esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The rapid increase of train speed has brought greater challenges to the safety and reliability of railway systems. Therefore, it is necessary to monitor the operation status of trains, infrastructure, and their operating environment in real time. Because the operation environment of railway systems is complex, the construction cost of wired monitoring systems is high, and it is difficult to achieve full coverage in the operation area of harsh environments, so wireless sensor networks are suitable for the status monitoring of railway systems. Energy resources of nodes are the basis of ensuring the lifecycle of wireless sensor networks, but severely restrict the sustainability of wireless sensor networks. A construction method of special wireless sensor networks for railway status monitoring, and an optimal energy resources allocation method of wireless sensor networks for intelligent railway systems are proposed in this paper. Through cluster head selection and rotating probability model, clustering generation and optimization model, and partial coverage model, the energy consumption of nodes can be minimized and balanced. The result of simulation experiment proved that the lifetime of wireless sensor networks can be maximized by the optimal energy resources allocation method based on clustering optimization and partial coverage model, based on polynomial time algorithm.  
wireless sensor network; energy resources allocation; intelligent railway system; clustering optimization; partial coverage +
Footnote †: journal:  
_Article_  
Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 2 Review of Empirical Literature
[72] using a national dataset of 7987 rural households in Afghanistan found that mobile phone usage and mobile phone promotion policy reduced inorganic fertilizer application among the farmers, helping to ensure environmental and agricultural sustainability goals. In rural Viet Nam, [PERSON] & [PERSON] [73] found through a panel dataset (from 2008 to 2012) that internet access is associated with a 6.8% increase in total agricultural output as a result of more efficient use of fertilizer, particularly among younger households and in the less developed northern provinces of Viet Nam. [PERSON] et al. [45] established that internet use meaningfully increased rural households' income and expenditure in China. [PERSON] et al. [46] found that using the internet positively impacts the technical efficiency of farmers producing banana in China. [PERSON] [42] showed that mobile phones significantly impacted on post-harvest losses, profit, and price volatility among fishermen in Kerala in India. In summary, we observed a paucity of information using cross-country data covering sub-Saharan African countries that illuminate the impact of information and communication technologies on agricultural production. Rather, silos of studies that use country-specific cases dominate. There is, therefore, the need to have more cross-country studies to bridge the unbalanced literature, particularly with policy implications for the global south. Studies on information and communication technologies have overly concentrated on mobile phones and internet, with limited consideration of the vast array of ICTs (radio, television, computers, smartphones, and tablets). Conspicuously missing in most of the literature is the consideration of variables such as net national income per capita; credit to the private sector; rural population; and its impact on agricultural production (crop and livestock production) using rigorous estimation approaches such as the panel autoregressive distributed lag model (ARDL). Our study seeks to fill the lacuna in the literature.
## 4 Structural Analysis
### Validation of the Model's Accuracy by Linear Elastic Analysis  
Figure 6 shows the deflection results of instrumenting the bridge compared to the FEM model under the linear elastic domain. The validation coefficient (\(\mathfrak{n}\)) was determined from the deformation or deflection obtained from the model. The residuals and coefficients present the characteristics and agreement of the model. It further details the relationship of compressive and tension members, that is, the measurement from the top and bottom chord. Dissimilar characteristics of the model versus measurement approach and anomalous validation coefficients [48] disclose a poor connection between structural members and lower structural stiffness, prompting extensive evaluation [6; 48]. The mean of the measured results is presented in Table 3 with their respective residuals and validation coefficients.  
The mean deflection from transducers R1, R2, R3, R4, L1, and L2 recorded deflections ranging from 0.0035-0.0488 from Loading cases 1 to 3. On the other hand, the image processing approach recorded a mean ranging from 0.0079-0.0492 from Loading cases 1 to 3, a magnitude higher than the predecessor. The residuals had similar characteristics increasing from Loading cases 1-3. However, it was observed that compressive members had higher residuals than the corresponding tension members--the transducer measurements [2]. Therefore, the validation coefficient established the reliability of the model and structure, indicating a consistent ratio from both the transducer and image processing approach. A higher ratio was realized on the image processing approach on Loading case 1, 2.79. The ratio was peradventure informed by characteristic inaccuracies of image processing on small deformations. The coefficients show that the bridge is intact and close to the FEM model. The mean coefficient ratios of 1.45 and 1.94 for the transducer and image processing, respectively, indicate the model approximately fulfills the design requirements and operates within the elastic region. It is assumed the model idealized the in-situ bridge structure, paving the way for nonlinear analysis.
