esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.3 The Total Phenolic Content (TPC), ABTS, DPPH, FRAP  
The total phenolic content (TPC) was measured using a modified method from [PERSON] et al. (2018). First, 300 uL of a diluted fermented cassava root flour extract (made by mixing 1 mL of the sample with 100 mL of water) was mixed with 1.5 mL of diluted Folin-Ciocalteu reagent (5 times dilution), 1.2 mL of a 7.5% sodium carbonate solution, and a methanol-water mixture (6:4 ratio). This mixture was left at room temperature for 60 min. Subsequently, the absorbance was measured at 760 nm using a Hitachi Model 100-20 spectrophotometer (Hitachi, Tokyo, Japan). The results were reported as milligrams of gallic acid equivalent per milliliter (mg GAE/mL).  
To measure antioxidant activity, the ABTS+ radical scavenging test was used, following a modified version of the method by [PERSON] et al. (2018), adapted for 96-well plates. The ABTS+ stock solution (7 mM) was prepared by mixing equal parts of ABTS (8 mg/mL in water) and potassium persulfate (2.45 mM in water), and the mixture was left in the dark at room temperature for 12-16 h. This stock was diluted 1:29 with phosphate-buffered saline (PBS, pH 6.9) to obtain a working solution. A standard curve was created using Trolox at different concentrations (0 to 800 uM, in methanol). For the test, 10 uL of the sample (prepared for HPLC) was mixed with 190 uL of ABTS+ working solution in a microplate well and kept in the dark for 30 min. Absorbance was then measured at 750 nm using a Multiscan FC reader. The antioxidant activity was calculated by comparing the absorbance drop with the Trolox standard and expressed as micrograms of Trolox equivalent per milliliter (ug TE/mL).  
The DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging activity was determined following a slightly modified version of the method by [PERSON] et al. (2018). A 130 uL portion of the sample was mixed with a 0.1 mM DPPH solution and kept in the dark for 30 min. The absorbance was then measured at 510 nm. Control samples were prepared similarly, substituting methanol for the test sample, and methanol alone was used as the blank. The percentage of DPPH radical scavenging was calculated using the appropriate formula.  
\[\text{DPPH\,Scavenging\,\%}=\frac{\left(\text{Acontrol}-\text{Asample} \right)}{\text{Acontrol}}\times 100 \tag{1}\]  
The ferric reducing antioxidant power (FRAP) assay was carried out using a modified version of the method developed by [PERSON] and [PERSON] (2018). About 2 mg of the sample extract was mixed with 1 mL of FRAP reagent. This reagent was obtained by combining a 300 mM sodium acetate buffer (pH 3.6), a 10 mM TPTZ (2,4,6-tri(2-pyridyl)-s-triazine) solution, and a 20 mM FeCl\({}_{3}\cdot\)6H\({}_{2}\)O solution in a 10:1:1 ratio. The mixture was then diluted with water to reach a total volume of 4 mL. It was incubated in a water bath at 37 \({}^{\circ}\)C for 30 min. After incubation, the absorbance was read at 593 nm using a Hitachi Model 100-20 spectrophotometer (Hitachi, Tokyo, Japan).
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 2 Related Works
In our previous work [37] for a preliminary wireless sensor network (WSN) topology, we first applied static bounded error in codebook for data correlation compression and outperformed DF-TS in sensor energy saving. Then, the dynamic bounded error in more realistic WSN topology was considered in [38] to further validate the energy efficiency by four different BEA in linear/non-linear proportions. Furthermore, the authors of [36] improved previous works described above by applying run length encoding (RLE) in data correlation compression and revising the spatial correlation compression method. Different with [36; 37; 38], in a wireless body sensor network (WBSN), a recent article [39] first applied an error-bounded lossy compression scheme [42] in high performance computing (HPC) and then used a machine learning technique to rebuild the transmitted data with given bounded error on the edge node. However, for IEI sensor networks and different QoS2/QoD required in their various applications, our proposed architecture applies overall lossless compression schemes in data, spatial and temporal correlations among bounded-error-pruned sensor data. Without the need to rebuild the lossy transmitted data in fog-computing machines, the total data size in transmitting and storing can be minimized for energy saving to prolong IoT lifetime for IEI applications in order to quickly access the bounded-error-pruned sensor data.
# Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-Learning
## 5 Experimental Results
### Validation of Components in Open DGML
We first compare the effectiveness of our proposed data segmentation, feature extraction, and encoding steps with various flow processing methods on the ISCX2012 dataset. We implement K-means clustering for feature-based clustering and feature-based clustering with payload. The latter boosts the accuracy by 0.9% because the payload contains important information. However, the payload is often too long and contains redundancy, decreasing efficiency. We compare our method with the approach that directly converts raw packets into binary images and find that our Open DGML method outperforms it by 3.4%. We also find that our method outperforms methods that only truncate packets or flows, which perform data dimensionality reduction. The payload contributes to accuracy improvement by 1.1%. Our experiments showcase the advantages of our proposed data preprocessing module for intrusion detection. In addition to improving accuracy and detection rate, our data segmentation and truncation steps significantly reduce the input size and computation cost, leading to a shorter time for intrusion detection, which better meets the needs of real-time traffic identification. To quantify the efficiency improvement in terms of time consumption, we compare the methods in terms of the number of samples recognized per unit time, with Open DGML set as the standard of 100 samples per unit time. The results in Figure 4 show that the direct image conversion method has the lowest recognition efficiency because it contains too much redundant information that cannot be effectively utilized and increases the input dimension, leading to a lower number of recognized samples per unit time. The method that only truncates packets or flows has a relatively higher time efficiency because the number of packets is often smaller than the number of bytes in a flow, resulting in lower input dimensions. Although these two methods have similar accuracy to our method, the time consumption difference is much larger, with our method achieving a 20-30% improvement. Finally, the method without payload sacrifices a significant amount of accuracy for a minor improvement in time efficiency, which is not desirable for intrusion detection tasks where accuracy is crucial. Therefore, our flow processing method in the proposed framework can balance high accuracy and low time consumption to meet the real-time requirements of intrusion detection in practical scenarios. In conclusion, the ablation study and comparative experiments underscore the superiority of the Open DGML framework in intrusion detection. The integrated approach of flow image technology, Mul-Mixup, and meta-learning not only improves accuracy and detection rates but also aligns well with real-time requirements in practical scenarios.
###### Abstract  
In order to eliminate the negative effects caused by traditional pavements, permeable pavements are gradually being used in road construction. In recent years, polyurethane (PU) has been used as a new binder in permeable pavement mixtures. However, compared to traditional pavement mixtures, the adhesion properties between PU and aggregate have not been systematically analyzed. In addition, no clear standards have been established for the performance testing of PU mixtures, posing significant challenges for the selection of materials and the optimization of formulations for PU mixtures. Therefore, this paper proposes new methods for evaluating the performance of PU mixtures from a microscopic point of view, aiming at evaluating the adhesion properties between PU and aggregates. In this study, a PU binder was synthesized. The adhesion properties of this PU binder with aggregate were evaluated by surface free energy measurement and molecular dynamics (MD) simulation. Finally, the effects of different environmental conditions and aggregate types on the PU-aggregate adhesion properties were investigated. The results showed that the adhesion between PU and basalt is consistently better than that with limestone, although the adhesion between PU and aggregate decreased under acidic conditions. It implies that the PU-basalt mixture has better water resistance than the PU-limestone mixture. Furthermore, the results of the surface free energy measurements and MD simulations for the evaluation of adhesion at the PU-aggregate interface showed good correlation with the macroscopic performance experiments, which can be extended to the study of the adhesion properties of other materials.  
p _Applied Sciences_  
1 Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an 710064, China; [EMAIL_ADDRESS] (J.J.) [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (J.W.)  
