esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 2 Related Work
### Edge Computing and Edge AI  
Edge computing brings data processing closer to the data source by utilizing local devices or edge servers, thereby mitigating the drawbacks of cloud-based systems. In the context of IoT, Edge AI refers to deploying artificial intelligence algorithms directly on edge devices, enabling real-time data analysis and decision-making.  
Recent advancements in Edge AI have demonstrated its potential to enhance IoT applications. A 2024 study highlighted the integration of AI in IoT devices, emphasizing its role in transforming workloads and improving experiences across smart homes [8]. Another 2024 research discussed the challenges and technological advances in privacy-preserving workflows within distributed edge environments, which is crucial for smart home applications [9].  
Federated learning (FL) has emerged as a viable privacy-preserving alternative to cloud-based AI, allowing smart home devices to collaboratively train models without transmitting raw sensor data to a central server [10,11]. This approach mitigates privacy risks and reduces the communication overhead associated with cloud-based retraining. Studies have demonstrated the feasibility of federated learning for IoT cybersecurity, anomaly detection, and privacy-sensitive applications [12]. Additionally, advancements in model quantization and optimization techniques, such as pruning and knowledge distillation, have further enhanced the efficiency of Edge AI for real-time IoT applications [13].  
The integration of Edge AI in IoT systems offers several benefits:* **Reduced latency**: Local data processing enables immediate detection of anomalies, which is crucial for timely responses in smart home applications.
* **Enhanced privacy**: Processing data on-device minimizes the exposure of sensitive information, addressing privacy concerns associated with cloud computing.
* **Lower bandwidth usage**: By analyzing data locally, the need for constant data transmission to the cloud is reduced, conserving network bandwidth. These advantages make Edge AI a compelling approach for implementing anomaly detection in smart home environments.
## 5 Materials and Methods
### Protein Expression, Extraction and Activation  
Primers were designed to clone the novel ICPs genes. SU4_5 (5'-GGA ATT CGA TGA GTC CGA AT ATC AGA AT-3') and 8H_3 (5'-CGC GTC GAC TTA CAT TTC TTC TAC AAT CAA TTC-3') primers were designed to amplify _cry8 Ha1_ gene, while SU4_5 and 8I_3 (5'-CTC ATT TCT ACA ATC AAT TCT ACA CTG TC-3') primers were used to amplify _cry8 la1_ gene. A KOD DNA polymerase and PTC-100 Peltier Thermal Cycler (MJ Research) was used for PCR, comprising 30 cycles (each cycle was composed of 1 min at 94 \({}^{\circ}\)C, 1 min at 54 \({}^{\circ}\)C, and 4 min at 68 \({}^{\circ}\)C) followed by 10 min incubation at 68 \({}^{\circ}\)C. The amplified full-length _cry8 Ha1_ and _cry8 la1_ genes were inserted into the _Eco_RI-_Sal_I sites and _Eco_RI-_Eco_CRI sites of the pSTK shuttle vector, respectively. The plasmids purified from _E. coli_ DH5\(\alpha\) strain were then transformed into _E. coli_ SCS110 for producing nonmethylated recombinant plasmids. Then, the expression vectors containing _cry8 Ha1_ and _cry8 la1_ genes were introduced into the crystal negative Bt strain HD-73' by electroporation [27]. The positive transformant colonies were incubated in LB medium supplemented with kanamycin at 30 \({}^{\circ}\)C until sporulation. The spore/crystal mixtures were collected and washed twice in 1.0 M NaCl and sterile distilled water successively. The final suspension was analyzed by sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) 10% acrylamide gel.  
The crystal/spore mixtures were subsequently solubilized in a pH 10.2 buffer containing 50 mM Na\({}_{2}\)CO\({}_{3}\), 50 mM EDTA, 3% 2-mercaptoethanol, incubated on a rotary shaker at 150 rpm at 0 \({}^{\circ}\)C for 4 h. The supernatant soluble protoxins were separated by centrifugation at 13,500\(\times\)_g_ for 10 min and the protoxins were precipitated by adding 4 M Sodium acetate-Acetic acid (NaAc-HAc) buffer (pH 4.5) until the pH reached 5.0 and then incubated at 4 \({}^{\circ}\)C for 4 h. The protoxin pellet was collected by centrifugation at 13,500\(\times\)_g_ and washed with pre-chilled distilled water three times to remove NaAc and HAc, and then dissolved in 50 mM Na\({}_{2}\)CO\({}_{3}\) (pH 10.2). The protoxins were activated by treatment with chymotrypsin (Sigma) (10:1 w/w). The digestion was carried out in 50 mM Na\({}_{2}\)CO\({}_{3}\) buffer (pH 10.2) at 37 \({}^{\circ}\)C for 2, 12 or 24 h. Reactions were stopped by chilling in ice. The digested proteins were then boiled 3 min with protein-loading dye and analyzed on a 10% acrylamide SDS-PAGE.
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Correlations Relating Climatic Factors, LOS and NPP  
Partial correlation analysis was used to explore the relationships between NPP and climatic factors (mean annual temperature, annual cumulative precipitation, and annual cumulative solar radiation from 2001-2014) as well as LOS, which excluded the effects of other variables. The significance of the correlation coefficients was tested at a significance level of 0.05.  
