esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Scheme
### Data Hiding  
For each interpolated image block of 3 \(\times\) 3 pixels shown in Figure 2, 16-bit secret data are used to be embedded into the four interpolated pixels \(C\)(0,1), \(C\)(1,0), \(C\)(1,2), \(C\)(2,1). We  
Figure 2: Diagram of image interpolation.  
embed the 4-bit secret data \(\{b_{1}\), \(b_{2}\), \(b_{3}\), \(b_{4}\}\) into the four least significant bits (LSBs) of the interpolated pixel within the block by  
\[C^{\prime}(i,\,j)=16\times\left[\frac{C(i,\,j)}{16}\right]+8\times b_{4}+4 \times\ b_{3}+2\times\ b_{2}+b_{1} \tag{5}\]  
where \(C^{\prime}(i,j)\in\{C^{\prime}(0,1)\), \(C^{\prime}(1,0)\), \(C^{\prime}(1,2)\), \(C^{\prime}(2,1)\}\) is the stego pixel. Moreover, in order to achieve encrypted image authentication, 3-bit authentication data are used to be embedded into the central interpolated pixel \(C(1,1)\) within the block for tamper detection. Note that we use the authentication data to identify any modification made to the authenticated image. The authentication data of the three bits are generated by checking the intensity-relation of each block. For each interpolated image block of \(3\times 3\) pixels shown in Figure 2, the authentication data are computed by  
\[\begin{split} a_{1}&=\begin{cases}1,&\text{if }O(0,\,0)<O(0,1),\\ 0,&\text{otherwise}.\end{cases}\\ a_{2}&=\begin{cases}1,&\text{if }O(0,\,0)<O(1,0),\\ 0,&\text{otherwise}.\end{cases}\\ a_{3}&=\begin{cases}1,&\text{if }O(0,\,0)<O(1,1),\\ 0,&\text{otherwise}.\end{cases}\end{split} \tag{6}\]  
where \(a_{i}\) is the authentication data. Then, we embed the 3-bit authentication data \(\{a_{1}\), \(a_{2}\), \(a_{3}\}\) into the three LSBs of the central interpolated pixel \(C(1,1)\) within the block by  
\[C^{\prime}(1,\,1)=8\times\left[\frac{C(1,\,1)}{8}\right]+4\times a_{3}\ +2\times\ a_{2}+a_{1} \tag{7}\]  
where \(C^{\prime}(1,1)\) is the stego pixel.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
## 5 Conclusions  
Under a sum relay power constraint, this paper proposed the optimal power allocation schemes to maximize the achievable rates for hybrid three-hop relay networks when the channel gains and the transmit power from the source are given. By solving the optimization problem, we derived the transmit power value from the first relay in closed form for the AF-DF and DF-AF relay networks. With the same channel gains for the AF-DF relay network, we showed that more power should be allocated at the first relay than at the second relay to maximize the achievable rate. In addition, we derived the optimal power allocation scheme for the DF-AF relay network for the possible four cases. When the SNR of the first hop is the same, it is shown that the optimal power allocation scheme for the AF-DF relay network provides a higher achievable rate than for the DF-AF relay network when the channel gain between two relays is higher than that between the second relay and the destination. In contrast, the achievable rate of DF-AF relay network is greater than that of the AF-DF relay network when the channel gain between the second relay and destination is higher than that between two relays. Therefore, we can consider this an appropriate hybrid relaying scheme with optimal power allocation which enables us to provide the best performance in given channel conditions. Both the analytical solutions and simulation results show that the proposed optimal power allocation schemes outperform the grid search schemes and equal power allocation scheme. In addition, the complexity for determining the transmit power value from the first relay in the proposed schemes is much lower than in the grid search schemes.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper; [PERSON] has approved the submitted version.  
