esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
###### Abstract  
Air quality level has a complex nonlinear relationship with air pollutant and meteorological conditions, including multiple factors, overlapping information, and difficulty solving equations. In order to identify significant factors, remove correlations, reduce data dimensionality, and simplify the model structure, a BP neural network model for air quality level prediction optimized by stepwise discriminant analysis (STEPDISC) and principal component analysis (PCA) is proposed with 12 factors of historical daily meteorology and air pollutants in Bayannur city as samples. The results showed that, at the significance level of 0.01, the STEPDISC method retained 9 significant impact factors. The PCA method made an orthogonal linear combination of the 9 factors to form the principal components, and the contribution of the top 5 principal components were 37.6%, 19.2%, 15.3%, 8.8%, and 7.7%. At a contribution threshold of 0.85, the top 5 principal component scores were used as input nodes to construct the STEPDISC-PCA-BP model, which had a prediction accuracy of 85.5%.Compared with the PCA-BP and BP model, which had a prediction accuracy of 61.8% and 56.7%, respectively, the STEPDISC-PCA-BP model has a higher prediction accuracy, shorter time, and lower complexity of structure and data dimensionality, and can provide the necessary technical support for the local air quality improvement.  
computer neural network; prediction of air quality level; stepwise discriminant method; principal component analysis; BP neural network
# Reverse Electrodialysis: Potential Reduction in Energy and Emissions of Desalination
## 2 Methods
### Estimation of GHG Emissions: Global Warming Potential (GWP)  
The Greenhouse gas (GHG) emissions savings were quantified using the 100-year Global Warming Potential (GWP\({}_{100}\)) metric as defined in the Intergovernmental Panel on Climate Change (IPCC) Model, 100-year time horizon, Fifth Assessment Report, 2013.  
The GWP\({}_{100}\) in units of mass of carbon dioxide equivalents per cubic meter of desalted water (computed with Equation (A4)) was estimated assuming RED supplies to the SWRO plant (i) the maximum attainable energy--i.e., the difference between the salinity gradient exergy--as defined by Equation (A1), entering and leaving the RED plant (Retrieved Exergy); (ii) the actual energy output of the RED system (Net Specific Energy, computed with the RED system's model). The aforementioned scenarios were compared to the business-as-usual (BaU) scenario where the local grid mix fulfils the energy requirements of the SWRO plant.  
The local grid mix's GWP\({}_{100}\) indicator per unit of electricity produced was retrieved from the ecoinvent database 3.6 [65] (dataset: market for electricity, medium voltage, Appendix B). The GWP\({}_{100}\) of the industrial-scale RED stack was estimated based on the [PERSON] et al. [66] life cycle assessment (LCA) of a RED unit equal to that of our assessment, normalized to the energy yield (net specific energy or specific salinity gradient exergy retrieved for conversion) over the lifetime of the RED plant (20 years) in each location and energy supply scenario (Appendix B).
###### Abstract  
Named Data Networking (NDN) has been recognized as the most promising information-centric networking architecture that fits the application model of IoT systems. In-network caching is one of NDN's most fundamental features for improving data availability and diversity and reducing the content retrieval delay and network traffic load. Several caching decision algorithms have been proposed; however, retrieving and delivering data content with minimal resource usage, reduced communication overhead, and a short retrieval time remains a great challenge. In this article, we propose an efficient popularity and freshness caching approach named PF-ClusterCache that efficiently aggregates the storage of different nodes within a given cluster as global shareable storage so that zero redundancy be obtained in any cluster of nodes. This increases the storage capacity for caching with no additional storage resource. PF-ClusterCache ensures that only the newest, most frequent data content is cached, and caching is only performed at the edge of the network, resulting in a wide diversity of cached data content across the entire network and much better overall performance. In-depth simulations using the ndnSIM simulator are performed using a large transit stub topology and various networking scenarios. The results show the effectiveness of PF-ClusterCache in sharing and controlling the local global storage, and in accounting for the popularity and freshness of data content. PF-ClusterCache clearly outperforms the benchmark caching schemes considered, especially in terms of the significantly greater server access reduction and much lower content retrieval time, while efficiently conserving network resources.  
