esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Third, the absence of electrical components eliminates spark-related safety risks in underground environments [20; 21; 22]. Most importantly, DOFS enables continuous spatiotemporal monitoring with high-resolution data acquisition, establishing an advanced methodology for the stability assessment of superincumbent formations. These advantages have led to the widespread adoption of DOFS in dynamic monitoring applications within physical simulation experiments [23; 24]. Currently, in the engineering field (such as mining and geology), distributed optical fiber technology mainly uses the optical fiber strain integration method to monitor the deformation of rock and soil bodies. This method requires a high coupling degree between the optical fiber and the structure being measured to ensure accuracy. To address these challenges, this study develops an integrated methodology that couples distributed optical fiber sensing technology with key stratum fracture mechanics for precise goat stress field prediction. The specific objectives of this research are to establish a new monitoring method by deploying fiber optic sensors at critical layers and establish a dual-state theoretical framework to describe the settlement behavior of the critical layer before and after breaking. By deploying optical fiber sensors in key layers, a new method for continuous spatiotemporal monitoring is constructed. Finally, a data-driven approach is created to connect fiber monitoring data with stress field prediction models, which is validated through physical experiments. The remainder of this paper is organized as follows: Section 2 presents the theoretical basis for key stratum fracture mechanics and establishes the mathematical relationship between stratum deformation and goat stress. Section 3 details the experimental setup, including the physical model construction and optical fiber sensor deployment. Section 4 analyzes the monitoring results and validates the proposed methodology through comparison with conventional pressure sensors. Finally, Sections 5 and 6 summarize the key findings and discuss their implications for mining safety practices. This research provides several significant contributions to the field. By integrating high-precision optical fiber sensing with theoretical modeling, we establish quantitative relationships between goat pressure changes and key stratum failures. The determination of the ultimate strain threshold for key stratum failure offers crucial parameters for stability prediction. Furthermore, our methodology enables continuous spatiotemporal monitoring while accounting for the mechanical properties of fragmented masses and the synergistic effects of critical stratum failure, addressing a significant gap in comprehensive inverse analysis approaches.
# Low Dose Ionising Radiation-Induced Hormesis: Therapeutic Implications to Human Health
## 4 The Phenomena of LDIR
### Bystander Effects
The degree of DNA damage induced by BE has been reported to be greater in high LET radiation compared to low LET such as X-rays and that it is cell type- and dose-dependent [86]. In 2011, [PERSON] et al. demonstrated a new phenomenon known as \"radiation-induced rescue effect\" (RIRE), where the bystander cells rescued the irradiated cells through intercellular signal feedback, reducing the cytotoxicity and genotoxicity induced by ionising radiation. The study showed that \(\alpha\)-particle-irradiated cancer (HeLa) cells were rescued by unirradiated bystander primary human lung fibroblast (NHLF) in two-cell co-culture systems after 24 h irradiation at doses of 20 or 40 cGy [87]. This finding has raised concerns over the efficiency of treatment of cancers using ionising radiation being compromised due to the rescue effect from unirradiated normal cells, as RIRE demonstrated a protective bystander effect [87; 88; 89]. Moreover, there are two types of RIRE, whereby Type 1 RIRE shows reduced detrimental effects on targeted cells upon receiving feedback signals from bystander cells [87; 88], while Type 2 RIRE exacerbate the detrimental effects in targeted cells upon receiving feedback signals from bystander cells [88; 90; 91]. Both the protective or the detrimental phenomena of BE are dependent on several factors, such as the types and characteristic of the bystander cells and the species of bystander factors, types and qualities of radiation, the biological endpoint and the radiation dose [88; 89].
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 5 Simulation Results
### Performance Comparison  
Table 2 presents a comprehensive performance comparison between the proposed method and the four existing approaches, with the RMSE results evaluated under the varying conditions specified in Sections 5.1-5.3. The proposed method consistently surpassed the search-based, EPUMA, and SS-MUSIC methods in performance, particularly in underdetermined and snapshot-limited scenarios, while maintaining compatibility with both uniform and non-uniform array geometries.