This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
# Antijamming Improvement for Frequency Hopping Using Noise-Jammer Power Estimator
## 4 Computer Simulations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distribution** & **RMSE (\(\mu_{1}\))** & **RMSE (\(\mu_{2}\))** & **RMSE (\(\mu_{3}\))** & **RMSE (\(\mu_{4}\))** \\ \hline Gamma & 6.03 \(\times\) 10\({}^{-7}\) & 2.03 \(\times\) 10\({}^{-7}\) & 3.23 \(\times\) 10\({}^{-8}\) & 3.60 \(\times\) 10\({}^{-9}\) \\ Normal & 3.71 \(\times\) 10\({}^{-6}\) & 2.62 \(\times\) 10\({}^{-7}\) & 1.44 \(\times\) 10\({}^{-7}\) & 1.89 \(\times\) 10\({}^{-8}\) \\ Weibull & 1.52 \(\times\) 10\({}^{-5}\) & 7.63 \(\times\) 10\({}^{-7}\) & 2.41 \(\times\) 10\({}^{-7}\) & 9.40 \(\times\) 10\({}^{-9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Root mean squared errors (RMSEs) of moments of fitted distributions. Figure 3: Cramér-Rao bound according to (**a**) bit-energy-to-noise-power spectral-density ratio and (**b**) number of symbols in a slot. In Figure 5, the receiver-operating-characteristic (ROC) curves of the proposed method and the conventional methods were evaluated to show detection probability according to various \(P_{fa}\). In Figure 5, the proposed method exhibited greater detection probability than conventional methods at small \(P_{fa}\) regions, and required a smaller \(P_{fa}\) for specific detection probability. As seen in Figures 4 and 5, the proposed detector demonstrated the best detection performance. Figure 4: Detection probability: (**a**) \(\rho=0.0005\), (**b**) \(\rho=0.005\), (**c**) \(\rho=0.05\). Figure 5: Receiver-operating-characteristic (ROC) curve for \(\rho=0.05\). Figure 6 illustrates the BER performance of the combining slots without jammed slots versus \(E_{b}/J_{0}\) at \(P_{fa}=10^{-10}\) for \(\rho=0.0005\), \(0.005\), and \(0.05\). The tested detection methods were the same as in Figure 4. For reference comparison, the BER from combining without- and with-discard of known jammed slots is also plotted with a black solid line and a dashed line, respectively. If perfectly jammed slots were discarded, the BER was about \(2\times 10^{-4}\) regardless of \(E_{b}/J_{0}\). If not, when \(E_{b}/J_{0}\) was less than 5 dB, and \(\rho\) was 0.0005, 0.005, and 0.05, the BER from combining all slots increased up to 0.001, 0.009, and 0.07, respectively. These results demonstrated that rejecting jammed slots by the jammer detector was important in increasing BER performance in FHSS systems. In Figure 6, the methods ranked by increasing BER performance are CA-CFAR, SO-CFAR, DJR, and the proposed method. This result matched with detection performance in Figure 4. In Figure 5(b), when \(\rho\) was 0.005 and \(E_{b}/J_{0}\) varied from 10 to 30 dB, the BER performance of the proposed method was similar to that of perfect jammer detection, while the BER performance of other detectors decreased at the \(E_{b}/J_{0}\) region. When \(E_{b}/J_{0}\) was larger than 30 dB, the BER of all schemes encountered an error floor. In this case, even though jammer power was small, BER performance did not increase at all for all cases. As seen in BER comparisons, the proposed method demonstrated the best BER performance.