## 4 Experiment and Result Analysis
### Discussion
#### 4.7.3 Influence of Network Instability on the Proposed Method  
The mutual communication between UAVs is the basis for task-assignment negotiation and coordination during task execution. Due to the high-speed maneuvering of UAVs in 3D space, the topology of the flying ad hoc network (FANET) [22] changes rapidly, which may cause communication interruption, delay increase, and other problems. These problems may further make the negotiation period of task assignment longer, or even cause conflicts between the assigned plans due to the interruption of communication, and ultimately make the overall task assignment result worse. Therefore, the next step is to evaluate the impact of network instability on the method and study the corresponding strategies to improve its robustness, which can also avoid making this problem a vulnerable point to attack by the enemy.  
Since the cooperation of UAVs in the proposed method mainly occurs between UAVs within 2 hops, the route-maintenance strategy of FANET can pay more attention to the optimization of short-distance routes to improve the QoS, which can not only improve the speed of distributed task allocation in this paper, but also help avoid collisions between UAVs and so on.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 4 Mathematical Models for Energy-Aware Routing Calculation  
The DCN topologies presented in Section 2.1 exhibit significant redundancy, leading to excessive power consumption in periods of reduced traffic. In such a case, the flows may be routed over a limited set of paths engaging only some of the links and switches--see Figure 2a. One may switch unused equipment into low-power standby mode to save energy. However, further savings may be attained by harmonizing energy-aware functions globally, especially incorporating them into routing. Typically for DCN networks, there are many equal cost paths. Standard routing algorithms use path length metrics and tend to spread the load evenly, as shown in Figure 2a. Energy-aware routing should behave differently thanks to incorporating power consumption into a given metric--it consolidates paths on a minimal subset of nodes and links, as shown in Figure 2b.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Network Topology (Structure)  
Another way to group routing protocols is to classify them based on the network structure (topology). The protocols included in this category are further divided into five broad subcategories according to their functionalities, which are: flat, hierarchical, mobility or heterogeneity-based and geo-routing protocols. We detail each of these categories below.
# Parallel Compact Differential Evolution for Optimization Applied to Image Segmentation
###### Abstract  
A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented after predefined iterations. Two communication strategies are proposed in this paper. The first one is to replace the local optimal solution by global optimal solution in all groups, which is called optimal elite strategy (oe); the second one is to replace the local optimal solution by mean value of the local optimal solution in all groups, which is called mean elite strategy (me). Considering that the pcDE algorithm does not need to store a large number of solutions, the algorithm can adapt to the environment with weak computing power. In order to prove the feasibility of pcDE, several groups of comparative experiments are carried out. Simulation results based on the 25 test functions demonstrate the efficacy of the proposed two communication strategies for the pcDE. Finally, the proposed pcDE is applied to image segmentation and experimental results also demonstrate the superior quality of the pcDE compared with some existing methods.  
Keywords:parallel compact differential evolution (pcDE); compact differential evolution (cDE); image segmentation; communication strategies +
Footnote †: journal: Applied
## 3 Materials and Methods
### Data Preprocessing  
The NUPT-FPV dataset [46] was selected due to its concurrent acquisition of FP and FV images. FP offers high discriminative power but is sensitive to surface conditions, while FV provides stable internal features that are more resistant to spoofing and noise. The main characteristics and acquisition protocol of the NUPT-FPV dataset are summarized in Table 5. This dataset supports robust evaluation due to its large size, variety of subjects, and diverse acquisition conditions [46].  
Regarding class distribution (number of images per user), most users provided either 60 images (18 users) or 80 images (12 users). The first half of users (IDs 1-56) averaged 55 images each, while the second half (IDs 57-140) averaged 93 images. The preprocessing involved converting the images to grayscale, resizing them to 224 \(\times\) 224 pixels, applying Min-Max normalization.
# Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
## 2 GP based Surrogate Model
### Aleatoric Uncertainty GP  
A GP is used to predict a manufacturing process related aleatoric uncertainty \(\sigma_{al}=\sigma_{al}(x_{m},x_{p},\bar{\Sigma}_{al}(Z))\) of the manufactured part. Aleatoric uncertainty data are generated by uncertainty propagation analysis with FEM simulation. The inputs are the setting parameters from a real physical manufacturing process \(x_{m}\), properties of the part to be manufactured \(x_{p}\) and aleatoric manufacturing process uncertainty \(\bar{\Sigma}_{al}(Z)\) obtained from, e.g., sensor data of the real physical manufacturing process, see Figure 2. Here, \(Z\) describes a characteristic of the manufacturing process, e.g., the velocity profile of a forming tool. The output \(\sigma_{al}\) is predicted by a GP regression, such that \(\sigma_{al}\sim\mathcal{GP}(m(x_{m},x_{p},\bar{\Sigma}_{al}(Z)),k((x_{m},x_{p },\bar{\Sigma}_{al}(Z)),\\ (x_{m},x_{p},\bar{\Sigma}_{al}(Z))^{\prime}))\) with mean \(m(x_{m},x_{p},\bar{\Sigma}_{al}(Z))\) and covariance function \(k((x_{m},x_{p},\bar{\Sigma}_{al}(Z)),\\ (x_{m},x_{p},\bar{\Sigma}_{al}(Z))^{\prime})\).  
Of course, a wide variety of manufacturing process characteristics can be implemented, e.g., rolling speeds, cutting forces, heating times etc. As a running example, we choose as a manufacturing process hot metal forming on a friction screwpress, where \(x_{m}\) contains different input features which control the forging aggregate (clutch pressure between flywheels and rotation speed of the electric motor), \(x_{p}\) describes the part to be forged by different dimensions and part temperature and \(Z\) is a resulting velocity profile of the forging tool over time for a given input \(x_{m}\), where \(\bar{\Sigma}_{al}(Z)\) represents aggregated aleatoric deviations in respect to forging velocity. \(\sigma_{al}\) then describes the deviations of the final forged part, i.e., deviations from important final part geometries. All relevant details of our running example can be found in Section 4.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 7 The Future of the Spaceborne LiDARs
### Future Missions
#### 7.1.4 Gualan  
The Guanalan Science Mission is a Chinese initiative that aims to advance space oceanography. It will combine two key technologies: interferometric altimetry (IA) and ocean LiDAR (OL). Using dual-frequency (Ku and Ka bands), the IA will provide high-resolution ocean surface topography measurements. The OL, the first spaceborne active LiDAR designed for oceanography, will penetrate deeper into the ocean to profile optical properties and detect marine life in the euphotic layer. This mission promises to improve our understanding of ocean dynamics, bio-optical properties, and marine ecosystems, ultimately contributing to global ocean observation efforts [174].
## 5 Iron, ROS and Iron Chelators That Can Cause DNA Damage in Cancer
### Iron Chelators and ROS
#### 5.1.4 Di-2-Pyridineketone Hydrazone Dithiocarbamate (DpdtC)  
The iron chelator Di-2-pyridine ketone hydrazone dithiocarbamate (DpdtC) was designed originally as an analogue of Dithiocarbamates. It was explicitly intended for iron chelation and thus had a strong chelating ability for metals. It was suggested that it chelates labile iron forming a redox-active complex that undergoes Fenton chemistry. DpdtC induced cell cycle arrest and DNA fragmentation, resulting in autophagy and ROS-mediated apoptosis in HepG2 cells [200]. A second study was then performed, studying the mechanisms of how this iron chelator exerted its anti-cancerous effect through the induction of ROS. They discovered that it stemmed from catalase inhibition by causing a conformational change in catalase [201]. This design shows that when considering the design of iron chelators, attention needs to be paid to their steric effects as they can also have an allosteric or physical effect. Later, this iron chelator exhibited that it could upregulate NRDG-1 by depleting the intracellular iron content, which was shown explicitly in HER-2 overexpressed cancer [202]. Later, it was demonstrated that ferritophagy, the process of ferritin degradation in the lysosomes, leads to ROS generation and lipid peroxidase [176]. DpdtC also inhibits the cell signalling pathway EGFR/AKT and induces apoptosis in KYSE-150 and KYSE-450 esophageal cancer cells [203].