## 4 Opportunities and Open Research Areas in VANET Multimedia
### Overcoming Technical Challenges  
In the field of multimedia transmission, several key challenges have been identified by researchers. These primarily include the large amount of data and the need for effective resource management, both of which require in-depth study. One of the main needs is to allocate bandwidth resources effectively for multimedia dissemination. This may involve focusing more on the use of advanced compression techniques and adaptive streaming protocols.  
In addition, there is a critical need to optimize the delivery of static audio image-based information. This involves a careful balance between conserving resources and ensuring clear content delivery. Another important requirement is for context-aware resource management. This involves developing smart algorithms that can adapt to changing network conditions, user preferences, and content needs. In unpredictable network conditions, it is crucial to ensure reliable multimedia delivery, a key aspect of Quality of Service (QoS) assurance. This highlights the importance of using adaptive mechanisms and error-resilient coding schemes.  
Furthermore, real-time applications require minimizing latency. This involves a detailed examination of protocols and computational models that meet strict time constraints. Areas such as edge-computing integration and priority-based content delivery are included in this exploration. After careful examination, these challenges point towards a new era where innovative strategies and solutions will significantly contribute to achieving efficient multimedia communication within vehicular environments.
# Physicochemical Properties and Oxidation Status of Pork from Three Rearing Systems
## 2 Materials and Methods
### Oxidation Status Evaluation  
Thiobarbituric acid reactive substances (TBARSs): the extent of lipid oxidation was evaluated and determined as TBARS. First, 10 g of minced meat samples was homogenized for 2 min with 95.7 mL of distilled water and 2.5 mL of 4 M HCl. The samples were distilled until a 50 mL distillate was obtained. Then, 5 mL of 15% trichloroacetic acid, 0.375% thiobarbituric acid reagent was added to 5 mL of distillate and the mixture was heated in a boiling water bath for 35 min. The samples were left to cool then absorbance of the samples was measured at 532 nm by a spectrophotometer, against an appropriate blank. TBARS values were obtained by multiplying the absorbance value by 7.8. Oxidation products were quantified as malondialdehydedehyde equivalents (MDA mg\(\cdot\)kg\({}^{-1}\)) [25]. Free fatty acids (FFAs): determination was performed by the titration method according to CSN EN ISO 660 (588756) [26] and expressed as the percentage of total fat as oleic acid. Antioxidant capacity: the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method was used for the determination of the antioxidant capacity [27]. Sample preparation was according to [PERSON] et al. [28] by homogenizing 3 g of sample (minced meat) with 15 mL of 5% trichloroacetic acid, then 10 mL of chloroform was added. DPPH in methanol (0.025 g/L) was dissolved in order to prepare a fresh solution of radical stock. This fresh solution of DPPH was measured by a spectrophotometer against a blank at 515 nm, and the obtained result (absorbance value) was recorded (A0). A sample of meat extract (0.2 mL) was added to DPPH solution (3.8 mL) and the absorbance was measured (A10) after 10 min. The percentage of inhibition for the DPPH radical was calculated according [27] to the following formula:  
\[\text{DPPH}\left(\%\right)=\left[\left(\text{AbsA0}_{\text{DPPH}}-\text{ AbsA10}_{\text{DPPH}}\right)/\text{AbsA0}_{\text{DPPH}}\right]\times 100\]
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 1 Introduction  
There are many problems in rural medical services, such as insufficient funds, shortage of medical facilities, and unbalanced allocation of resources. More than half of the rural elderly in China suffer from chronic diseases and other complications. It is difficult for them to obtain the necessary long-term care services [1]. According to two surveys in 2015, the rural population accounts for more than half of China total population, but only 33% of the total medical resources are allocated to rural areas [2].  
Unbalanced rural medical resources and population aging have promoted research on intelligent health care [3]. A system that can continuously monitor the health of the rural elderly and share information with remote doctors or hospitals is of great significance to promote livable villages [4]. The Wireless Body Area Network (WBAN) is a wireless sensor network (WSN) technology. It is composed of interconnected, low-power, intelligent and miniaturized sensors, which transmit health data (temperature, arterial pressure, electroencephalogram signals, etc.) through the wireless medium in or on the human body [5].  
