposed that, while an AI base of these assistants makes them more extensible, it also makes comprehending their full risks difficult. [PERSON] et al. [50] manipulated skills of Amazon Alexa and Google Home by launching remote, large-scale attacks. They carried out these attacks remotely by publishing attach skills and stole user data and eavesdropped on their conversations. [PERSON] et al. [51] demonstrated how smart assistants could be exploited to collect user data without their knowledge. They developed a spyware app and disguised it as a popular microphone controller game app. They stealthily recorded incoming and outgoing calls and synthesized Google Assistants keywords. They used Google Smart Assistant to collect environment light and noisy data by exploiting accelerometer, ambient light sensor, and the microphone without being detected by the antivirus systems.  
[PERSON] [52] identified a lack of choice for users and the lack of control over data collections by existing smart assistants as their two main problems. They suggested that not only could these devices collect data from their direct surroundings but also other smart devices in their vicinity. This was demonstrated by [PERSON] et al. [24], who collected data from Amazon Alexa and its enabled devices to characterize the lifestyles and living patterns of its users successfully. [PERSON] et al. [29] presented a risk analysis of Google Assistant to identify security and privacy concerns of integrating artificial intelligence in Android smartphones. They raised concerns about the transparency in its permission use, and validation.  
Despite their wide-scale use and potential role in smart cities, the number of studies addressing privacy and security issues in smart assistants is small. Most of these studies focus on exploiting the abilities of different smart assistants [24; 49; 50; 51]. Likewise, other studies motivate to further investigate issues in the privacy controls of smart assistants and explore related risks [26; 29; 52].
## 3 Results
### Probiotics and Vitamin B6 Affect the Fecal Microbiome of LI Patients  
A total number of 1,782,640 raw sequences referred to genus was obtained and analyzed; 1,573,358 reads passed the filters applied through the QIIME split_library.py script. The mean value of the percentage of reads classified to genus was 89%. The average number of species operational taxonomic units (OTUs) identified in the fecal samples of ZR patients (mean value of 233) and PL (mean value of 179) significantly (\(p\) = 0.049) differed. Shannon index and \(\beta\)-diversity did not differ between ZL and PL samples (data not shown). Compared with 16S-rRNA (total bacteria), the highest significant differences between the fecal microbiota of the two groups (ZR and PL) were found using 16S-rRNA data (metabolically active bacteria). Eleven phyla (Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Chlorobi, Synergistetes, Spirochaetes, Tenericutes, Chloroflexi, Cyanobacteria, Fibrobacteres) were identified (Figure 4). Proteobacteria relative amount was higher (\(p\) < 0.05) in ZR samples compared with PL samples.  
The relative abundance of genus of _Bifidobacterium_ was higher (\(p\) = 0.002) in ZR (0.46%) compared with PL group (0.22%). Compared with PL, ZR drove several genus to a higher amount, including _Slackia_, _Enterococcus_, and _Thricococcus_ (\(p\) < 0.05). _Lactobacillus_ genus did not differ between ZR and PL. A decreased relative abundance of _Klesbiella_, _Serratia_, and _Enterobacter_ was detected in treated patients compared with PL (\(p\) < 0.05) (Figure 5). Compared to T0, the treatment ZR increased some components of the culturable microbiota (heterotrophic aerobic and anaerobic bacteria, presumptive lactic acid bacteria and _Bifidobacterium_) (Table S3).  
Figure 4: Relative proportions (percent) of phylum in the fecal samples of treated patients (ZR) and those of placebo (PL) patients.