## 3 Results and Discussion
### Influence of Degree of Saturation and Porosity on Electrical Resistivity--Laboratory Investigations
Literature reports for clean sands give the values of the tortuosity coefficient and the cementation exponent at a = 1.0 and m = 1.3, respectively [12]. Recent research and considerations have allowed to extend the range of these parameters, e.g., [12; 42; 46]. However, it should be remembered that in the case of unsaturated materials, this relationship will be inversed and the increase in porosity will lead to increase in the electrical resistivity of a porous medium [2]. This should be taken into account when interpreting data from in situ measurements. In the case of cohesive soils, including unsaturated ones, a simple predictable relationship between porosity and electrical resistivity cannot be expected. For this purpose, tests of unsaturated cohesive soil samples were carried out in a modified oedometer. The electrical response, caused by the change in the load of the soil sample, can be interpreted in terms of changes in porosity and fabric features such as pore tortuosity and particle alignment [16]. Figure 8 shows the consolidation curves in relation to curves of electrical resistivity changes as functions of time. The electrical resistivity of the sandy clay sample (sisaCl from SGGW Campus) recorded during the test is inversely proportional to the increasing vertical stress. This study resulted in setting an empirical relation between porosity and electrical resistivity, which is shown in Figure 9 and describes this relation when the saturation degree is close to 1, and is described by the equation:  
\[n=0.004\cdot\rho_{b}+0.309, \tag{6}\]  
where n is the porosity [-] and \(\rho_{b}\) is the electrical resistivity of soil [\(\Omega\cdot\)m]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil** & **Effective Size d\({}_{50}\) [mm]** & **m [-]** & **R\({}^{2}\) [-]** \\ \hline Fine sand (FSa) & 0.22 & 1.39 & 0.86 \\ Medium sand (MSa) & 0.40 & 1.41 & 0.89 \\ Coarse sand (CSa) & 0.80 & 1.45 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Empirical constants in the relationship between porosity and electrical resistivity according to Equation (5) [45]. Figure 8: Changes in the void ratio and electrical resistivity during loading cycles of sandy clay (sisaCl) samples taken from the SGGW Campus in a modified oedometer.
# Getting Young People to Farm: How Effective Is Thailand's Young Smart Farmer Programme?
## 3 Data and Methods  
### Research Area  
The study was conducted in the Prachin Buri province in central Thailand (Figure 2) because of its importance as a large agricultural production area in the central region undergoing structural change (decreasing income from agriculture and a decline in the share of people employed in the agricultural sector). The province spans 2.98 million rai which equals 4762.36 km\({}^{2}\) (1 rai = 0.0016 km\({}^{2}\)) and is located ~136 km east of Bangkok. Most of the Prachin Buri province is used for agriculture (52.6%), especially rice (21.2%), perennial and fruit trees (16.2%), and field crops (10.8%) [33]. The agricultural sector contributes 2.2% to the Gross Provincial Product (GPP), where the service sector contributes 19.1% and the strongest industrial sector, comprising 965 large factories, contributes 78.7% [34]. The proportion of the population below 45 years accounts for 62.1% of the province's total population, while the population 60 years and older accounts for 16% [35]. Most people work in the service (47.3%) or industrial sectors (34.3%), with 18.4% working in the agricultural sector [33].  
Figure 1: A result chain for the Young Smart Farmer programme implemented in the Prachin Buri province in Thailand between 2014 and 2018. **Notes.** YSF = Young Smart Farmer, PCAEO = Prachin Buri Provincial Agricultural Extension Office, DAEO = District Agricultural Extension Office, YEDLC = Young Farmer Development Learning Centre.
## Materials and Methods
### Nuclear Magnetic Resonance Test (NMR)  
In recent years, nuclear magnetic resonance (NMR) has been widely used for the study of pore water within rock and soil because of its fast, effective, non-contact and non-destructive manner of measurement [39; 40]. The basic principle of the NMR method is to apply a radio frequency magnetic field to a target in order to promote the energy transition of the nucleus (i.e., proton \({}^{1}\)H of soil water) and detect the relaxation processes after removing the applied magnetic field. The measured variation of the NMR signal (free induction decay) with time, referred as the FID curve, is correlated with the transverse relaxation time T\({}_{2}\), which is a measure of the mobility of the water molecule [41]. The T\({}_{2}\) distribution can be determined by the use of the Fourier inversion of the FID curve and it usually shows distinct peaks, a property which reflects the different energy states of the pore water. This method, therefore, can be used for distinguishing capillarity-dominated water from adsorption-dominated water. The amount of each type of water can be represented by the peak area (under the T\({}_{2}\) curve) within the corresponding T\({}_{2}\) regime [42]. In this study, all of the loess specimens that were prepared with the aforementioned wetting and drying methods were subjected to NMR tests; the relative amounts of capillary and adsorbed water in each specimen were derived based on the T\({}_{2}\) measurements so as to assist in analyzing the mechanism of the loess' tensile strength's variation with its water content.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 4 Results
### Thruster Fault Detection
As shown in Figure 11c, the false alarm rates varied from 16 to 26, and these rates are much higher than that of the blending filter solution. Several faults are presented in this paper for the fault detection rates test. The first group was 1000 A-type faults; the second group was 1000 B-type faults; the third group was 1000 C-type faults; the fourth group contained mixed faults i.e., 1000 B-type and C-type mixed faults. The \(T_{Dn}\) was used in this test and the test results are given in Table 2.  