The number of nodes in a WBAN is usually small, and the connectivity of transmission links is affected by human posture. Most nodes are powered by microbatteries and have limited energy [6, 7]. If a node runs out of energy early, it will affect the network connection and eventually shorten the network lifetime. Compared with a traditional WSN, the signal transmission attenuation in the WBAN is faster. The human tissues increase the signal path loss, and the energy consumption of the WBAN is higher than that of other networks of the same scale. Battery replacement and charging are not feasible in implanted medical devicesand will cause the loss of monitoring data. Therefore, each node must work long with limited energy [8]. Routing algorithms play an important role in improving communication efficiency and prolonging the WBAN lifetime.  
Compared with those of the cities, the rural medical resources are less, and the rural elderly work more and lack adequate medical services. In addition, the loss of wireless signals in rural areas is greater. In this paper, we studied the application of the WBAN to monitor the health of the rural elderly. We introduced a delay-tolerant network to establish a time-varying prediction model of link connection probability. This delay-tolerant network takes into account both energy efficiency and data transmission efficiency and avoids multiple data retransmission by predicting the link connectivity probability. The data transmission probability was improved on the basis of reducing unnecessary energy consumption. According to factors such as link stability, residual energy and transmission distance, we constructed a transmission cost function based on data priority to transmit the elderly health data reliably.
## 3 Results
### Devices Comparison  
3.2.1. Student's T-Test Results for Paired Samples  
Table 4 displays the results of the paired-samples t-test for Pedar(r) and Physiosensing(r) device comparison, considering the MMP (kPa) and contact area (cm\({}^{2}\)) parameters.  
The analysis of MPP measurements revealed a mean difference of 39.13 kPa between the Pedar(r) and Physiosensing(r) platforms, with a standard deviation of 25.01 kPa and a mean standard error of 6.46 kPa. The 95% confidence interval for this difference was between 25.28 kPa and 52.98 kPa, showing an accurate and reliable estimate of the discrepancies. In addition, the \(p\)-value < 0.001 confirms that this difference is statistically significant.  
Analysis of contact area measurements revealed an average difference of \(-\)170.17 cm\({}^{2}\), indicating that the Physiosensing(r) platform records significantly larger contact areas than Pedar(r), justified by a greater number of sensors on the Physiosensing(r) platform. Furthermore, the standard deviation of the differences was 51.47 cm\({}^{2}\), with a standard error of the mean of 13.29 cm\({}^{2}\), which reflects the variability of measurements. The 95% confidence interval was between \(-\)198.67 cm\({}^{2}\) and \(-\)141.67 cm\({}^{2}\), providing a robust estimate  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Paired Differences**} \\ \cline{3-8}  & & **Mean** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Error of the** \\ **Mean** \\ \end{tabular} & \
\begin{tabular}{c} **95\% Confidence Interval of** \\ **the Difference** \\ \end{tabular} & **Bilateral \(p\)** \\ \hline \multirow{3}{*}{Pair 1} & Pedar® MPP [kPa] & \multirow{3}{*}{39.13} & \multirow{3}{*}{25.01} & \multirow{3}{*}{6.46} & \multirow{3}{*}{25.28} & \multirow{3}{*}{52.98} & \multirow{3}{*}{<0.001} \\  & & & & & & & \\ \cline{1-1}  & \multirow{2}{*}{
\begin{tabular}{c} Pedar® contact area [cm\({}^{2}\)] \\ versus Physiosensing® contact area [cm\({}^{2}\)] \\ \end{tabular} } & \multirow{3}{*}{\(-\)170.17} & \multirow{3}{*}{51.47} & \multirow{3}{*}{13.29} & \multirow{3}{*}{\(-\)198.67} & \multirow{3}{*}{\(-\)141.67} & \multirow{3}{*}{<0.001} \\ \cline{1-1}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Paired-samples t-test comparison of MPP and CA obtained by Pedar® and Physiosensing®.  