## 3 Results and Discussion
### Limitations and Future Challenges  
The methodology presented herein for downscaling GRACE TWSA is a simple but reliable approach to acquire TWSA estimates at considerably higher spatial resolution than the typical GRACE footprint. It can be applied using times series of GRACE/GRACE-FO TWSA and GPM IMERG, where pixel-wise parameters of the regression equation are evaluated, considering the time lags between TWSA and precipitation. An obvious limitation is the inherited uncertainty of the remotely sensed information and the uncertainty introduced in the various computational steps, e.g., the resampling process. Previous works have relied mostly on a combination of modeled and remote sensing variables as auxiliary downscaling variables [15; 59; 60]. In the present work, the downscaling ability of a single remotely sensed dataset, i.e., GPM IMERG was evaluated using a regression formula with a residual correction algorithm, and the results are satisfactory. Other computational approaches, like machine learning, are also challenging alternatives, which may result in improved outcomes. Future works may focus on the use of corrected GPM IMERG datasets with ground measurements to acquire a better representation of the spatial variability of precipitation. Although GPM IMERG is considered as a global state-of-the-art precipitation dataset that is constantly improved, it has an inherent source of uncertainty, like all remotely sensed products. While previous works have highlighted the value of GPM IMERG products in hydrological research, there is an uncertainty in acquired estimates, and correction using ground measurements improves the reliability of the precipitation estimates [47], and this, in turn, can have a better representation of water resources variability and consequently in the downscaling outcome. Other auxiliary variables, known to influence the TWS, like topography in the form of the Digital Elevation Model (DEM), can also be incorporated easily in the downscaling process; however, topography implicitly impacts the GPM IMERG algorithm by the numerous gauge stations used in the validation efforts of the GPM products [61]. Nevertheless, one should keep in mind that incorporating many different auxiliary variables in the downscaling process may result in a complex process and may introduce additional sources of uncertainty. In the present work, the GRACE-FO data period was used to develop our methodology, as the pre-2018 period is influenced by temporal data gaps in the GRACE datasets. However, it should be noted that the presented approach can also be applied as a possible gap-filling method for GRACE missing months, especially during 2015-2018, where data gaps are frequent. A future challenge is to evaluate the potential for improvement of GRACE data temporal resolution using the daily GPM IMERG product.
# Reducing the Cost of Implementing Filters in LoRa Devices
## 1 Introduction
In this paper, two methods of optimizing LoRa transmitters are proposed in order to reduce the complexity of filtering. First, waveform segmentation is used to generate an entire basic LoRa chirp waveform from only a portion of the total number of chirp samples in order to reduce the size of the chirp generation ROM. While this does not directly impact the LUT size, it reduces the overall memory requirement. This method is inspired by the CSS transceiver design presented in [9] and it has been adapted for LoRa. The second method involves quantizing the LoRa chirp samples to a significant degree so as to reduce the number of unique input values to the multiplier-less filter, which helps to reduce the LUT size. While chirp segmentation does not add any error, quantization adds some rounding errors to the quantized chirp signals. It is important to ensure that the desired sample reduction can be achieved without significant performance degradation. The feasibility of implementing a multiplier-less SRRC pulse shaping filter in a LoRa transmitter will be evaluated in terms of the tradeoff between the potential sample reduction and impact of quantization noise on the performance. In order to quantify the effectiveness of the sample reduction, the number of samples required to form LoRa chirp waveforms and subsequent filtered signals shall be compared to that of an unoptimized LoRa device. The degree of sample reduction, therefore, depends on which spreading factors are supported, the length of the pulse shaping filter, if chirp segmentation is used, and the quantization step size (if any). Finally, the performance of the system will be evaluated for LoRa signals with various degrees of quantization. A LoRa communication system is simulated in Matlab, and the performance is evaluated in terms of the occupied bandwidth (OBW) of transmitted LoRa signals, the output of the fast Fourier transform (FFT) performed for symbol demodulation, and the bit-error-rate (BER). The goal is to find appropriate levels of quantization in order to significantly reduce the overall memory requirement while maintaining excellent performance.
## 4 Algorithms
### Ranking of Final Skyline Solutions  
Once the algorithm reaches the number of generations set by the user, the temporary skyline solutions of the last generation are regarded as the final results. Note that under the instruction of experts, we used two types of ranking standards in this study, including a priority score and an expected score. For the priority score \(imp_{i}\), suppose the \(imp_{i}\) of decision objective \(i\) is greater than the \(imp_{i}\) of decision objective \(j\). This means that the government attaches more importance to decision objective \(i\) than to decision objective \(j\). The expected score \(exp_{i}\) is the value the government expects the decision objective \(i\) to reach. While objective values are often considered to be either larger the better or smaller the better, in practice, governments generally have an acceptable range, and neither values above this range nor below are acceptable. These two scores are applied to the proposed algorithm as follows.  