Mount Tishan Traffic Planning & Design Consulting Co., Ltd., Tai'an 271000, China; [EMAIL_ADDRESS]  
**Keywords:** polyurethane; aggregate; adhesion properties; surface free energy; molecular dynamics simulation
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 3 Methods
### Definition of the Pedestrian Trajectory Prediction Problem  
Pedestrian trajectory prediction involves predicting the future position sequence of pedestrians based on their historical position sequences in two-dimensional space. Ultimately, this problem can be regarded as a temporal prediction problem. Suppose that within a given time period \([1,T_{obs}]\), the position sequence \(P\) of \(N\) pedestrians is given; then, the position of any individual \(p_{t}^{i}\) in \(P\) can be represented as:  
\[p_{t}^{i}=\{(x_{t}^{i},y_{t}^{i})|t\in\{1,2,\ldots,T_{obs}\},i\in\{1,.,2, \ldots,N\}\} \tag{1}\]  
Our goal is to predict the position sequence \(\hat{P}\) of these pedestrians from \(T_{obs+1}\) to \(T_{pred}\) based on the position sequences of the past \(N\) pedestrians within \(T_{obs}\) time steps. Then, the position of any individual \(\hat{p}_{t}^{i}\) in \(\hat{P}\) can be represented as:  
\[\hat{p}_{t}^{i}=\{(x_{t}^{i},y_{t}^{i})|t\in\left\{T_{obs+1},T_{obs+2}  
With this equation, we have successfully transformed the pedestrian trajectory prediction challenge into a constrained learning problem governed by bivariate Gaussian distributions.  
By learning the underlying bivariate Gaussian patterns embedded in historical trajectory data, we can now probabilistically forecast pedestrian positions across future time steps through the acquired distribution parameters. This paradigm shift enables systematic uncertainty quantification while maintaining the mathematical tractability in motion prediction.
# Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method  
[PERSON]  
1 School of Astronautics, Beihang University, Beijing 100191, China; [EMAIL_ADDRESS]  
Hengyi Lv  
2 Chang  
are quite some works on path feasibility analysis [10]. A basic approach is to collect the conditions along the path to form a SMT(LIA) formula, and decide whether the formula is satisfiable or not. The program path is feasible if and only if the formula is satisfiable. So, the path feasibility analysis problem is reduced to a constraint solving problem [8; 11]. Furthermore, we may ask which execution path is a hot/cold path [12]. Identification of such types of paths are valuable for testing case generation and can be translated into #SMT(LIA) problems [13].  
In this paper, we propose an approximate method for #SMT(LIA). Specifically, a novel Markov Chain Monte Carlo (MCMC) sampling algorithm based on the flat histogram method is proposed [14]. The flat histogram method has been widely studied in statistical physics [15; 16; 17], which is based on the observation that if a random walk in energy space is performed with a probability proportional to the reciprocal of the density of states \(1/g(E)\), then a flat histogram is achieved for the energy distribution. Here, \(E\) is an energy level and \(g(E)\) is the number of states with energy \(E\). For valuations of a SMT(LIA) formula, we define the energies of them, and use the idea of flat histogram method to sample from the space of valuations. The density of the zero energy is used to estimate the volume of the formula. The contribution of this paper is summarized as follows. First, this is the first work of exploiting the flat histogram method to solve the #SMT(LIA) problem. Secondly, a pseudo sampling strategy is introduced for evaluating the flatness of histograms generated in the sampling process, which is shown to be effective for #SMT(LIA) problems. Thirdly, extensive experiments were carried out to compare our methods with four related methods: an exact #SMT(LIA) solver developed in [18], an exact method for counting the number of solutions of convex bodies called VolCE+LattE [9], an approximate counter for convex bodies called SMTApproxMC [19] and another counter using bitblasting called ApproxMC2 [20; 21]. Results show that our approaches are competitive to the state-of-the-art.  