NDN; IoT; in-network caching; data freshness; data popularity; clustering
## 4 Technology Challenges and Future Prospects  
The process of remediating heavy metals presents a significant challenge that requires a considerable amount of time and effort [155]. Furthermore, one of the primary concerns is the successful implementation of microbial-based heavy metal removal techniques at a commercial level, specifically in water treatment plants. There exist diverse strategies that can be employed to enhance the removal of heavy metals from aqueous media. These include the manipulation of the immobilization of bacterial strains onto solid substrates and the utilization of composite nanomaterials or biopolymers. Moreover, genetically engineered microorganisms have better heavy metal removal potential in comparison to wild-type microorganisms. It is better to scale up the implementation of these microbes into wastewater treatment plants. It is noteworthy to mention that biological remediation methods have the potential to eliminate other toxic metal ions, including Ni, V, Hg, and Ti, which can be further explored in future research. Moreover, there is a need to implement CRISPR-mediated gene expression in the microbes and plants used in the heavy metal remediation process and their scale-up for wastewater treatment plants. Growth media, optimum growth conditions, and culture maintenance are the major technological changes in microbial-based heavy metal removal.  
In addition, phytoremediation is a good method for the removal of heavy metals from contaminated water but there needs to be further research on the safe disposal of the used plant. In addition, advanced technical assessment tools such as the life cycle assessment (LCA) and techno-economic analysis of large-scale wastewater treatment plants can be beneficial in developing cost-effective, energy-saving, and eco-friendly systems in the future. Furthermore, it is of great value to contemplate the molecular mechanisms of microbes underlying the elimination of heavy metals and conduct a comparative transcriptomic analysis in prospective research agendas. As a result, it is imperative to persistently investigate and devise novel strategies to enhance the efficacy of heavy metal elimination from water, all the while taking into account the long-term viability and ecological implications of the remediation process.
## 4 Proposed Method
### Extraction  
This section goes details the extraction procedure. In semi-blind watermark extraction, partial data of significant data is required. The extraction procedure is provided in Figure 5.  
**Step 1:** Examine the image that is watermarked.  
**Step 2:** The color watermarked image is converted to red, green, and blue layers.  
**Step 3:** As the watermark is embedded in the blue component, the same is considered for extraction.  
**Step 4:** The blue component is applied with one-level DWT with a Haar wavelet.  
Figure 5: The proposed scheme: extraction process.  
**Step 5:** The LL subband is considered, and SVD is applied to generate a singular value matrix.  
**Step 6:** A scrambled QR watermark is extracted based on the key values and partial data of the significant image.  
**Step 7:** Inverse scrambling is applied to the extracted watermark using the CLM algorithm.  
**Step 8:** The watermark is extracted with the decrypted message using the public key, which contains the private key values to verify the watermarked data.
# Assessment of a Substandard Reinforced Concrete Frame's Beam-Column Joint Using Shake Table Testing
###### Abstract  
This study investigates the seismic performance of substandard reinforced concrete (RC) frames, particularly emphasizing the behavior of beam-column joints through comprehensive shake table testing. Historical evidence indicates that RC structures erected prior to the 1970s frequently exhibit critical deficiencies in shear reinforcement, significantly undermining their seismic resilience. The primary objective of this research is to experimentally quantify the shear capacity, deformability, and failure mechanisms of beam-column joints within substandard RC frames. Shake table experiments were conducted on a meticulously scaled (one-third scale), single-story, single-bay RC frame, representative of construction practices typical of 1980s Turkiye. The input seismic loading was derived and scaled from the recorded ground motions of the 2023 Kahramanmaras earthquake to ensure realistic seismic demand conditions. Experimental outcomes revealed a maximum lateral displacement of 53.1 mm, corresponding to a story drift ratio of approximately 0.055 radians and a computed damage index of 0.758, indicative of near-collapse performance levels. Notable damage observations included extensive shear cracking and concrete spalling at beam-column interfaces, accompanied by pronounced pinching effects during cyclic loading. The findings emphasize an urgent need for targeted seismic retrofit solutions, specifically addressing shear vulnerabilities in beam-column joints. Furthermore, the results carry substantial implications for revising current seismic design codes and enhancing the earthquake resilience of existing RC infrastructure in seismic-prone regions.  