## 2 Revisit the URUP Demonstration Project at Nanjing
### On-Site Monitoring of Segment Internal Force  
In tunneling practice, grouting pressure, as well as soil surcharge, has significant influence on segment stability [40,41]. During the URUP tunneling process in negative and shallow overburden, the internal force of the lining segment is non-uniformly distributed and subjected to load variation. Therefore, a comprehensive field monitoring program was performed on the segments to record the in-situ internal force of the URUP tunnel lining.  
Multiple rebar stress meters (RSMs) are used for measuring the stress variation of segment reinforcing bars, as sketched in Figure 4. For each measuring section, nine measuring points are employed (G1-G9) with each point consisting of four pre-welded RSMs on the rebars (as shown in Figure 4c). The positions of G1-G9 at the 27 th, 39 th, 83 th rings, 28 th, 40 th rings, and 84 th ring (west line) are shown in Figure 4.  
Figure 4: Rebar stress meters (RSMs) arrangement: (**a**) measurement point arrangement; (**b**) arrangement of each segment; and (**c**) welded RSMs prior to segment cast.  
The rebar stress in the segment can be expressed in Equation (1).  
\[\sigma_{s}=\sigma_{f}-\sigma_{0} \tag{1}\]  
where \(\sigma_{0}\) is the initial stress, \(\sigma_{f}\) is the monitored stress, \(\sigma_{s}\) is the rebar stress of the segment after being assembled.
## 4 Results and Discussions
### Effect of Slider Geometry under Different Submergence Conditions
#### 4.3.4 Effect of Slider Geometry  
Six different \(R\) were calculated for each initial submergence condition, for a total of 18 cases. The change of maximum wave height with \(R\) is shown in Figure 19. The maximum wave heights measured at G\({}_{1}\) are marked as the solid line in Figure 19 and that at G\({}_{2}\) are marked as a dotted line. The triangle, square and circle, respectively, represent the subaerial landslide, partial subaerial landslide and submarine landslide in Figure 19. It can be seen that the wave height of the subaerial landslide (h = 0 m) is the largest, followed by the partial subaerial landslide (h = \(-\)0.2 m), and that of the submarine landslide (h = \(-\)0.4 m) is the smallest, which is consistent with the conclusion proven in Figure 9. As the ratio increases, the maximum wave heights all decrease at the three different initial submerged states at G\({}_{1}\) and G\({}_{2}\), which indicates that the total wave energy decreases as the ratio increases.
## 3 Results and Discussion
### Biochar and Biofetilizer Characteristics
Due to the high water content of npopal, npopal-based biofertilizers' nutrient content is lower than that of organic fertilizers prepared from animal wastes [63] or other plants by composting [64]. Besides nutrients, biofertilizer provides beneficial microorganisms that improve the use of nutrients. In this \begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Value** \\ \hline pH & 8.2 \(\pm\) 0.09 \\ EC (\(\mu\)S\(\cdot\)m\({}^{-1}\)) & 1021 \(\pm\) 12 \\ CEC (cmol\({}^{+}\)\(\cdot\)kg\({}^{-1}\)) & 37 \(\pm\) 3.1 \\ P\({}_{\text{av}}\) (mg\(\cdot\)kg\({}^{-1}\)) & 250 \(\pm\) 8.4 \\ N\({}_{\text{T}}\) (mg\(\cdot\)kg\({}^{-1}\)) & 3500 \(\pm\) 0.005 \\ C\({}_{\text{org}}\) (\%) & 67 \(\pm\) 2.7 \\ C\({}_{\text{tong}}\) (\%) & 1.24 \(\pm\) 0.003 \\ HC\({}_{\text{org}}\) (molar ratio) & 0.38 \(\pm\) 0.001 \\ C:N & 191 \(\pm\) 10 \\ WHC (\%) & 61 \(\pm\) 4.7 \\ BD (g\(\cdot\)cm\({}^{-3}\)) & 0.20 \(\pm\) 0.003 \\ Particle-size & 33.5\% \textless{} 0.6 mm \\ \hline \hline \end{tabular}
\begin{tabular}{c} EC: electric conductivity, CEC: cation exchange capacity, P\({}_{\text{av}}\): available phosphorus, C\({}_{\text{org}}\): organic carbon, C\({}_{\text{tong}}\): inorganic carbon, N\({}_{\text{T}}\): total nitrogen, WHC: water-holding capacity, BD: bulk density. \\ \end{tabular}
\end{table} 
Table 1: Characteristics of biochar added to soil. case, the biofertilized presented \(5\times 10^{4}\) CFU\(\cdot\)mL\({}^{-1}\) of N-fixing microorganisms, and a high mesophilic aerobes population (10.2 \(\times\) 10\({}^{7}\) CFU\(\cdot\)mL\({}^{-1}\)), while no fecal coliforms, _Salmonella_, _E. coli_, or total coliforms were found, which indicates the biofertilizer is microbiologically safe for foliar or soil application.
