esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Glyphosate and Its Metabolite AMPA on Aquatic Organisms
## 3 AMPA (Aminomethylphosphonic Acid)
### Toxic Effects  
Although AMPA has been studied less than glyphosate, [PERSON] et al. (2017) pointed to affecting chlorophyll biosynthesis which leads to plant growth reduction. That means that AMPA can also be translocated to diverse plant tissue. AMPA is also known as a phytotoxin, which can amplify the indirect effects of glyphosate on physiological processes. On the other hand, due to its chemical similarity, AMPA can compete with glycine in biological sites and pathways, affecting chlorophyll biosynthesis and therefore the photosynthetic process as well ([PERSON] et al., 2017). Plants treated with AMPA showed a decreased glycine, serine, and glutamate ([PERSON] et al., 2017). According to published data, AMPA seems to be highly toxic on aquatic organisms (Table 7).  
### Toxic Effects  
Although AMPA has been studied less than glyphosate, [PERSON] et al. (2017) pointed to affecting chlorophyll biosynthesis which leads to plant growth reduction. That means that AMPA can also be translocated to diverse plant tissue. AMPA is also known as a phytotoxin, which can amplify the indirect effects of glyphosate on physiological processes. On the other hand, due to its chemical similarity, AMPA can compete with glycine in biological sites and pathways, affecting chlorophyll biosynthesis and therefore the photosynthetic process as well ([PERSON] et al., 2017). Plants treated with AMPA showed a decreased glycine, serine, and glutamate ([PERSON] et al., 2017). According to published data, AMPA seems to be highly toxic on aquatic organisms (Table 7).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Concentration** & **Exposure** & **Effects** & **References** \\ \hline \begin{tabular}{c} European eel \\ (_Anguilla Anguilla_) \\ \end{tabular} & 11.8, 23.6 \(\mu\)g/L & 1, 3 days & \(\uparrow\) CDI, FPG, EndoIII & [100] \\ \hline \begin{tabular}{c} Zebrafish \\ (_Danio rerio_) \\ \end{tabular} & 1.7, 5, 10, 23, 50, 100 mg/L & 24, 48, 72, 96 h & \begin{tabular}{c} Genotoxicity with LOEC 1.7 mg/L, induce \\ primary DNA lesions, \\ \end{tabular} & [53] \\ \cline{2-3} \cline{5-5} \
\begin{tabular}{c} Guppy \\ (_Pocilia reticulata_) \\ \end{tabular} & 82 mg/L & 96 h &
\begin{tabular}{c} Proliferation of the interlamellar epithelium, \\ fusion of secondary lamellae in gill, steatosis, \\ pyknotic nuclei in liver, degeneration of \\ hepatocytes \\ \end{tabular} & [50] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Toxic effects of AMPA on aquatic organisms.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 4 Theory and Method of Traffic Operation Status Evaluation
### Svd  
The Singular Value Decomposition (SVD) is an important matrix factorization method in linear algebra, which has important applications in signal processing, statistics, and other fields [19].  
In the establishment of the bridge traffic impact assessment model, the relative importance values of each evaluation factor are obtained through the Delphi method. In order to facilitate the calculation of the impact degree later, decoupling and normalization processing are needed, and the SVD method is used here [20].  
Firstly, construct the initial matrix. The data obtained from the Delphi method expert scoring are a set of relative importance values, which are a set of one-dimensional data. In order to apply the SVD algorithm, a two-dimensional matrix needs to be constructed. If the original data are \(b_{i}(i=1,2,\ldots,r)\) and \(r\) is the number of evaluation model factors, then the constructed matrix is \(A\in R^{r\times r}\). The construction method is as follows:  
1. \(a_{i}=1(i=1,2,\ldots,r)\). The diagonal elements of the matrix have values of 1;
2. If \(i\
eq j(i,j=1,2,\ldots,r)\), set \(k=i:1:j-1(i,j=1,2,\ldots,r)\), then \(a_{ij}=a_{ij}*b_{k}\);
3. If \(a_{ji}\
eq 0\), then \(a_{ij}=1/a_{ji}\).  