Keywords:shake table testing; substandard RC frames; seismic resilience; damage index +
Footnote †: journal: Applied Sciences
## 1 Introduction
Since multiple CAVs are required for cooperative driving, it is natural to adopt a multi-agent reinforcement learning framework to achieve the cooperative goal. The multi-agent proximal policy optimization (MAPPO) in a cooperative setting [13], which had a surprising effectiveness, was used as the benchmark algorithm in this paper. The original algorithm directly took the information from the agent's neighbors as its own observations. As the number of agents increased, learning became more difficult [14]. Therefore, an attention representation was utilized to select the most relevant information from the vehicle's neighbors. In the context of the cooperative multi-agent setting, the reward assignment problem concentrated on how to assign a global return to each agent that accurately reflected the agent's contribution to the overall behavior. We noted that the distance between a vehicle and the intersection had different effects on the safe operation of the system, and thus proposed a weight reward assignment scheme. Therefore, to address the above problems in this paper, a decentralized MAPPO based on the attention representations (Attn-MAPPO) is proposed to make joint decisions at the intersection based on the trajectory prediction of all the vehicles. The contributions of this paper are fourfold. First, decision-making at the intersection where the CAVs and HDVs coexist was formulated as a decentralized MARL problem. Based on the traffic rules and the most valuable neighbor information extracted by an attention module, the Attn-MAPPO algorithm was developed to allow vehicles to cross the intersection safely and effectively. Secondly, a weighted reward assignment scheme was proposed. According to the position of the vehicles, the contribution of each CAV could be measured to enhance the cooperation between the vehicles. Thirdly, an effective reward function was designed. Due to the uncertain behavior of the HDVs, all the vehicle's trajectories at every time step were predicted in a forward predictive time horizon, which reflected precisely how well the action was taken. Fourthly, we conducted the comparative experiment about the traditional heuristic rule-based and our proposed approaches, and the results showed that our proposed approach was more adaptive and generalized in a complex traffic environment. The remainder of the paper is organized as follows. Section 2 briefly reviews several approaches to settle the decision-making problem at an intersection. Section 3 states some of the problems, including the research scenario, the right of way the rules, and the vehicle intersection model. The problem formulation and the proposed MARL framework are described in Section 4. The experiments, results, and discussions are presented in Section 5. The paper is concluded, and the future works are discussed in Section 6.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 4 Laser-Induced Breakdown Spectrometry (LIBS) for Geochemical and Geometallurgical Characterization  
Laser-induced breakdown spectrometry (LIBS) is a flexible method for studying rock composition [24,25]. In this process, a laser pulse is used to bombard the material being studied, which ablates a small amount of the material and turns it into a plasma emitting light at wavelengths characteristic of the material's composition. Some of the laser pulse's energy is transformed from electromagnetic energy (light from the laser source) to mechanical energy, creating a distinct sound [26]. LIBS essentially provides information on the chemistry at the laser-excited target point. As rocks often contain more than one mineral, methods such as multiparameter correlation statistics or machine learning algorithms may be necessary to quantitatively estimate mineral contents [27]. Recent studies have shown that the laser ablation sound energy is correlated with the Vickers hardness of minerals, indicating that LIBS sound signals could be used to estimate mechanical properties of a rock mass [28].
