esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Artificial Neural Network
### Model Robustness  
An ANN model may achieve good prediction, which could be evaluated according to the fit obtained between the original outputs of the model and the simulated responses. However, the model may only be considered robust if it predicts the response in a realistic manner, that is, according to the underlying physical behaviour of the investigated system. Hence, the model also needs validation to confirm its robustness to assess the relationship [55]. In this regard, a sensitivity analysis was carried out to evaluate the response of the developed ANN model. The incremental sensitivity method was used to assess the significance of each input parameter in the prediction equation [56]. Sensitivity analysis is a simple and innovative technique that examines the connection weights of the trained network by interpreting the relative significance of the input variables [57]. In the incremental sensitivity analysis, all input variables except one are fixed to the mean values and the other variables are varied between the input range (minimum to maximum); finally, the predicted response is measured in each step [58]. For this study, this process was repeated for all drill types and the robustness was examined to see how well the predicted data are in agreement with the physical behaviour over a range of input data. Figure 11 shows that, in general, for all the drill types, \(R_{a}\) increases by increasing spindle speed and feed. However, the effect of spindle speed was found to be more than that of feed. This is in line with the previous experimental studies conducted by [16; 17; 18]. In addition, the graph of uncoated drill presented low values of \(R_{a}\) at low spindle speed while a significant increase was observed with the rise in spindle speed. The TiCN coated drills performed better at the high spindle speed whereas the TiN drill gave the highest values of \(R_{a}\) compared to those measured from uncoated and TiCN carbide coated drills. This concluded that the developed model agrees with the expected effect of spindle speed, feed, and coatings on the \(R_{a}\). Therefore, the developed model can be useful for industries and manufacturing engineers for predicting the surface roughness of Al2024 in multi-hole simultaneous drilling processes.  
Figure 10: Scatter and error plots between the measured and simulated values of surface roughness for testing.
## 4 Summary and Conclusions  
The impact response for the TRI and UD GFRP laminates after impact, including residual failure strength reduction, damage patterns and stress concentration, was investigated, considering the effects of the impact energy, impactor shapes and laminate structure. Furthermore, impact damage patterns were investigated and stress concentration around the impact indents boundary was evaluated from the full-field stress distribution, determined by DIC measurement, over the impacted area.  
1. The TRI GFRP laminate was remarkably more susceptible to the impact indentation than the UD laminate. Reduction of residual in-plane tensile strength for the impacted TRI laminate was relatively more severe than that for the impacted UD laminate.
2. Reduction of residual in-plane tensile strength for the TRI and UD GFRP laminates was dependent on the impact energy level and indent shape. The strength reduction became greater with increasing impact energy level, and the pyramidal indent had a relatively greater influence on the residual strength reduction than the hemispherical indent.
3. Full-field directional stress, including longitudinal (\(\sigma_{yy}\)), transverse (\(\sigma_{xx}\)) and shear (\(\sigma_{xy}\)) stress, distributions in the vicinity of the pyramidal and hemispherical indents for the TRI and UD GFRP laminates were determined from the DIC measurement using a linear elastic orthotropic relationship. These stresses were relatively significantly concentrated in the vicinity of the pyramidal indent for the TRI laminate, compared to those for UD laminate. Stress concentrations in the vicinity of the pyramidal and hemispherical indents for the TRI laminate were relatively  
Figure 11: Normalized longitudinal stresses ahead of the indent for the impacted (**a**) TRI and (**b**) UD GFRP laminate.  
higher than those for the UD laminate. Furthermore, the stress concentration became higher with increasing impact energy levels.
4. The residual strength reduction of the impacted GFRP laminates may be considerably closely associated with the stress concentration in the vicinity of the indent.
5. The impact damage patterns created in the TRI and UD GFRP laminates were dependent on the impactor head shape, but the dominant patterns were matrix cracking, delamination, debonding and fiber breakage.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]-[PERSON] and [PERSON]; formal analysis, [PERSON]-[PERSON]; investigation, [PERSON] and [PERSON]; data curation, [PERSON]-[PERSON]; writing--Original draft preparation, [PERSON]-[PERSON], writing--Review and editing, [PERSON]-H.H. and [PERSON]-H.K.; supervision, [PERSON]-H.H. and [PERSON]-[PERSON]; funding acquisition, Y.-H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the KETEP (Korea Institute of Energy Technology Evaluation and Planning) granted by the Korea government (MSIT No. 20153010024470) and KRISS.  
