esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In [11], a lightweight and efficient stacked intelligent meta-surface (SIM) is mounted on a LEO satellite to achieve multi-user beamforming. Similarly, reconfigurable intelligent surfaces (RISs) are utilised in [12] to enhance coverage in space-air-ground integrated networks. Later, the fractal concept is adapted to design reflectarray unit cells instead of square or circular ring structures. The self-similarity and space-filling properties of the fractal [13] help to miniaturise the array structure with the required performance. Different fractal shapes, such as the Koch triangle, Minkowski fractal, and Koch square unit cells, are considered unit cells for reflectarrays. Their performances are tested in [14] with various scaling factors and fractal iterations. A new opportunity is unlocked for fixed-size and different-size reflectarray designs with the fractal concept. Similarly, [PERSON] et al. developed a miniaturised Minkowski fractal reflectarray with a fixed-size patch [15]. A 15 \(\times\) 15 reflectarray prototype is designed and presented for fixed-beam and large-scanning capabilities. The balanced performance characteristics and ease of fractal reflectarray design draw the attention of many researchers to its use in satellite communications. Although many reflectarray designs have been proposed for satellite communications, they remain bulky and complex in design, and there is considerable scope for miniaturising these designs. Additionally, the major performance parameters must be evaluated in relation to different design parameters, and a relationship between them needs to be established. This motivated the study in the design and performance evaluation of miniaturised fractal reflectarrays and to test it in a real-time simulation environment for real-world applicability. In this article, a similar Minkowski Fractal concept, as presented in [6], is used as the unit cell for the design of a reflectarray in Section 2. The evaluation of performance metrics, such as gain and beam scan capabilities, is explored to the maximum extent here, with the help of sensitivity analysis of the design parameters in the same section. A wide variety of design parameter options are examined to find the best possible outcome of the fractal reflectarray. The reflectarray prototype is then fabricated, and its reflection and radiation characteristics are measured using a horn antenna as its feeder in Section 3. After that, a comparison is made in terms of size and other performances of the proposed reflectarray with other available similar designs. The performances of the same antenna are then tested in a real-time MATLAB simulation environment in Section 4. A satellite communication scenario is created with satellites in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and a ground station. The designed antenna is then imported and tested for interference scenarios under varying conditions and times. Ultimately, the conclusion is drawn in Section 5, outlining the possible future applications of the design.
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
## 3 Single Trace Analysis on a CDT Sampler
### Examining Raw Traces  
The targets of attack are steps 4 to 6 in Algorithm 2. Due to the application of timing attack countermeasures, step 5 in Algorithm 2 is repeated \(len\) times regardless of the selected random value. However, note that 1 is added to \(S\) when the selected random is larger than the table value. When the selected random is larger than the table value, the result of a subtraction is negative so that the Hamming weight of the value increases. More specifically, note that the significant bits are 1 s when the negative numbers are expressed in two's complements. Therefore, the Hamming weight of the subtraction result is at least 16 bits minus the number of bits used for sampling. On the other hand, when the result of a subtraction is positive, then 0 is added to the value \(S\). Consequently, its Hamming weight is at most the number of bits used for sampling. For a numerical example, suppose the values are expressed in 16-bit integers, and nine bits are used to sample as in FrodoKEM [18]. When the result of a subtraction is negative, the most significant seven bits are 1, while half of the remaining bits are 1 on average. Therefore, negative values will have Hamming weight of 11.5 on average. In a similar manner, when the result of a subtraction is positive, the most significant seven bits are zero, while half of the remaining bits are 1 on average. Hence, positive numbers will have Hamming weight of 4.5 on average.  
Due to the gap of Hamming weight between positive and negative number, the sign of the subtracted value (i.e., \(\Psi[i]-rtd\)) is visible in the power consumption trace. Furthermore, this sign is related to adding 1 or 0 to the \(S\). As a result, instantaneous power consumption increases when 1 is added to the value compared to when 0 is added. We claim that adding 1 and 0 in step 5 can be detected through STA, which allows an attacker to find out which number was sampled by counting this information.  