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 3 Background
### Nonlinear Velocity Obstacle  
For a disc-shaped robot \(R_{a}\) and moving obstacle \(O_{b}\) of radii \(r_{a}\) and \(r_{b}\), respectively, the nonlinear velocity obstacle (NLVO) induced for \(R_{a}\) is a set of its velocities that will result in its collision with \(O_{b}\) at some time in the future. NLVO is defined in terms of its temporary components. Let at current time \(t=0\), the center of the robot represented by \(p_{a}(0)\) is at origin. In a robot configuration space, let set \(B\) represent the obstacle's circular geometry with radius equal to the sum of radii \(r_{a}\) and \(r_{b}\), and the robot geometry is represented by a point, as shown in Figure 1a. For an obstacle following a generalized trajectory \(c(t)\), the temporary velocity obstacle induced for a robot due to obstacle's position at time instant \(t_{i+n}\) is as follows:  
\[NLVO(t_{i+n})=\frac{c(t_{i+n})\oplus B}{t_{i+n}} \tag{1}\]  
where \(c(t_{i+n})\oplus B\) denotes the Minkowski sum of vector \(c(t_{i+n})\) and set \(B\). \(NLVO(t_{i+n})\) is a set of all absolute velocities of a robot that will result in a collision of \(p_{a}\) with a point in \(B\) at time instant \(t_{i+n}\). The set of robot velocities that will result in collision of \(p_{a}\) with \(B\) within time horizon \([0,\tau]\) can be defined in terms of \(NLVO(t_{i+n})\) as follows:  
\[NLVO^{\tau}=\bigcup_{0<t\leq\tau}NLVO(t) \tag{2}\]  
Geometrically, \(NLVO^{\tau}\) is a warped cone, such as the one shown in Figure 1b.  
Figure 1: For a robot with current position \(p_{a}(0)\) at origin and obstacle following trajectory \(c(t)\), (**a**) shows the temporary velocity obstacle, \(NLVO(t_{i+n})\) induced for a robot due to the position of an obstacle at time \(t_{i+n}\). It is a disc of radius \((r_{a}+r_{b})/t_{i+n}\) and its center is at \(c(t_{i+n})/t_{i+n}\) in the robot velocity space. If the velocity of a robot is such that \(v_{a}\in NLVO(t_{i+n})\), then \(p_{a}(t_{i+n})\in c(t_{i+n})\oplus B\). In (**b**), \(NLVO^{\tau}\) is shown as the union of its temporary components over time horizon \([0,\tau]\). If a robot velocity \(v_{a}\) is in \(NLVO^{\tau}\), then the collision will occur within time horizon \([0,\tau]\). NLVO: Nonlinear Velocity Obstacle.  
Collision avoidance is then achieved as follow: Robot \(R_{a}\) selects its new velocity at time \(t=0\) such that \(v_{a}\
otin NLVO^{\tau}\) to remain safe for at least \(\tau\) seconds into the future. The new velocity is usually selected that minimize the Euclidean distance to velocity \(v_{pref}\), which in turn points to next sub goal extracted from global waypoint plan. \(v_{a}\) is then applied for short time horizon until a next control loop begins.
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 2 Materials and Methods
### Methods
#### 2.3.4 Coupling Mechanism of Markov Chain and the PLUS Model  
Although the Markov chain can effectively reflect the temporal changes in LULC, it cannot effectively characterize the spatial attributes of the data, nor can it display the spatial change distribution of these elements. Therefore, the method based on the coupling of the Markov chain and the PLUS model can be used to simulate and predict the temporal and spatial distribution of future LULC in the study area. The coupling mechanism is shown in Figure 4. The total amounts of different LULC types in Rwanda in different years (2015, 2020, 2030, 2040, and 2050) were forecasted. Then, the total LULC data for different years were input into the PLUS model, and the spatial distribution of different LULC types in Rwanda in each year was simulated using 2010 as the starting point of the simulation. The simulation results for 2015 were used to verify the model.  