The authors declare no conflict of interest.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 5 Experimental Results
### Scale  
The scalability of the environments was tested using BGP, due to its industry-level implementation and scalability, which outperforms the rest of the routing protocols that were already analyzed under the Sybil framework. The single host emulations using Mininet, Kathara, and Core were tested over the infrastructure mentioned above. On the other hand, the distributed emulations were tested with Megalos using a cluster composed of 14 virtual machines with 4 CPUs and 16 GB de RAM each. Figure 5 shows the maximum \(k\) reached with each environment. Furthermore, Table 2 summarizes the results, adding the number of nodes and interfaces emulated with each environment.  
The first observation is that distributed emulation outperforms a single host one in terms of scalability. This is shown by the fact that Megalos successfully emulates more than a thousand nodes and forty thousand virtual interfaces. This result is expected since Megalos benefits from horizontal scalability, but there is a trade-off of scale vs. cost (i.e., the number of servers needed), which shall be considered. In contrast, on the single host side, Mininet scaled up to sixty hundred nodes and thirteen thousand virtual interfaces.  
Considering only single host environments, Mininet outperforms Core and Kathara, emulating more than three times as many nodes and six times as many virtual interfaces. A feasible explanation for this performance gap of Core and Kathara is the density of the fat-tree topologies. Increasing the \(k\) factor rapidly increases the number of virtual interfaces and virtual links, exhausting host resources and preventing connectivity with the emulation process. On the other hand, increasing the \(k\) factor to 22 in Mininet leads to a 100% CPU use due to the topology density and the number of BGP and zebra daemons created.
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 4 Microwave Irradiation  
In the electromagnetic spectrum, microwave (MW) irradiation occurs in a frequency range of 300 MHz to 300 GHz with a corresponding wavelength of 1 mm-1 m. MW irradiation is considered a promising alternative to conventional heating methods. In wastewater and wastewater sludge treatment, during microwave irradiation, the destruction of microorganisms and other molecules may occur in two ways: thermal and athermal (non-thermal) effects. Thermal effects are generated via ionic conduction (in shorter frequencies) and dipole rotation (in higher frequencies)--the former means the electrophoretic (conductive) migration of dissolved ions in the electromagnetic field [28], while the latter is generated through the rotation of dipole molecules (like water) due to the constant and repeated changes in the polarity of the field [29]. Athermal effects are induced by the change in dipole orientation of certain polar molecules, which increase the possibility of breaking down the hydrogen bonds of biopolymers (polysaccharides, proteins, DNA, RNA) [30; 31]. In industrial use, a frequency of 915 MHz is the most favourable, since shorter frequencies have higher penetration depth [32], thus increasing the extent of thermal and athermal effects.  
It was concluded that the SCOD/TCOD ratio increased from 2% to 22% of sludge after MW irradiation (630 kJ total irradiated energy) [33]. SCOD/TCOD ratios were also increased in waste activated sludge from 8% to 18% after microwave-based heating to 72.5 \({}^{\circ}\)C [34] and from 6% to 18% after MW heating to 96 \({}^{\circ}\)C [35]. [PERSON] et al. reported that depending on the applied total energy and power ratings, a 43% to 66% of increment occurred in the solubility (COD/TVS ratio) of floated sewage sludge [36].  
Standalone microwave irradiation can also increase the extent of biogas production from sludge. Waste activated sludge (WAS) heated to different temperatures through microwave treatment resulted in a higher rate and extent of biogas production [37]. [PERSON] et al. showed that the microwave heating of thickened waste activated sludge up to 175 \({}^{\circ}\)C resulted in a 135% higher biogas yield compared to the control samples [38]. Applying a total of 14.000 kJ/kgTS microwave energy resulted in a +570% biogas yield, as reported by [PERSON] et al. in 2015 [39]. In another experiment, the effects of microwave irradiation on the removal of COD were investigated: [PERSON] et al. reported that the treatment of WAS by MW to 91 \({}^{\circ}\)C, 64% of COD decrement could be achieved [40]. Combination of microwave irradiation with ultrasonication can also be a promising method in wastewater and wastewater sludge treatment: [PERSON] et al. applied ultrasonic irradiation (0.4 W/mL, 6 min) after MW treatment (2450 MHz, 3 min), which resulted in a higher cumulative biogas production compared to the control samples [41].