In order to support our claim, we sampled using a CDT sampler that does not cast the subtracted value to an unsigned type. We implemented the CDT sampler in ATmega128 (Atmel, San Jose, CA, USA) and the measurement devices used to obtain the power consumption traces involved a LeCroy HDO6104A oscilloscope (Testforce, Pickering, ON, Canada) sampling at 1 GS/s. The red line in Figure 1 illustrates the power consumption trace when the subtraction result is negative, and the red line in Figure 1 illustrates the power consumption trace when the subtraction result is positive. As depicted in Figure 1, the result was expected as described above. Due to the large Hamming weight of the negative value, an attacker can deduce whether the subtracted result is positive or negative.  
Figure 1: (a) power consumption trace when subtraction result is negative; (b) power consumption trace when subtraction result is positive.
## 13 Results and Discussion
### Aggregate Performance Table  
Table 6 consolidates these findings. All algorithms achieve feasibility in all 30 runs. JADEDO attains the most negative (thus, the best) composite objective score of \(-52.4180\) and also leads with a mean composite objective of \(-36.8937\). GWO is a strong second,  
Figure 12: Comparison of mean effectiveness over all feasible solutions. Higher means more effective threat mitigation.  
Figure 13: Average convergence curves (composite objective) across all runs. Each line represents the mean over 30 runs for the respective algorithm.  
with a best score of \(-51.9165\) and an average score of \(-36.4190\), while FLO follows closely with a score of \(-36.3929\) for the average composite objective. SSOA, although consistent, converges to a higher mean of around \(-33.0937\). Chimp's performance falls in the intermediate range with an average of \(-35.9650\). Meanwhile, GWO and FLO run very quickly with MeanCPUTime values of about \(0.0783\) and \(0.0845\) s, respectively, whereas JADEDO requires a bit more time on average, at \(0.6995\) s.  
Across these results, we see that JADEDO is the strongest performer in terms of effectiveness and objective quality, though at a slightly higher computational cost. FLO and GWO balance speed with robust solutions. Chimp has moderate performance, and SSOA shows reliable results with lower variability, though it converges to a less negative objective. In scenarios where marginal gains in effectiveness can be critical, JADEDO or GWO might be preferable, while FLO or GWO may be chosen in time-sensitive settings due to their faster run times.
# Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning
###### Abstract  
Online handwriting recognition has been the subject of research for many years. Despite that, a limited number of practical applications are currently available. The widespread use of devices such as smartphones, smartwatches, and tablets has not been enough to convince the user to use pen-based interfaces. This implies that more research on the pen interface and recognition methods is still necessary. This paper proposes a handwritten character recognition system based on 3D accelerometer signal processing using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). First, a user wearing an MYO armband on the forearm writes a multi-stroke freely character on a touchpad by using the finger or a pen. Next, the 3D accelerometer signals generated during the writing process are fed into a CNN, LSTM, or CNN-LSTM network for recognition. The convolutional backbone obtains spatial features in order to feed an LSTM that extracts short-term temporal information. The system was evaluated on a proprietary dataset of 3D accelerometer data collected from multiple users with an armband device, corresponding to handwritten English lowercase letters (a-z) and digits (0-9) in a freestyle. The results show that the proposed system overcomes other systems from the state of the art by 0.53%.  
3D accelerometer data; handwritten character recognition; Convolutional Neural Networks (CNN); Long Short-Term Memory (LSTM); 3D signal processing Article
# Characterization of the Solution of the Seismic Source Mechanism in Southeastern Sichuan
## 5 Discussion
In situ stress measurements indicate that the maximum horizontal principal stress in Region B, which is closer to the Tibetan Plateau, reaches approximately 130 MPa [72], while in Region A, located at a greater distance, this value is around 112 MPa [73]. These data suggest that Region A, with its relatively lower background stress levels, is more susceptible to stress field deflection under the influence of equivalent stress perturbations. The stress gradient within the Sichuan Basin also provides insight into the second major difference between Regions A and B, namely, the variations in their seismicity patterns as indicated by their respective Mohr circles. In Region B, where the background stress level is higher, stress perturbations are more likely to precipitate rupture in unstable faults. Conversely, in Region A, the lower background stress level, in conjunction with the injection of high-pressure fluids during shale gas extraction, facilitates more pronounced pore pressure changes, thereby increasing the likelihood of seismic events.