Suppose the priority scores and expected scores for \(l\) decision objectives are (\(imp_{1}\), \(imp_{2}\),..., \(imp_{l}\)) and (\(exp_{1}\), \(exp_{2}\),..., \(exp_{l}\)): the \(k\) final skyline solution results are {\(f_{1}\), \(f_{2}\),..., \(f_{k}\)}, and the values of solution \(f_{i}\) in each objective are (\(o_{i1}\), \(o_{i2}\),..., \(o_{il}\)). Then, the solutions are ranked as follows.  
1. First, compare the objective values of \(f_{i}\) (\(o_{i1}\), \(o_{i2}\),..., \(o_{il}\)) to the expected scores (\(exp_{1}\), \(exp_{2}\),..., \(exp_{l}\)). If the value of objective \(j\) is better than the expected value, then this value is replaced by the expected score. If the value of objective \(j\) is equal to or poorer than the expected score, then the value of objective \(j\) is retained. After the comparisons are completed, the adjusted values of \(f_{i}\) are defined as (\(c_{i1}\), \(c_{i2}\),..., \(c_{il}\)).
2. The adjusted values are used to calculate the score of \(f_{i}\): \(Sc_{i}\)=\(\sum_{i=1}^{l}c_{ij}\times imp_{j}\).
3. After the scores of all of the solutions (i.e., \(f_{1}\)-\(f_{k}\)) are calculated, they are ranked from largest to smallest. Note that if the scores of two solutions are the same, we compare the weighted raw data of the two solutions using their priority scores, i.e.,\(\sum_{j=1}^{l}o_{ij}\times imp_{j}\), as the basis of their ranking. If they have the same weighting, we compare the values of each decision objective, beginning with that with the highest priority score, until we find the optimal solution.
## 2 Materials and Methods
### Analysis of the Influence of Data Source Errors on Parameter Inversion  
Monitoring subsidence issues in mining areas through drone-based photogrammetric and InSAR techniques yields data with errors compared to leveling data. Consequently, assessing the impact of data source errors on inversion parameters holds significant importance. Herein, the BFGS algorithm is employed to analyze the influence of data source errors on the inversion of parameters in the probabilistic integration method for static prediction.  
The simulation experiment was conducted considering the geological mining conditions in the study area. The parameters of the probability integration model were set as follows: subsidence coefficient (q) = 1.2, horizontal movement coefficient (b) = 0.22, tangent of major influence angle (tan\(\beta\)) = 2.37, mining influence propagation angle (\(\theta_{0}\)) = 88. Additionally, the deviation of the inflection points (s\({}_{1}\), s\({}_{2}\), s\({}_{3}\), s\({}_{4}\)) was set to 50 m.  
The subsidence values are initially simulated using the probability integration method. Subsequently, random noises of 3 cm, 5 cm, and 10 cm were added to all the monitoring values of the simulated strike and tendency lines for conducting the simulation experiments. Table 3 presents the root-mean-square error (RMSE) for each noise inversion parameter, and \"Pre\" represents the RMSE as a percentage of the initial parameter. Among the different noise levels (3 cm, 5 cm, and 10 cm), the inversion parameters for q, b, tan\(\beta\), and \(\theta_{0}\) were minimally affected. These four coefficients exhibited limited sensitivity to noise perturbations. However, with an increase in noise magnitude, the deviation between each parameter and its initial value also increased. In contrast, noise with a magnitude of 10 cm had a more pronounced impact on the deviation of inflection point (s), accounting for approximately 10% of the initial parameter.  
To assess the accuracy of the inversion parameters, ten sets of inversion parameters were employed for each type of noise to derive subsidence values, which were then compared with simulated subsidence to calculate the root mean square error (RMSE), as presented in Table 4. For the case with 10 cm noise, the maximum RMSE among the ten parameter groups was 18.94 mm, with a mean of 9.6 mm. When the noise was reduced to 5 cm, the maximum RMSE reduced to 6.42 mm, and the mean decreased to 4.04 mm. These results indicate a decrease in both the mean and maximum RMSE after noise reduction. Notably, the maximum RMSE of the subsidence inversion, within 10 cm of noise, accounted for only 0.99% of the maximum subsidence value. This finding suggests that the data source error remained within the 10 cm range, meeting the requirements of the parameter inversion for the mining subsidence probability integration method.