## 3 Methodology Research
### Random Forest (RF)  
RF is an ensemble learning algorithm proposed by [PERSON] [70] in 2001, based on decision trees and bagging. It addresses the weak generalization capability of decision trees and significantly improves their accuracy [71]. A large body of research indicates that the RF model exhibits good tolerance to outliers and noise in datasets, making it widely regarded as one of the best machine-learning models currently available [31; 72; 73]. RF is a type of ensemble classification model composed of multiple decision trees. In the RF model, each decision tree votes to select the optimal classification result, given the independent variable X [74]. In the RF model, both the training samples for each tree and the attributes used for node splitting are randomly selected. This dual randomness helps to some extent in preventing the overfitting of the model. The RF algorithm is capable of handling high-dimensional data without the need for dimensionality reduction or feature selection. It exhibits a fast training speed, is less prone to overfitting, and is insensitive to missing values. Even when a significant portion of the features is missing, it can maintain accuracy. However, for some classification or regression problems with considerable noise, overfitting may still occur [75].  
The generalization error of a random forest model typically decreases as the number of decision trees increases. This also implies that increasing the number of decision trees in a random forest model can enhance the accuracy of model predictions. This is because, with the increase in the number of decision trees, a random forest model can better utilize training data information and reduce the occurrence of model overfitting [32]. [PERSON] et al. [76] utilized a random forest model for landslide susceptibility assessment in the Piedmont region of Italy. The results showed that the random forest model exhibits good applicability for landslide susceptibility assessment in large, heterogeneous areas. One advantage of the random forest model is that it produces fewer errors when dealing with large sample sizes and multiple evaluation factors, and it is less prone to overfitting [77]. It can handle large datasets with high accuracy without the need to reduce high-dimensional feature input samples. Additionally, this model can be used to assess the importance of individual features in classification problems [78]. Its principle is based on the Gini index, whereby the random forest algorithm calculates the Gini index for each evaluation factor and compares it by taking the average of the Gini indices for all evaluation factors to determine their importance.
# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 5 Simulation Results and Discussions
### NDN Network and TCP/IP Network without Acceleration Strategy  
In this section, we deploy the Bitcoin system in the NDN network and the TCP/IP network, and the two solutions use the same blockchain network topology. In this topology, there are 150 nodes, 20 of which are miner nodes, and 15 of which are wallet nodes, the remaining nodes are forwarding nodes. These nodes are connected to each other with probability \(P\) and perform data interaction.  
As shown in Figure 4, we compared the transmission delay of the Bitcoin system in the NDN network and the traditional TCP/IP network. The delay/ms represents the transmission delay when a block is broadcast to a miner node. The path represents the number of miners passed by the block. It can be seen that under the same network conditions, the NDN network has better performance than the traditional TCP/IP network, which confirms the view that the NDN network is more suitable for the blockchain network. However, simply deploying the blockchain on the NDN network will not bring a more significant performance improvement, so we need to further optimize the NDN network, which will be given in the next section.  
Figure 4: Transmission delay in NDN network and TCP/IP network.