###### Abstract  
Space borne synthetic aperture radar (SAR) data have become one of the primary sources for aboveground biomass (AGB) estimation of forests. However, studies have indicated that limitations occur when a single sensor system is employed, especially in tropical forests. Hence, there is potential for improving estimates if two or more different sensor systems are used. Studies on integrating multiple sensor systems for estimation of AGB over Malaysia's tropical forests are scarce. This study investigated the use of PALSAR-2 L-band and Sentinel-1A C-band SAR polarizations to estimates the AGB over 5.25 million ha of the lowland, hill, and upper hill forests in Peninsular Malaysia. Polarized images, i.e., HH-HV from PALSAR-2 and VV-VH from Sentinel-1A have been utilized to produce several variables for predictions of the AGB. Simple linear and multiple linear regression analysis was performed to identify the best predictor. The study concluded that although limitations exist in the estimates, the combination of all polarizations from both PALSAR-2 and Sentinel-1A SAR data able to increase the accuracy and reduced the root means square error (RMSE) up to 14 Mg ha\({}^{-1}\) compared to the estimation resulted from single polarization. A spatially distributed map of AGB reported the total AGB within the study area was about 1.82 trillion Mg of the year 2016.  
aboveground biomass; tropical forest; microwave sensor system +
Footnote †: journal: Applied Sciences
# Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study
## 4 Discussion
### Limitations  
We conducted a cross-sectional study including 300 older inpatients with cancer (age \(\geq\) 65 years). Our cohort was a mix of patients with solid cancer and hematological neoplasm. While subgroup analysis was possible for patients with hematological disease, the sample size was too small to compare different entities of solid cancers (except for lung cancer). Overall, subgroup analysis should be interpreted carefully due to the small sample size. While patients \(\geq\) 85 years were included, we only surveyed 13 individuals, which is not representative. Here, a larger sample size is required to make assumptions on nutritional risk and symptom burden in this particular age group. No distinction between patients with curative or palliative concepts was made. We also have to consider that our study's design only identified patients at risk but did not confirm manifesting malnutrition by nutritional assessment. The screening results were forwarded to the treating physicians. A shortcoming of this anonymous study is that we could not follow up on whether treating physicians initiated a deeper nutritional assessment of patients at risk or not. As the data analysis was mainly descriptive, we did not consider adjusting for multiple testing. Overall, our screening tool is reductive. Screening results should be used as a starting point for a deeper, more detailed assessment and the counseling of patients at risk. No information on comorbidities such as gastrointestinal diseases or other factors influencing nutrition (surgery, irradiated fields) was assessed.
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Space Segment Modeling
#### 3.1.1 Dividing Shells  
StarPerf [4] divides satellite shells based on altitude and inclination from launch records, and it uses silhouette coefficient analysis to partition orbital planes according to real-time satellite orbit data. By plotting average silhouette coefficients for different shells, StarPerf determines the number of satellites per shell--a critical parameter. However,while StarPerf successfully imports real-time orbital data into its models, this method does not fully account for the dynamic nature of satellite operations. Satellites do not consistently operate at the altitudes and inclinations specified in FCC filings, as shown in Figure 3. Consequently, silhouette coefficient analysis may not accurately reflect the real-time partitioning of satellite orbital planes.  
To achieve shell segmentation and orbital plane division, SatScope employs the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, enabling flexible simulation of satellite networks based on real-time satellite data. DBSCAN is a density-based clustering algorithm that can effectively identify clusters of arbitrary shape and handle real-time satellite data, including satellites between shells. By analyzing the density distribution of satellite data, this algorithm automatically determines the number of shells and orbital planes without requiring pre-specified parameters, thus adapting to the dynamic changes in satellite networks.  