## 3 Methodology
### Vector Databases  
Vector databases differ from traditional databases in both structure and function, offering advantages for applications that rely on similarity search rather than exact matching. Traditional databases store structured data and use rule-based queries. In contrast, vector databases manage high-dimensional embeddings of numerical representations of unstructured data, such as text, images, or video, to enable semantic search using similarity metrics, including cosine similarity or Euclidean distance [28]. Most vector database systems include built-in support for embedding generation, vector computation, and optimization. These databases are crucial in AI-driven use cases, such as recommendation systems, image retrieval, and personalized search, where relevance depends on meaning rather than keywords. They also provide efficient indexing and storage at scale, support real-time querying, reduce latency in machine learning workflows, and lower the cost and complexity of building custom retrieval solutions [29; 30].  
In this work, we utilized a self-hosted instance of Qdrant, an open-source and commercially available database solution running within a Docker container. Qdrant is a high-performance vector similarity search engine designed for managing and querying high-dimensional vectors with optional metadata, known as payloads. It is well-suited for applications such as semantic search and recommendation systems, where traditional databases often fall short. Qdrant supports distance metrics such as cosine similarity, dot product, and Euclidean distance, as well as B25 Hybrid search, and it utilizes efficient indexing methods like HNSW for fast Approximate Nearest Neighbor search. Data is organized into collections of points, each consisting of a vector, an ID, and optional payloads for filtering and enriched search results. With flexible storage options, a simple API, and support for various deployment environments, Qdrant offers an efficient and scalable solution for vector-based retrieval tasks.  
The database instance was optimized to utilize an ANN + BM25 hybrid search for improved vector search. The data was accessed through the REST-API functionality provided by the Qdrant database.
## 4 Conclusions  
The study successfully optimized the processing parameters for AZ31/1.75 wt.% GNP composites using the Taguchi method, identifying compaction pressure as the most influential factor on both microhardness and compressive strength, followed by sintering temperature and sintering time. The optimal combination (350 MPa, 600 \({}^{\circ}\)C, 60 min) significantly improved mechanical properties, yielding a microhardness of 108.5 Hv and compressive strength of 452.2 MPa. Low porosity (%) and reduced grain size are accountable for an improvement in the mechanical properties of the composite compared to the base alloy. Regression models demonstrated strong predictive accuracy, reinforcing the reliability of the optimization process. The findings of this study have important implications for the development of lightweight, high-strength magnesium-based composites, particularly for aerospace and automotive applications. By optimizing processing parameters, this research provides a cost-effective approach to enhancing material performance. However, one limitation of this study was the lack of experimental validation for the regression models beyond the training dataset. Future work will focus on cross-validation techniques and additional experiments to confirm model accuracy across different processing conditions. Further investigations into the long-term mechanical stability and wear resistance of these composites will also be valuable for practical applications. By addressing these limitations and expanding the scope of analysis, future studies can further refine the optimization  
Figure 10: Comparison of experimental and predicted values obtained using the Taguchi method for (**a**) microhardness (HV) and (**b**) compressive strength (MPa). The graphs illustrate a strong correlation between the experimental data and the Taguchi-predicted values across 9 trials. The minimal deviations observed between the two datasets indicate the high accuracy and reliability of the predictive model.  
process and broaden the applicability of AZ31/GNP composites in advanced engineering applications.
## 3 Results and Discussion
### Adsorption Kinetics and Isotherm
#### 3.3.2 Adsorption Isotherm
The Freundlich model (1906) is a model that applies the data for multilayer and heterogeneous surfaces, the model equation being as follows [58]:  
\[q_{e}=K_{F}\;*\;C_{e}\frac{1}{n} \tag{12}\]  
The linearization of Equation (12) gives the following equation:  
\[logq_{e}=logK_{F}+\frac{1}{n}logC_{e} \tag{13}\]  
where \(K_{f}\) (mg/g) is a constant related to the adsorption energy. The values of 1/n, \(K_{f}\), and \(R^{2}\) were calculated and listed in Table 3.  