Based on the classification of class A, B, and C faults, it was easy to distinguish the fault classed based on the state of the control input of class A faults which is different from that of class B and C faults. The class B and C faults produced different thrust so the \(\lambda_{k}\) from fault detection functions were different and could be classified by comparing their values. All multiple thrusters received the same control command, and the results suggest that class B and C faults can be part of the same group of control thrusters at the same time. Hence, when class B and C failures occur at the same time, the thrust generated is greater making fault detection easier. The blending filter solution has a high detection rate of A-type, B-type, B and C mixed faults i.e., at than 90%, and a good detection rate of 87.8% was obtained for type C leakage faults as they are not easy to detect as shown in Table 2. However, owing to the lack of the suppression function of approximate control signal noise, the EKF based solution showed worse performance than the blending filter solution in the fault detection rates test. The test results are shown in Table 2.  
Figure 11: False alarm rates test: (**a**) blending filter solution false alarm rates test by \(T_{D}\); (**b**) blending filter solution false alarm rates test by correction \(T_{D}\); (**c**) extended Kalman filter solution false alarm rates test by correction \(T_{D}\).
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 3 Proposed Method
### Procedure of Encryption and Data hiding
#### 3.2.1 Image Encryption  
The proposed method uses the G-EtC method [39] in the encryption process. We omitted the color transformation process in the G-EtC method to guarantee full reversibility and alternatively combine RGB color components to derive a grayscale image.  
\begin{tabular}{l l} Step1-1: & Combine R, G, and B components of an original image \(I\), and a grayscale-based image \(I_{G}\) is derived. \\ Step1-2: & Divide \(I_{G}\) into multiple blocks with \(B_{x}\times B_{y}\) pixels. \\ Step1-3: & Conduct position scrambling, block rotation or flip, and negative-positive transformation on each block, and obtain an encrypted image \(I_{G_{enc}}\). \\ \end{tabular}
## 4 Results and Comparative Analysis
### Cross-Domain Risk Propagation
A local or remote attacker that gains access to the smartphone, via a phishing attempt, malware, or ADB misconfiguration, could reuse these credentials as if they were a legitimate user. Since the server relies on token acceptance, we may see the same propagation and abuse example to the upper levels of the application, and ultimately the server, potentially compromising the endpoints' associated patient, record, data, or analytics function. Finally, the components use insecure protocols and allow legacy functionality (e.g., a permissive UPnP network setting on the server, permissive SELinux permissions on the phone and watch), which can support lateral movement. An attacker who compromises a single weak point from either the wearable or mobile layer increases the actors' privileges, while allowing for a permanent connection to the backend services, further allowing them to take unauthorized actions or make sensitive information available for large scale extraction [57; 58; 59]. Based on repeated emulation trials in our controlled test environment, the end-to-end exploitation chain, initiated via passive BLE sniffing from the Galaxy Watch 7, token injection into the Samsung Galaxy A55, and culminating in API misuse on the container backend, was successfully executed in under 7-10 min per scenario. The overall success rate across 10 simulated runs exceeded 87%, assuming proximity under 3 m, default SELinux configurations, and no prior user interaction. These performance metrics underscore both the real-world feasibility and systemic risk of multi-layer propagation in mHealth infrastructures. This addendum reiterates that there is a need for a coherent set of security policies and continuous telemetry around the layers and endpoints in the health data  
Figure 3: Exploitation flow from BLE-based wearable to backend server, highlighting trust boundaries and propagation path. delivery for an organization so that appropriate defense-in-depth actions can be taken at the first compromised layer [6; 34; 47]. To complement these scenarios, we conducted controlled simulations of key attack vectors. BLE-based data interception and replay were consistently successful within 2-3 min in close-range conditions. Token reuse attacks succeeded in 4 out of 5 trials, while container CVE exploitation (e.g., zlib and SQLite) was achieved within 5 to 10 min using public tools, depending on patch status and image configuration.