The organization of this paper is as follows. In the next section, we introduce the basic concepts and applications of #SMT(LIA), and some backgrounds on MCMC. In Section 3, we briefly review related works. In Section 4, a flat histogram based MCMC method for #STM(LIA) is presented. Following that, experimental results are presented. Finally, we conclude the paper.
## 6 Multidrug Resistance of _A. baumannii_
### Heteroresistance to Antibiotics in A. baumannii  
The term \"heteroresistance\" is defined as the presence of subpopulations of cells that have a higher MIC than the dominant population [228; 229]. In contrast to persisters which are dormant cells, heterosexual subpopulations can proliferate in the presence of antibiotics.  
_A. baumannii_ heteroresistance to colistin in \"colistin-susceptible\" clinical isolates was described for the first time by [PERSON] et al. [230]. Colistin heterosexuals was caused by mutations and the insertional inactivation of the lipid A biosynthesis genes, leading to the complete loss of lipopolysaccharidesaccharides [63; 231]. Heteroresistance to carbapenems, aminoglycososides, and trimethoprim/sulfamethoxazole in _A. baumannii_ was also reported [167; 232; 233]. For example, it was found that increased tobramycin resistance was an unstable phenotype that emerged due to the extensive RecA-dependent amplification of the _aadB_ gene encoding an aminoglycoside adenylyltransferase. The _aadB_ gene was carried on a plasmid, in the region containing four other resistance genes [233]. Gene amplification seems to be the main mechanism conferring heteroresistance in various pathogens. Analysis of the prevalence and mechanisms of heteroresistance in _A. baumannii_, _E. coli_, _Salmonella enterica, and Klebsiella pneumoniae_ revealed that almost 28% of clinical isolates were heterosexual to various antibiotics. The majority of heteroresistance cases were unstable, and they resulted from tandem amplification of resistance genes [234].
# Can Defect Prediction Be Useful for Coarse-Level Tasks of Software Testing?
###### Abstract  
It is popular to use software defect prediction (SDP) techniques to predict bugs in software in the past 20 years. Before conducting software testing (ST), the result of SDP assists on resource allocation for ST. However, DP usually works on fine-level tasks (or white-box testing) instead of coarse-level tasks (or black-box testing). Before ST or without historical execution information, it is difficult to get resource allocated properly. Therefore, a SDP-based approach, named DPAHM, is proposed to assist on arranging resource for coarse-level tasks. The method combines analytic hierarchy process (AHP) and variant incidence matrix. Besides, we apply the proposed DPAHM into a proprietary software, named MC. Besides, we conduct an up-to-down structure, including three layers for MC. Additionally, the performance measure of each layer is calculated based on the SDP result. Therefore, the resource allocation strategy for coarse-level tasks is gained according to the prediction result. The experiment indicates our proposed method is effective for resource allocation of coarse-level tasks before executing ST.  
software testing; software defect prediction (SDP); analytic hierarchy process (AHP); graph theory; incidence matrix +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Sample Preparation and Functionalization  
To ensure the homogeneity of the coating, the dispersions were ultrasonicated for five minutes immediately before application to minimize particle aggregation. In addition, the spray deposition technique was carefully optimized with precise control of key parameters such as flow rate and spray distance to achieve a uniform and consistent coating. The samples were functionalized by surface spraying 20 mL of aqueous dispersions containing TiO\({}_{2}\) nanoparticles at a concentration of 16 g/L [31]. The application was performed at room temperature using an air compressor positioned approximately 20 cm from the samples, resulting in a coverage rate of approximately 0.05 mL/cm\({}^{2}\) (Figure 7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemical Compositions (Mass \%)** & **Gray** & **White** \\ \hline O & 50.79 & 45.55 \\ Ca & 29.09 & 41.05 \\ Si & 13.51 & 10.33 \\ Al & 1.76 & 1.69 \\ K & 1.68 & 1.39 \\ Fe & 1.62 & - \\ S & 0.53 & - \\ Na & 0.52 & - \\ Mg & 0.51 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of cementitious samples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Size & 20–40 nm \\ Crystalline phases & Anatase 80\%, Rutile 20\% \\ Band gap & 3.14 eV \\ Purity & \textgreater{}99.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characterization of nano-TiO\({}_{2}\) particles.  