This constructs an \(r\times r\) matrix and also solves the problem of data coupling in the original one-dimensional array. By solving matrix \(A\in R^{r\times r}\), a set of independent data are obtained.  
Secondly, the singular value decomposition algorithm is applied to decompose matrix \(A\in R^{r\times r}\). In order to maintain the sum of the weights of each factor as 1, after performing singular value decomposition on the matrix, the results also need to be normalized. The implementation steps are as follows:  
1. Solve the eigenvectors \(pv\) and the eigenvalues \(po\) of the matrix;
2. Find the maximum eigenvalue and the eigenvector corresponding to the maximum eigenvalue;
3. Normalize the eigenvector.  
By following the steps above, the weight values of each evaluation factor in the evaluation model can be calculated. Since the model is implemented using the Analytic Hierarchy Process, this method has been used more than once in model evaluation, and will not be repeated here.
## 3 Surrogate Modeling and Multi-Objective Optimization
### Multi-Objective Optimization  
The two previously developed surrogate models, representing surface roughness and volumetric shrinkage in relation to process parameters, were used to formulate a multi-objective optimization problem aimed at identifying optimal process settings. This problem was defined to simultaneously minimize both surface roughness and product volume shrinkage, with process parameters constrained by the bounds listed in Table 3.  
The surrogate models were constructed using the Kriging method, selected for its proven capability in modeling complex, nonlinear relationships characteristic of injection molding processes. Unlike statistical approaches such as the ANOVA, which primarily quantify variance contributions of individual factors, Kriging produces a comprehensive approximation of the entire solution space, allowing for reliable predictions at untested parameter combinations. Moreover, it provides uncertainty quantification (e.g., confidence intervals), enhancing the reliability of decision-making--particularly when experimental data are limited.  
In Equation (3), the aggregate objective function F consists of two components, \(F_{1}\) and \(F_{2}\), corresponding to surface roughness and volumetric shrinkage, respectively. The parameter vector z includes the molding process parameters, as listed in Table 3.  
\[\begin{array}{ccc}\text{Minimize}&&F=(F_{1}(z),F_{2}(z))\\ \text{Such that}&&z_{\text{min}}\leq z\leq z_{\text{max}}\end{array} \tag{3}\]  
Optimization was performed using the pattern search algorithm, which is suitable for problems where derivative information is unavailable [67; 68]. The following settings were used: a mesh size tolerance of \(10^{-6}\); maximum iterations set to 100 \(N\), where \(N\) is the number of variables; an initial mesh size of 1.0; and mesh expansion and reduction factors of 2.0 and 0.5, respectively. The algorithm's adaptive mesh refinement strategy ensured convergence, terminating when the mesh size dropped below the specified tolerance.  
To evaluate the robustness of the obtained solutions, multiple optimization runs were initiated from different starting points across the feasible design space. These runs yielded highly consistent results, with variation below 5%, indicating reliable convergence toward the global Pareto front rather than local optima.  
A representative Pareto front is shown in Figure 11, illustrating the inherent trade-off between surface roughness and volumetric shrinkage. This offers valuable insight for decision-makers by presenting a spectrum of optimal parameter combinations, balancing competing performance objectives without the need for additional simulations.  
Three main points can be identified in this graph (listed in Table 7). The first point, P1, is the best choice for minimizing volumetric shrinkage, but the surface roughness will be at its maximum value. The opposite is true for the third point, P3. Meanwhile, the second point, P2, is the best compromise between the two functions.  
Figure 11: A plot of the Pareto front obtained from optimization.
# Service Interface Translation. An Interoperability Approach
## 2 Related Work
### Semantic Platforms and Ontologies  
Semantic compatibility between heterogeneous systems is seen as an effective approach to addressing interoperability issues. This is intended to provide a uniform and agnostic representation of service entities and their relationships, which can be achieved via a common data model to semantically describe entities. In addition to the use of existing technologies from the semantic web (OWL, OWL-S, SPARQL), promising new approaches can be found in the literature. [PERSON] et al. [5] proposed a semantic interoperability architecture for smart spaces that details an ontology model and interoperability interactions between smart objects.  