SatScope uses the altitude and inclination parameters from ephemeris data to perform shell segmentation of real-time satellites. The visualization of the clustering algorithm is shown in Figure 4. Table 1 presents the results of applying the DBSCAN algorithm for shell and orbital plane division. Typically, approximately 2% of satellites are not assigned to specific shells across multiple clustering outcomes. These unassigned satellites are usually either satellites that have not yet reached their operational orbits or those designated for specific applications.  
Figure 3: Comparison of altitude and inclination of real—time satellites. On the left of Figure 3, the probability cumulative distribution of the difference between the real—time altitude and the declared altitude of the satellite is presented. On the right of Figure 3, the probability cumulative distribution of the difference between the real—time inclination and the declared inclination of the satellite is shown. The variance of the data is indicated in the lower right corner of both the left and right of Figure 3. Specifically, 6.16% of the data exhibit an altitude difference of less than 0.5 km, and 12.46% of the data show an inclination difference of less than 0.001 degrees.
###### Abstract  
Satellite remote sensing of plant phenology provides an important indicator of climate change. However, start of the growing season (SOS) estimates in Northern Hemisphere boreal forest areas are known to be challenged by the presence of seasonal snow cover and limited seasonality in the greenness signal for evergreen needleleaf forests, which can both bias and impede trend estimates of SOS. The newly developed Plant Phenology Index (PPI) was specifically designed to overcome both problems. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000-2014) to analyze the ability of PPI for estimating start of season (SOS) in boreal regions of the Northern Hemisphere, in comparison to two other widely applied indices for SOS retrieval: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Satellite-based SOS is evaluated against gross primary production (GPP)-retrieved SOS derived from a network of flux tower observations in boreal areas (a total of 81 site-years analyzed). Spatiotemporal relationships between SOS derived from PPI, EVI and NDVI are furthermore studied for different boreal land cover types and regions. The overall correlation between SOS derived from VIs and ground measurements was rather low, but PPI performed significantly better (\(r=0.50\), \(p<0.01\)) than EVI and NDVI which both showed a very poor correlation (\(r=0.11\), \(p=0\). 16 and \(r=0.08\), \(p=0.24\)). PPI, EVI and NDVI overall produce similar trends in SOS for the Northern Hemisphere showing an advance in SOS towards earlier dates (0.28, 0.23 and 0.26 days/year), but a pronounced difference in trend estimates between PPI and EVI/NDVI is observed for different land cover types. Deciduous needleleaf forest is characterized by the largest advance in SOS when considering all indices, yet PPI showed less dramatic changes as compared to EVI/NDVI (0.47 days/year as compared to 0.62 and 0.74). PPI SOS trends were found to be higher for deciduous broadleaf forests and savannas (0.54 and 0.56 days/year). Taken together, the findings of this study suggest improved performance of PPI over NDVI and EVI in retrieval of SOS in boreal regions and precautions must be taken when interpreting spatio-temporal patterns of SOS from the latter two indices.  