The Tamkin model is used mainly for the retention of gases on solid surfaces. Therefore, several researchers propose applying this model to the adsorption of aqueous solutions on the surface of bio-adsorbents and assume that the adsorbent molecules are retained on the solid surface energetically, since the model is based on the free energy of sorption as a function of binding energy. The linear form of the model is as follows [58]:  
\[q_{e}=\bigg{(}\frac{RT}{b}\bigg{)}Ln(A_{T}C_{e})=B_{T}Ln(A_{T}C_{e}) \tag{14}\]  
\[q_{e}=B_{T}Ln(A_{T})+B_{T}Ln(C_{e}) \tag{15}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**adsorbent** & **adsorption Capacity (mg/g)** & **References** \\ \hline Soy meal hull & 120.5 & [50] \\ Bamboo sawdust & 6.43 & [51] \\ Modified bamboo sawdust & 13.83 & [51] \\ Chamomile plant & 10.1 & [52] \\ Pumice & 22.422 & [50] \\ Potato peel & 58.8 & [53] \\ Neem bark & 10.41 & [53] \\ Activated potato peel & 8.4 & [53] \\ Activated neem bark & 5.71 & [53] \\ Modified silk maze & 30.30 & [54] \\ Soil containing copper & 26.2 & [40] \\ Iron filings & 25.3 & [55] \\ GO-MVK-ANI & 11.8 & [56] \\ Sugar beet industrial filter cake waste & 64.1 & [57] \\ Cockleshells (CS) & 4.65 & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the maximum biosorption capacity of DR-81 dye onto various adsorbents. where \(\rm q_{e}\) (mg/g) is the maximum adsorption capacity, \(\rm C_{e}\) (mg/L) is the adsorbate concentration at equilibrium, and \(\rm Ar_{T}\): Tamkin constant (L/g). The Tamkin model equilibrium constant corresponds to the maximum binding energy \(\rm b_{T}\) (mg/L), which is related to the heat of sorption \(\rm B_{T}\) (J/mol). The Tamkin constant represents the variation of energy as a function of binding energy. The relevant coefficients can be calculated from three isotherms, such as \(\rm K_{L}\), \(\rm Q_{max}\), \(\rm R_{L}\), \(\rm K_{f}\), n, A, B, and b. The values are listed in Table 3.
###### Abstract
)\", existing methods may retrieve triples directly connected to the query entity \"[PERSON]\" as in-context triples. Upon analysis, it becomes evident that most of these retrieved triples are irrelevant to the reasoning. Unselectively incorporating them into the context may instead introduce noise, thereby diminishing the reasoning effectiveness. In contrast, it can be determined that introducing triples such as \"([PERSON], works at, University of Colorado Boulder), (University of Colorado Boulder, located in state, Colorado)\" as in-context triples can provide valuable contextual knowledge, thereby enhancing the performance of LLMs on knowledge graph reasoning. Therefore, in this paper, we propose the **rule-and-reinforce triple extraction method**; the method can enhance the in-context learning of LLMs for knowledge graph reasoning. The proposed method contains two parts: (1) **logical rules guiding in-context triples retrieval and extractor pre-training. ** We retrieve the in-context triples involved in the logical rules for each query triple as supervised training data, and train an extractor to generate in-context triples for specific query triples; (2) **reinforcement learning with LLMs' feedback as rewards**. The feedback of LLMs is collected as rewards to further optimize the extractor through reinforcement learning, in which the policy gradient is utilized to encourage the extractor to explore more useful triples that yield higher rewards. Through experiments and comparisons conducted on five different knowledge graphs, the results demonstrate that the in-context triples extracted by the proposed method can effectively enhance the performance of large language models in knowledge graph reasoning. The contributions of this paper are summarized as follows:  
* To alleviate the problem that traditional LLM-based reasoning methods fail to fully utilize the existing data of knowledge graphs, we propose the rule-and-reinforce triple extraction method; the method can enhance the in-context learning of LLMs for knowledge graph reasoning;
* In order to obtain more effective in-context triples, we construct an in-context triple extractor, which is designed based on the encoder-decoder architecture. The triples involved in the logical rules of the knowledge graph are utilized as supervised data for pre-training. Then, the extractor is further trained through reinforcement learning methods with the feedback from LLMs;
* The experimental results on five different knowledge graphs indicate that the in-context triples extracted by the proposed method can effectively enhance the capabilities of LLMs in knowledge graph reasoning.