Due to the heterogeneity of the data produced by IoT, several ontologies have been created to address interoperability issues among sensor data. Some well-known examples are the semantic sensor network (SSN) ontology [6] and the sensor, observation, sample, and actuator (SOSA) ontology [7]. A wide number of ontologies have been developed in recent years; however, ontologies ultimately simply move the interoperability mismatch to a higher level. Applications with different ontologies cannot understand each other.  
Other approaches include the use of ontology alignment to avoid the need for a common ontology [8]. These approaches include mediation engines that use semantics to support relationships among services [9]. Such approaches are required to be fully consistent and maintained over time to provide a mechanism that truly relates concepts and vocabularies from different ontologies. Unfortunately, according to [PERSON] et al. [10], these alignment tools are no longer maintained after 1 or 2 years. Moreover, the use of ontologies implies confidence in the interpretation of semantic meaning. Automated ontology alignment still provides unsatisfactory results: only 40% accuracy is obtained for multilanguage matching and even lower values (approximately 20%) for complex matching, according to the studies of [PERSON] et al. [11].
## 4 Results
### Performance Evaluation Metrics
#### 4.1.6 Average Packet Delivery Ratio (\(\mathcal{R}\))  
The ratio of delivered packets to total packets sent, or the packet delivery ratio (\(\mathcal{R}\)), is used to gauge a system's reliability. It is preferable for \(\mathcal{R}\) to be close to unity. The average packet delivery ratio is determined using Equation (42):  
\[\bar{\mathcal{R}}\ =\ \frac{100}{\mathcal{K}N}\sum_{m=1}^{\mathcal{K}}\sum_{i=1}^ {N}\frac{X_{ri}}{X_{ti}} \tag{42}\]  
In Equation (42), \(X_{ri}\) is the total number of packets received at corresponding destinations that are sent from source node \(i\); \(X_{ti}\) is the total number of packets sent from source node \(i\); \(\mathcal{K}\) is the total number of data gathering cycles; and \(N\) is the total number of nodes. As was specified for latency, the source and destination for \(\bar{\mathcal{R}}\) computation are also two different nodes (OBUs or RSUs). The meaning of the average Packet Delivery Ratio (PDR)given in Equation (42) in routing is the reliability of the routing framework in delivering packets from a given source node to a destination node on average.
# Bridgeless Buck-Boost PFC Rectifier with Positive Output Voltage
## 4 Design Considerations  
Before we tackle this section, some specifications and assumptions are given below: (i) the input voltage range locates between 90 Vrms and 130 Vrms with a nominal voltage of 110 Vrms; (ii) the output voltage is set at 80 V; (iii) the output power range locates between 22.5 W and 90 W with a rated power of 90 W; (iv) the switching frequency is set at 100 kHz; (v) the rated-load efficiency under the minimum input voltage is set at assumed to be 90%; (vi) the line frequency is set at 60 Hz; (vii) the peak-to-peak value of the output voltage ripple is set at 3% of the output voltage; and (viii) the circuit always works in the DCM except that the circuit works in the BCM at the peak input current which happens under the minimum input voltage and the rated output power.  
Figure 3: Power flow in: (**a**) state 1; (**b**) state 2; (**c**) state 3.  
Furthermore, the sampling frequency is 100 kHz, synchronous with the switching frequency. The number of sampling bits out of the ADC is 10 bits. The voltage ratio of the voltage divider is 3 over 80. The number of PWM signal bits out of the FPGA is 10 bits.  
In addition, the product name for the switches \(S_{1}\) and \(S_{2}\) and is LXTQ88N28T. The product name for the switch \(S_{3}\) is STP120 NF10. The product name for the diodes \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{4}\) is APT30D30 BCT. The product name for the diode \(D_{o}\) is MBR40100 PT. The inductor \(L\) has a value of 58.5 \(\upmu\)H based on a CM270125 core with 18 turns. The capacitor is constructed by two paralleled Nippon Chemi-Con capacitors, 650 \(\upmu\)F/650 \(\upmu\)F.
# Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
## 2 Literature Review
### Evaluating Targets of RAG  
Despite the proven effectiveness of RAG as outlined in Section 2.1, their successful implementation still requires careful tuning. Various factors, including the retrieval model, construction of the external knowledge base, and language model complexities, collectively influence the overall performance of the RAG system. Therefore, the evaluation of RAGsystems is critical [16]. However, before proceeding with evaluation, it is important to define evaluation targets.  
The workflow of a traditional RAG typically comprises three key steps according to [PERSON] et al. [6]: indexing, retrieval, and generation. Indexing involves extracting raw data from various formats, converting the data to plain text, segmenting the data into smaller chunks, and encoding these chunks into vector representations. Vector representations are the numerical representations of data, such as words or documents, that are used to capture semantic meaning and enable computational operations. During retrieval, user queries are transformed similarly into vector representations using the same encoding model applied during indexing. The query vectors are then compared with the vector representations of the indexed chunks to compute similarity scores. The chunks with the highest similarity scores are retrieved and serve as the expanded context for generating responses. In the generation phase, the query and selected chunks are synthesized as a prompt for an LLM to formulate a response. This process allows the model to leverage the encoded representations of the input data to generate contextually relevant and coherent responses [6].  
Let us note that there are slightly different approaches for RAG conceptualizations such as in IBM Research [7] where only the two phases retrieval and content generation are distinguished. However, both approaches agree that evaluation should ensure a comprehensive assessment of both the quality of the retrieved context and the coherence of the generated content. The retrieval quality assesses the effectiveness of the sourced context, while the generation quality evaluates the relevance and coherence of the responses [7].
## 2 Particle Swarm Optimization with Sigmoid Increasing Inertia Weight Algorithm  
### Theory of Particle Swarm Optimization  
The PSO algorithm is a random swarm intelligent search algorithm based on the process of birds foraging. Birds flying for food are regarded as particles and food is regarded as the optimal solution. In each iteration process, the particle changes its speed and position by tracking individual extremum and global extremum. Individual extremum represents the optimal solution found by the particle itself, defined as _pbest_. Global extremum represents the optimal solution found by particles in the whole particle swarm at present, defined as _gbest_. The velocity and position formulas of the standard PSO are as follows,  
\[v_{k+1}=v_{k}+c_{1}r_{1}(pbest_{k}-x_{k})+c_{2}r_{2}(gbest_{k}-x_{k}) \tag{1}\]  
\[x_{k+1}=x_{k}+v_{k+1} \tag{2}\]  
where \(k\) is the number of iterations; \(v_{k}\) is the velocity vector of particles; \(x_{k}\) is the position of the current particles; \(pbest_{k}\) indicates the position of the optimal solution found by the particle itself; \(gbest_{k}\) represents the random number evenly distributed among the positions of the optimal solution found by the whole population at present. \(c_{1}\) and \(c_{2}\) are learning factors, which, respectively, represent the cognitive ability and social ability of particles, \(c_{1}=c_{2}=2\); \(r_{1}\) and \(r_{2}\) are random numbers between 0 to 1. From a sociological point of view, the first part of Equation (1) (on the right side) is called a memory term and the second part is called a self-cognition, which means that the action of the particles comes from their own experience. The third part of Equation (1) is called a group cognition term,which indicates the cooperation and knowledge sharing among particles. To control and improve the search ability of the PSO algorithm, an inertia weight factor \(\omega\) can be added to Equation (1) as,  
\[v_{k+1}=\omega v_{k}+c_{1}r_{1}(pbest_{k}-x_{k})+c_{2}r_{2}(gbest_{k}-x_{k}) \tag{3}\]  
where \(\omega\) indicates the influence of the speed of the previous generation of particles on the speed of the next generation of particles. If \(\omega\) is large enough, it has strong global search ability; otherwise, it tends to search locally, which can be calculated as,  
\[\omega=\omega_{max}-iter\times\frac{\omega_{max}-\omega_{min}}{iter_{max}} \tag{4}\]  
where \(iter\) represents the current iteration steps, \(iter_{max}\) represents the maximum iteration steps.