Figure 3: Factors driving LULC change: (**a–d**) soil factors; (**e–h**) locational factors; (**i**) precipitation; (**j**) nature reserve and water area; (**k**) population; (**l**) topographic factor. Note: All soil factors presented are from 2015 (**a–d**). The road data are from 2017 (**e,f**). The locations of major cities are from 2008 (**g**). The precipitation map and population density map are from 2015 (**i,k**).
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 5 Discussion  
### Technical Challenges  
#### 5.1.1 Data Processing  
* Synchronization Issues in Multimodal Data Multimodal data fusion necessitates the synchronization of data acquired from various sensors, including speech, gestures, and facial expressions. However, variations in sampling rates and timestamps among these data sources present significant synchronization challenges. For instance, speech signals typically have a higher sampling rate compared to video frames, requiring precise temporal alignment during data fusion [62; 63]. Additionally, hardware-induced latency and network transmission delays further impact synchronization accuracy, complicating real-time data integration [64].
* High Computational Resource Demands and Limited Compatibility with Low-End Devices Processing multimodal data requires complex algorithms and deep neural networks, which demand high computational power. Real-time processing requires strong CPU and GPU performance, but many remote education devices, like personal computers and mobile devices, lack these resources. This limits the use of multimodal technology in low-resource settings. Developing lightweight and efficient algorithms can improve accessibility [65; 66; 67].
* Data Storage and Transmission Overhead In remote education, multimodal interaction technology produces large amounts of high-dimensional data. Managing and transmitting these datasets is a major challenge for educational platforms and cloud computing systems. Remote education depends on cloud storage and real-time streaming. High bandwidth demands can weaken system stability, especially in areas with poor network infrastructure [68; 69].
## 1 Introduction
The DV-Hop algorithm is easily implemented, and the localization accuracy it attains mainly depends on the estimated accuracy of average distance per hop and hop-count value between nodes. As known, the calculation error of the average distance per hop and the estimation error of hop-count value are the two primary sources of the estimation distance error. On the basis of the abovementioned discussions, a wireless sensor node localization algorithm based on the improved DV-Hop and differential evolution (DE) algorithms is proposed in this paper, namely DEIDV-Hop. It can effectively reduce the localization error of nodes without increasing network traffic and hardware, and consists of three steps: the first two steps estimate the distance between unknown nodes and anchor nodes throughout information such as the average distance per hop and hop-count value, and the third step uses DE to determine the location of unknown nodes. The main contributions of this paper include:  
* The equation formula for calculating the average distance per hop of anchor nodes is improved. The minimum mean square error criterion more excellent to calculate the average distance per hop, and the average distance per hop of anchor nodes is obtained by applying its average value. * DE algorithm has the defects of search stagnation and premature convergence; this improves the mutation formula and introduces random variation. In the crossover operation, the social learning part of the particle swarm optimization algorithm is introduced, and the heuristic information carried by the group optimal value is used to permit the current individual to learn from the group optimal individual, which is then intersected with the mutation vector. With the improvement of mutation operation and crossover operation of the basic DE algorithm, the diversity of the population is enhanced as well as information of the optimal individual in the population is effectively used to raise the efficiency of the algorithm. * To validate and show the effectiveness of the proposed algorithm, the proposed DEIDV-Hop algorithm is evaluated under four different network environments. In comparison with known algorithms, DV-Hop, PSODV-Hop [32], and GSODV-Hop [33], experimental results show that the proposed DEIDV-Hop algorithm has smaller localization errors and greater stability, yet more suitable for application scenarios with higher localization accuracy and stability requirements. The remainder of this paper is organized as follows. In Section 2, we summarize the related work, while in Section 3 we introduce the original DV-Hop and DE algorithms. The target algorithm DEIDV-Hop is presented in Section 4, then experimental results and performance evaluation are given in Sections 5 and 6. Finally, the concluding remarks and future work direction of this paper are provided in Section 7.