## 2 System Model  
We consider a VLC system composed of \(L\), \(l=\{1,\ldots,L\}\), optical transmitters that provide both illumination and data transmission to a receiver composed of \(N\), \(n=\{1,\ldots,N\}\), photodiodes. The transmitted signal follows a frequency shift keying (FSK) modulation in which the transmission of symbol \"0\" corresponds to a square signal at frequency \(f_{0}\) and symbol \"1\" is given by the same signal at frequency \(f_{1}\), in which \(f_{1}>f_{0}\), satisfying the modulation index for a data rate \(R_{\text{data}}\). Specifically, the transmitted signal is given by \(\mathbf{x}=\left[x_{1},\ldots,x_{L}\right]\in\mathbb{R}_{+}^{L\times 1}\), where \(x_{l}\) corresponds to the signal at optical transmitter \(l\). The signal received by the photodiode \(n\) is denoted by \(y_{n}\). Then, the signal received by the \(N\) photodiodes that compose the photoreceiver, which is denoted by \(\mathbf{y}=\left[y_{1}\ \ \ldots\ \ y_{N}\right]\in\mathbb{R}^{N\times 1}\), is given by  
\[\mathbf{y}=\delta\mathbf{H}\mathbf{x}+\mathbf{z}\, \tag{1}\]  
where \(\delta\) is the responsivity of the photodiodes, which is assumed as the same for all of them, and \(\mathbf{H}\in\mathbb{R}^{L\times N}\) is the resulting channel matrix defined as  
\[\mathbf{H}=\left[\mathbf{h}_{1}^{T}\ \ \ \mathbf{h}_{2}^{T}\ \ \ \ldots\ \ \ \mathbf{h}_{N}^{T}\right]^{T}\, \tag{2}\]  
in which \(\mathbf{h}_{n}=\left[h_{n,1}\ \ \ldots\ \ \ h_{n,1}\ \ \ldots\ \ \ h_{n,L}\right]\in\mathbb{R}^{L\times 1}\) is the optical channel of photodiode \(n\) and \(h_{n,l}\) is the optical channel between optical transmitter \(l\) and photodiode \(n\). In (2), \({}^{T}\) denotes the transpose operator. Moreover, in (1), \(\mathbf{z}\in\mathbb{R}_{+}^{N\times 1}\) is the noise vector, which is assumed as additive white Gaussian noise.
## 5 Ablation Study and Discussion
### Visualizing Sensor Feature Selection Weights  
We visualized the attentional weights in the SFS module to evaluate the effects of different sensor features on different parts of the human body and different actions. Figure 10 shows the IMU inertial sensing units in different parts of the human body in the PAMAP2 dataset; Figure 10,c shows the attentional weights of the different sensor features for the actions of \"running\" and \"ironing\", respectively.  
In Figure 10, the \"hand_acc\", \"chest_acc\", and \"ankle_acc\" three-axis sensors in the IMU have a significant impact on the running action. This is reasonable and intuitively understandable because all parts of the human body are coordinated to complete actions during running, and different types of sensor features play different roles in recognizing different actions. In Figure 10, the \"hand_acc\" sensor in the IMU is given more weight, which is also reasonable because ironing is mainly performed with the hand.  
Not all sensor features have the same contribution when performing action classification. Our SFS module can automatically learn the weights of different sensor features in the HAR task, capturing their contributions and potential importance. In short, our module effectively identifies sensor features that contribute to the HAR task, providing a more accurate basis for action classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action of Opportunity Dataset** & **Precision** & **Recall** & **Macro F1-Score** \\ \hline Open Dishwasher & 0.595 & 0.658 & 0.625 \\ Close Dishwasher & 0.486 & 0.586 & 0.531 \\ Open Drawer 1 & 0.294 & 0.385 & 0.333 \\ Close Drawer 1 & 0.539 & 0.467 & 0.500 \\ Open Drawer 2 & 0.889 & 0.500 & 0.640 \\ Close Drawer 2 & 0.636 & 0.700 & 0.667 \\ Open Drawer 3 & 0.606 & 0.769 & 0.678 \\ Close Drawer 3 & 0.560 & 0.667 & 0.609 \\ Clean Table & 0.909 & 0.526 & 0.667 \\ Drink from Cup & 0.762 & 0.647 & 0.700 \\ Toggle Switch & 0.857 & 0.450 & 0.590 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 10: Visualization of attention weights for “running” (**b**) and “ironing” (**c**) actions of the PAMAP2 dataset, and the position of sensors (**a**).