## 4 Results and Discussion  
### Construction Stage  
Table 1 outlines the division of the Lowari Tunnel into 62 sections, considering the different groundwater conditions encountered (dry, damp, dripping, and flowing portions), rock types (Granite, Gabro, Granodiorite, Amphibolite, Rhyolite, Gneiss), and observed rock conditions (compactness, jointing). Table 1 shows that the flow of water in Lowari Tunnel is dependent on joints, with the greatest water ingress occurring at compacted and jointed rock boundaries. The metavolcanic portion of the tunnel has less water ingress. The water-bearing joints are oriented at an angle of +45\({}^{\circ}\) and are either parallel or sub-parallel in orientation. The different geological formations that the tunnel passes through are likely responsible for the variation in groundwater conditions. Figure 5a shows the hydrological status, indicating that the majority of the tunnel (81.70%) is classified as dry and damp, while the remaining portion (18.3%) is classified as dripping and flowing. Figure 5b illustrates the dewatering process carried out during the construction stage from April 2007 to February 2009. The observed dewatering volume ranged from 5846 m\({}^{3}\) to 115,100 m\({}^{3}\), totaling 1,027,433 m\({}^{3}\). The average monthly flow was measured at 46,702 m\({}^{3}\), with an average discharge of 0.02 m\({}^{3}\)/s. In April 2007, between chainages 1 + 816.5 and 1880.5, a maximum recorded outflow of 115,100 m\({}^{3}\) occurred due to a rise in temperature leading to snowmelt. In May and June 2007, outflows of 2045.2 m\({}^{3}\) and 2186.5 m\({}^{3}\), respectively, were observed at chainages 1 + 889.3 and 2 + 042. These results suggest that the water ingress into the Lowari Tunnel is seasonal. This is likely because the water ingress is caused by the seepage of water along joints and fractures in the rock. These joints and fractures are more likely to be water-bearing during periods of snowmelt or heavy rainfall. During the excavation process in 2008 and 2009, a significant reduction in water ingress was observed due to the successful grouting of water leakage points such as joints and fractures with shotcrete. This led to a noteworthy mitigation of water infiltration in the tunnel, suggesting that grouting with shotcrete proved to be an effective technique for reducing water ingress during tunnel excavation. Figure 4: Hydrological condition during operation stage of Lowari Tunnel. (**A**) Snow condition at north portal during winter. (**B**) Water discharge at north portal during summer.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
###### Abstract  
Cellular structures (CSs) have been used extensively in recent years, as they offer a unique range of design freedoms. They can be deployed to create parts that can be lightweight by introducing controlled porous features, while still retaining or improving their mechanical, thermal, or even vibrational properties. Recent advancements in additive manufacturing (AM) technologies have helped to increase the feasibility and adoption of cellular structures. The layer-by-layer manufacturing approach offered by AM is ideal for fabricating CSs, with the cost of such parts being largely independent of complexity. There is a growing body of literature concerning CSs made via AM; this presents an opportunity to review the state-of-the-art in this domain and to showcase opportunities in design and manufacturing. This review will propose a novel way of classifying cellular structures by isolating their Geometrical Degrees of Freedom (GDoFs) and will explore the recent innovations in additively manufactured CSs. Based on the present work, the design inputs that are common in CSs generation will be highlighted. Furthermore, the work explores examples of how design inputs have been used to drive the design domain through various case studies. Finally, the review will highlight the manufacturability limitations of CSs in AM.  
cellular structures; additive manufacturing; cellular design; hierarchical structures; lattice structures +
Footnote †: journal: _Sci.  
have expanded the opportunities for manufacturing CSs and highlights the limitations and opportunities in fabricating such structures. As such, the focus of this work is twofold:  
1. To review the design and geometrical considerations for CS and their classification based on unit cell feature properties.
2. To highlight the manufacturability limitations and opportunities in DfAM with CSs.  
In recent years, a few other articles have focused on reviewing one specific or more aspects of AM cellular structures [13; 14; 15; 16; 17; 18; 19]. Other reviews have focused on specific types of porous structures, such as biodegradable structures for porous implants [20; 21; 22], Functionally Gradient Material (FGM) or Gradient Lattice Structures (GLS) [23; 24]. Furthermore, some reviews have touched to some extent on all the aspects CSs [25]. This review will bring forth a new classification paradigm, in addition to highlighting the issues that are specifically related to additively manufactured CSs.