vegetation phenology; SOS (start of season); MODIS time series; climate change; high latitudes; boreal forest 20179485; doi:10.3390/rs9050485
## 2 Materials and Methods
### Characterization Techniques  
X-ray diffraction (XRD) was conducted on background-free Si wafers using Cu K\(\alpha\) radiation from a Bruker Advance D8 diffractometer (Bruker, Billerica, MA, USA). Raman spectra were recorded with a micro-Raman system RM 1000 Renishaw using a laser excitation line at 532 nm. The N\({}_{2}\) adsorption-desorption isotherms were carried out at 77 K on an AutosorbiQ (automated gas sorption analyzer), Quantachrome INSTRUMENTS, porosimeter. Both PC and APC samples were outgassed under a vacuum (i.e., 10\({}^{-6}\) mbar) at 150 \({}^{\circ}\)C for 10 h before the experiments. Pore structure parameters were estimated using different models, such as the CPSM model [10; 11; 12; 13; 14; 15], the Brunauer-Emmet-Teller [16], the Horvath-Kawazoe model [17], and the Dubinin-Raduskevitch model [18]. The overall pore volume was calculated via the total adsorbed nitrogen amount at P/P\({}_{0}\) = 0.998. Atomic force microscopy (AFM) images were collected on silicon wafers in tapping mode with a Bruker Multimode 3D Nanoscope (Ted Pella Inc., Redding, CA, USA). Scanning electron microscopy (SEM) images were obtained using a JEOL JSM-6510 LV SEM Microscope (JEOL Ltd., Tokyo, Japan) equipped with an X-Act EDS-detector by Oxford Instruments (Abingdon, Oxfordshire, UK, an acceleration voltage of 20 kV was applied). Ultraviolet-visible (UV-vis) spectra of chromium solutions were measured in quartz cuvettes with a UV-2401(PC)-Shimadzu two-beam spectrophotometer. The scanning range was 400-700 nm and the increasing step was 0.5 nm. X-ray photoelectron spectroscopy (XPS) was performed with a PHI VersaProbeII (Physical Electronics, Chanhassen, MN, USA) spectrometer using an Al Ka source (15 kV, 50 W). The obtained data were evaluated and deconvoluted with the MultiPak (Ulvac-PHI, Inc., Osaka, Japan) software package. The spectral analysis included Shirley background subtraction and peak deconvolution employing mixed Gaussian-Lorentzian functions.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 8 Routing in a delay-tolerant WSN to a Single Mobile Sink
### Initial Optimization Problem Formulation  
The optimization problem was initially formulated as a quadratically constrained program \(QCP\). The following are the details of the formulation:  
* Decision variables: are all continuous and are classified as follows, 1. The actual decision variable sets are the time the sink stays at each location \(l\in\mathcal{L}\) within each tour, these were denoted by \(t_{l}\), and the data rate from node \(i\) to node \(j\) while the sink is at position \(l\), that were given as \(a_{ij}^{(l)}\). These two sets were replaced by the variables \(x_{ij}^{(l)}\), which represent the data flow between two nodes \(i\) and \(j\) with the sink at position \(l\), through the relation \(x_{ij}^{(l)}=t_{l}a_{ij}^{(l)}\).
2. \(y_{i}^{(l)}\) represents the the amount of buffered data at node \(i\) just as the sink leaves location \(l\).
3. \(T\) represents the number of cycles the mobile sink makes.  
Figure 12: Number of subgradient iterations needed for convergence by the partial and fully distributed schemes proposed in [2]. (**a**) Partially distributed scheme; (**b**) Full distributed scheme.  
* Objective function: Maximize the number of cycles the mobile sink makes i.e., \(\max_{T,x_{ij}^{(l)},y_{ij}^{(l)}}\). \(T\). This is a linear objective function in one continuous variable.
* Constraint Sets: The interpretation of the constraint sets is as given below, 1. Linear equality constraints that combine the transmission flows and buffered data for all possible sink locations to enforce conservation of flow constraints which guarantee that the total incoming flows for node \(i\) plus the buffered data is equal to the outgoing flows. 2. Non-negativity constraints on all variables that represent the flows, the buffered data and the number of rounds made by the mobile sink. 3. A bi-linear quadratic constraint set that guarantees that all the energy expended due to data transmission on the links for all possible sink positions. It was given by \(\left(\sum_{l=1}^{L}\sum_{j\in N_{l}(i)}e_{ij}^{(l)}x_{ij}^{(l)}\right)T\leq E _{i},\ \ \forall i\in\mathcal{N}\) where \(e_{ij}^{(l)}\) is the energy spent per unit data on the link \((i,j)\) when the sink is at position \(l\) and \(E_{i}\) is the available energy for node \(i\) and \(E_{i}\) is the available battery energy for node \(i\).