## 3 Results and Discussion
### Study Participants  
Twelve participants took part in the research study. Five of them were male and seven were female. There were four married couples among the participants. The remaining four participants lived in a single household. The youngest participant was 67 years old, whilst the oldest participant was 79 years old. The average age of the study participants was 72.17 years.  
At the beginning of the study, important information about participants' wants, needs, backgrounds and concerns was gathered with the modified wants and needs analysis. Six of the participants had a university diploma; five of them had finished some high school, while just one had finished only elementary school. Five participants out of 12 were using computers. On the other hand, all 12 participants owned a mobile phone and watched TV regularly. The study results showed that on average, the participants spend approximately 3.5 hours a day watching TV. Many of them explained they usually watch TV in the evenings, after 6:00 pm.  
The participants described their general health condition as appropriate for their age. The most common diseases reported among the participants were high blood pressure, high cholesterol, stomach diseases and diabetes. Some of the participants had some additional chronic diseases they did not want to reveal. All 12 participants reported they were taking medicines on a daily basis. Seven of them take a medicine only once a day. Three participants take a medicine three times a day and only two of them take their medicine twice a day. When asked whether they were forgetting to take their medicines, both prescribed and not prescribed, the participants reported they usually do not forget; however there were times when they failed to remember to take the medicine. The participants presented several different remembering procedures, such as: special medicine boxes, particular medicine placements (near the remote control, on the table in the living room, in the kitchen), lists and medicine schedules, and \"living\" reminders (usually partners, children or relatives). In our discussions with the participants, we also found out that often they forget the instructions about taking a specific medicine. To help them in such situations, video clips containing the instructions were added to the MedReminder service.
## 2 Methodology
### Fourier Markov Direct Grey Model (the FMDGM(2,1) Model)  
In order to develop the FMDGM(2,1), the residual of the DGM(2,1) predicted series is adopted. The mathematical function of the proposed model is introduced as follows [3,12,14].  
Step 1: Construct the FDGM(2,1) model.  
From the original series \(x^{(0)}\) in Equation (1), the predicted series \(\hat{x}^{(0)}\) of the DGM(2,1) model is obtained in Equation (5), and then the residual series \(e\) is defined the Equation (11).  
From Equations (10)-(13), the parameters of the Fourier series are calculated, and the predicted series \(\hat{x}_{f}\) of the FDGM(2,1), as presented in Equation (13).  
Step 2: Build up the Markov state transition matrix.  
Based on the distribution of the predicted series FDGM(2,1) \(\hat{x}_{f}\), and according to the Markov chain theory, states of stochastic process can be divided. Where \(\hat{Y}_{fm}(k)\) is the Markov chain, the values of \(\hat{Y}_{fm}(k)\) are distributed in the region of \(\hat{x}_{f}\). This uses the same process of constructing the MDGM(2,1) model as in Section 2.2. The transition probability and each state of \(\hat{Y}_{fm}(k)\) can be calculated using the formula of the Equations (6) and (7).  
Step 3: Obtain predicted values of the FMDGM(2,1) model.  
After determining the transition state and its probability by the Equations (6) and (7), we can utilize the \(R(m)\) probability matrix to calculate the future transition state of a system. The state probability for the next step is defined by probability in \(\pi\) vectors, denoted as \(\{r_{i}(t),i=1,2,\ldots\pi\}\) at time step \(t\). Set the center of the \(\pi\)-state as \(\{w_{i},(i=1,2,r)\}\). Then, the forecast values for the modified FMDGM(2,1) model are obtained as:  
\[\hat{x}_{fm}^{(0)}(t+1)=\hat{x}_{f}^{(0)}(t+1)+\sum_{i}^{\pi}r_{i}(t)w_{i} \tag{15}\]  
where \(r_{i}\) is the corresponding weight for the state \(i\).