Figure 4: Bland–Altman plot graphic for (a) MPP and (b) contact area.  
of the differences between the platforms. In addition, the \(p\)-value \(<\) 0.001 confirms the statistical significance of this difference.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 2 Related Work
[21] propose a barter-based cooperation system to increase the message delivery ratio. The authors develop a game-theoretic model in which the proposed approach indeed stimulates the cooperation of the nodes. The results show that the message delivery ratio considerably increases if the mobile nodes follow the Nash equilibrium strategy in the proposed mechanism, compared to the data dissemination protocol when no encouraging mechanism is present. [PERSON] et al. [22] propose a self-interest-driven (SID) incentive scheme to stimulate cooperation among selfish nodes for dissemination in autonomous mobile social networks. They formulate the nodal interaction as a two-player cooperative game whosesolution is obtained by the Nash bargaining theorem. The barter-based schemes will dramatically degrade routing performance if one of the two encountering nodes has fewer messages. [PERSON] et al. [12] propose a data dissemination scheme, called MuRIS, to allow nodes to cooperatively deliver information of interest to one another via chosen paths utilizing few transmissions. [PERSON] et al. [13] propose a credit-based incentive system, called MobiCent, to stimulate DTN selfish nodes to cooperatively forward messages to others. MobiCent also provides different payment schemes to cater to a client that wants to minimize either payment or delivery delay. [PERSON] et al. [14] propose a secure multilayer credit-based incentive scheme, SMART, to stimulate cooperation among DTN nodes. In SMART, a layered coins model is presented and serves as virtual currency to pay for the relays who participate in forwarding messages. [PERSON] et al. [15] propose Multicent, a game theoretical incentive scheme that not only provides cooperative incentive but also encourages nodes to follow defined rules to realize the desired performance objective. [PERSON] et al. [16] present a novel incentive scheme to stimulate selfish nodes to participate in probabilistic routing with credit and bargaining game theory. They model the message exchange process between a pair of nodes as a bargaining game. Hence, when a message is transferred from its source to its destination, it goes through a series of bargaining games. When this series of bargaining games is completed, the message reaches its destination. The above credit-based schemes only motivate nodes to deliver messages to other nodes with higher delivery utility, without considering the node congestion problem. So we propose the credit-based congestion-aware incentive scheme CBCAIS for DTNs to improve the single-copy routing performance when nodes are congested.
# An Application of Android Sensors and Google Earth
## 6 Maintenance Activities  
The maintenance decision criteria established for the developed maintenance management system were based on the collected IRI values for each segment. Generally, the maintenance strategies selected ranged from crack seals, slurry seals, and thin asphalt overlays to thick asphalt overlays. In other words, the selected type of treatment depended on the IRI values. The decision criteria for the maintenance activities were established as follows:  
* IRI < 2: the deterioration is very slight and there is no need for any treatment.
* 2 < IRI < 4: the pavement deterioration is mainly due to the presence of localized distresses such as cracks and potholes. This requires routine maintenance such as crack sealing and patching.
* 4 < IRI < 6: the pavement deterioration is major and the pavement surface needs treatment that reduces the rate of deterioration; therefore, preventive maintenance such as a slurry seal is selected.
* 6 < IRI < 10: in this case, enhancing ride quality is needed, so thin asphalt overlay is required.
* IRI > 10: this indicates that the pavement surface has become old; thus, a thick asphalt overlay might be the right choice.  
The suggested pavement maintenance activity based on the IRI values is illustrated in Table 2.  
Figure 8: The IRI values in a color-coded Google Earth map.  
Google Earth was used to reflect the type of maintenance selected for each segment, as presented in Figure 9.  
The unit costs of maintenance activities include labor, equipment, and materials. These were obtained from the city's General Corporation of Roads and Bridges, as presented in Table 3. Estimated unit cost for maintenance activities (prices were set in 2018).  
The total required maintenance cost calculated for each segment of the road network is presented in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**IRI Range** & **Pavement** & **Color Indicator** & **Suggest** & **Code** \\
**(m/km)** & **Condition** & **Color Indicator** & **Maintenance** & **Code** \\ \hline \(<\)2 & Very good & Dark green & Do nothing & M-00 \\
2–4 & Good & Green & Patching and/or & M-01 \\
4–6 & Fair & Orange & Slurry sealing & M-02 \\
6–10 & Poor & Red & Thin asphalt & M-03 \\  & & & overlaying (30 mm) & M-03 \\ \(>\)10 & Bad & Purple & Thick asphalt & M-04 \\  & & & overlaying (50 mm) & M-04 \\ \hline \end{tabular}
\end{table}
Table 2: Suggested pavement maintenance activity vs. IRI ranges.  
\begin{table}
\begin{tabular}{c c} \hline
**Maintenance Activity** & **Cost/Square Meter (USD)** \\ \hline Crack sealing (M-01) & 9 \\ \hline Patching and/or slurry sealing (M-02) & 35 \\ Thin asphalt overlay layer (M-03) & 54 \\ \hline Thick asphalt overlay layer (M-04) & 90 \\ \hline \end{tabular}
\end{table}
Table 3: Estimated unit cost for maintenance activities (prices were set in 2018).  