Figure 7: Spraying of an aqueous nano-TiO\({}_{2}\) dispersion.  
Three samples of each substrate were functionalized (G_T16, W_T16). In addition, three untreated samples of each substrate were used as a control (G_Ref, W_Ref). After the functionalization process, all samples were dried in an oven at 30 \({}^{\circ}\)C for 24 h to ensure complete evaporation of the aqueous solvent.  
Figure 8 shows the schematic representation of the overall methodology used in this study. The methods for evaluating self-cleaning are discussed in the following sections.
## 3 Dataset and Methodology
### Active Landforms Detection with DInSAR
The corresponding elevation was determined by means of the SRTM DEM, considering the height of the central point. Figure 2 shows several examples of pattern detection and classification of different landforms. Figure 2A shows a SPOT optical image projected onto a 3D model of the covered area (above) and a six-day interferogram (middle and bottom) of the same area. In this example, the detected patterns correspond to 'El Gigante' rock glacier (\(31\lx@math@degree 52\lx@math@degree 75\lx@math@degree 7\lx@math@degree 7\lx@math@degree 5,70\lx@math@degree 15\lx@math@degree 12\lx@math@degree 5\lx@math@degree 7\lx@math@degree\)W), which is a complex, multi-lobe, and multi-root rock glacier with a complex origin and different states of activity along different ages. It can be observed that only the upper lobes show activity. This indicates that the lower lobes have lower movement rates, which is confirmed by 24-day interferograms. Figure 2D shows a 12-day wrapped interferogram of the west part of the Cordon de La Ramada area (\(31\lx@math@degree 59\lx@math@degree 02\lx@math@degree 50\lx@math@degree 7\lx@math@degree\)S, \(70\lx@math@degree 80\lx@math@degree 44\lx@math@degree 50\lx@math@degree 7\lx@math@degree\)W). In this example, different active landforms with different degrees of phase decorrelation (in most cases indicating different degrees of activity) are identified. It can be observed that glaciers  
Figure 2: Identification and classification of different landforms detected in two different interferograms covering the area of the El Gigante rock glacier (**A**) and the western area of Cordon de La Ramada (**B**). The acronym meanings are: ARG: Active Rock Glacier; DCG: Debris-Covered Glacier; G: Glacier; SP: Snowpatch. (**C,D**) show the interferograms superimposed on a 3D terrain. (**E,F**) show the interferograms in 2D. Light blue polygons belong to the National Glacier Inventory (NGI). and snow patches show higher decorrelation, while rock glaciers and debris-covered glaciers show well-defined movement patterns.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 3 Swarm Robotics Design and Simulation
### Swarm Model Design
#### 3.3.3 Firefly Model  
The firefly model enhances swarm robotics by incorporating communication mechanisms essential for tasks like ore exploration and selective mining. These mechanisms mimic the bioluminescent signals used by fireflies [36; 37], facilitating efficient coordination among robots, particularly in open-pit mining where ore deposits are dispersed and require targeted exploration. In this model, two types of robots are distinguished: a guide robot that emits light using LEDs and a pick-up robot that responds to this signal. The firefly model's communication system allows robots to effectively target and extract high-grade ores, reducing the time needed for further exploration. This coordination is managed by a finite-state machine (FSM) that guides the robots through a sequence of actions, including exploration, light detection, and movement toward the light. Operating under a decentralized control system, the FSM directs the processes of exploration and movement towards light signals, ensuring efficient task execution. The operational flow of the firefly model, as governed by the FSM, is illustrated in Figure 7.