Figure 9: Road maintenance activity map.
## 3 SSF-EDF Schedulability Analysis  
We derive a schedulability test for SSF-EDF scheduling. Our derivation process follows the following idea: under SSF-EDF scheduling, the necessary conditions for task missed deadlines are derived. In negating this condition, the sufficient schedulability condition is obtained.  
Suppose that job \(\tau_{i,j}\) of task \(\tau_{i}\) first misses the deadline at \(A_{i,j}+D_{i}\) (see Figure 2). Under SSF-EDF scheduling, jobs with late deadlines will not affect jobs with early deadlines. Therefore, we give up the legitimate job sequence whose deadline is later than \(A_{i,j}+D_{i}\), and only consider the SSF-EDF scheduling of the job request of the remaining legitimate sequence. Thus, \(\tau_{i}\) missed deadline occurs at time \(A_{i,j}+D_{i}\) (which is the earliest task missed deadline to occur).  
For a given task set \(\Lambda\), let \(Q\big{(}t_{p},t_{q}\big{)}\) be the amount of processor time required within \(\big{[}t_{p},t_{q}\big{)}\), and let \(W\big{(}t_{p},t_{q}\big{)}\) be the amount of work done in \(\big{[}t_{p},t_{q}\big{)}\).  
Our derivation process has the following three steps: (1) to derive the lower bound of \(Q\big{(}t_{0},A_{i,j}+D_{i}\big{)}\) in \(\big{[}t_{0},A_{i,j}+D_{i}\big{)}\), (2) to derive the upper bound of \(Q\big{(}t_{0},A_{i,j}+D_{i}\big{)}\) in \(\big{[}t_{0},A_{i,j}+D_{i}\big{)}\), (3) combining (1) and (2) yields the necessary condition. \(t_{0}\) in the figure is a special time point, which we will explain later.
## 3 Results
### Land Use Evolution
#### 3.1.3 Projection of Land Use Evolution by 2050  
Given that the FLUS model was designed to be calibrated and simulated using only the most recent two years of data, the land use projections for 2050 were based on the data from the 2010 to 2020 period. Specifically, this research began with an investigation of land use information from 2010. The suitability probability of every land use class was computed using the FLUS method's neural network module, and then the land use evolution during 2010-2020 was simulated. The modeling results were validated against the real land use information in 2020. A satisfactory kappa coefficient of 0.736 was yielded, demonstrating high simulation accuracy. This value is higher than the kappa coefficient (0.67) obtained by the original FLUS research for China's national simulation [36], and the kappa coefficients (0.76 in 2015) and (0.72 in 2018) obtained by a regional application in Hengyang City [66]. Our model shows comparable accuracy despite covering a much larger geographical extent. This validation supports the reliability of the FLUS model in simulating future land use evolution at the national scale.  
Subsequently, a Markov Chain approach was employed for projecting the area change in land use by 2050. Finally, we projected the land use status in 2050 with the support of the well-calibrated model (Figure 3).  
The spatial analysis reveals several critical transformation patterns. The reduction in grassland is predominantly concentrated in northwestern China's ecologically fragile regions, while construction land expansion exhibits a continuous sprawl pattern along the  
Figure 3: Land use projection results in 2050.  
eastern coastal areas. Notably, there is a high spatial correlation between grassland loss and unused land expansion, suggesting potential ecological degradation risks. Although unused land shows the largest absolute increase, construction land demonstrates the highest annual rate of change (1.90%), indicating that urbanization remains the dominant driving force. The reciprocal relationship between grassland and forests decline suggests a structural transformation of ecosystems. Furthermore, the simultaneous expansion of construction and unused land, coupled with the reduction in ecological land (grassland and forests), forms a self-reinforcing trend that may accelerate the deterioration of regional ecosystem services.