To understand the direct and indirect effects of climatic factors, LOS on NPP variation (excluding multi-collinearity between climatic factors and LOS), a structural equation model (SEM) with standardized data was applied to examine the influences of climate and LOS change on NPP variation. The SEM was initiated by including all possible relationships. The least significant relationship was then removed stepwise until all relationships were significant and the fit of the model did not increase further [52]. SEM was conducted by the \"sem\" R package [53], which was then visualized by the R package \"semPlot\".
# Data Driven Leakage Detection and Classification of a Boiler Tube
## 4 Performance Evaluation and Results

Table 2: Structure of the adopted deep neural network (DNN). Figure 9: Wavelet features distribution. To endorse the efficacy of the developed model, a comparison of the results was made with the traditional k-nearest neighbors algorithm (k-NN) and the support vector machine (SVM) algorithm. These two algorithms are very popular for developing intelligent systems and industrial equipment fault diagnosis. DNN in the model developed was swapped with the k-NN and SVM algorithms. Both models were provided with the same set of features used in the proposed study. The experiment was repeated multiple times for stable results. The results for the k-NN and SVM algorithms are also listed in Figure 10b. It can be perceived that the performance of the proposed model surpassed that of k-NN and SVM. The average accuracy of the model developed is 99.2%, compared to k-NN and SVM which have 69.35% and 81.85% accuracies, respectively. Figure 10: (a) Confusion Matrix of the proposed model; (b) Results (accuracy (%)) comparison of the proposed boiler tube leakage classification model with other state-of-the-art fault diagnosis models. Furthermore, the results of the proposed model were also compared with published studies on the fault diagnosis of different objects using AE signals. One of the comparisons was based on a study in which a hybrid feature pool, principal component analysis (PCA) and k-NN classifier were used for fault classification of a mechanical system [20]. Similarly, in another published study, genetic algorithm (GA)-based feature selection and DNN were applied for the classification of AE signals obtained from a pressure vessel [21]. The third study consisted of a statistical feature pool (SFP) and support vector machine (SVM) for leakage detection of a spherical tank using AE signals [22]. The comparison results in terms of average classification accuracy and execution time are presented in Table 3. The proposed model had a longer execution time (11.8 seconds) to yield the desired result compared to the other algorithms. However, it is evident from the comparison results that the proposed model outperformed all of the other algorithms. The average classification accuracies of PCA + KNN, GA + DNN, SFP + SVM, and the proposed model are 91.65%, 94.67%, 95%, and 99.2%, respectively. Thus, the improvement in classification performance using the proposed model is at least 4.25% compared to the rest of the algorithms. Therefore, it is obvious that the proposed model is superior for detecting and classifying leaks in a boiler tube.
## 6 Results and Discussion
### RQ2: Does MPPEngine Produce More Patches for Bugs Compared to jGenProg?
While MPPEngine generates patches for 26 bugs out of 27 bugs except for Chart 14, whereas jGenProg generates patches for this bug (Chart 14). On the other hand, MPPEngine produces patches for the following 6 bugs: Chart 12 and Math 20, Math 28, Math 32, Math 49, and Math 50, which have not been repaired by jGenProg. We can witness the detailed results in Table 3. Let us consider the results of Math 28 bug, for which MPPEngine generated 142 test suite adequate \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pairs** & **Mean** & **N** & **Std. Deviation** & **Std. Error** \\ \hline No. of Patches by jGenProg & 44.52 & 27 & 111.43 & 21.45 \\ No. of Patches by MPPEngine & 56.44 & 27 & 102.02 & 19.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Paired sample statistics for the number of patches generated by jGenProg and MPPEngine. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Bug** & **Approach** & **No. of Patches** & **TC Count** & **Patch Diff. Count** & **Better?** \\ \hline \multirow{2}{*}{Math 80} & jGenProg & 39 & 105,032 & \multirow{2}{*}{0} & \multirow{2}{*}{Same} \\  & MPPEngine & 39 & 99,889 & & \\ \hline \multirow{2}{*}{Math 81} & jGenProg & 47 & 116,318 & \multirow{2}{*}{7} & \multirow{2}{*}{jGenProg} \\  & MPPEngine & 40 & 98,021 & & \\ \hline \multirow{2}{*}{Math 82} & jGenProg & 44 & 139,560 & \multirow{2}{*}{0} & \multirow{2}{*}{Same} \\  & MPPEngine & 44 & 130,337 & & \\ \hline \multirow{2}{*}{Math 84} & jGenProg & 1 & 15,928 & \multirow{2}{*}{0} & \multirow{2}{*}{Same} \\  & MPPEngine & 1 & 15,672 & & \\ \hline \multirow{2}{*}{Math 85} & jGenProg & 105 & 316,203 & \multirow{2}{*}{9} & \multirow{2}{*}{MPPEnging} \\  & MPPEngine & 114 & 254,046 & & \\ \hline \multirow{2}{*}{Math 95} & jGenProg & 17 & 50,598 & \multirow{2}{*}{0} & \multirow{2}{*}{Same} \\  & MPPEngine & 17 & 63,641 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._patches whereas, for the same bug, jGenProg executed 244,216 test cases, but no patch was generated in the given time. In another example, for Chart 1 bug, MPPEnigne consumes slightly more test cases but produces four more test suite adequate patches than jGenProg. It proves that our fitness function filters out the best candidates for the re-modification so that our method produced more patches. In RQ3, in fact, we focus only on the bugs repaired by both the approaches in terms of repair effectiveness. It is clear that MPPEnigne performs as good as jGenProg. However, in most cases, MPPEnigne (26/27) outperforms jGenProg (21/27). As a result, the repair effectiveness of jGenProg is 77.8%, whereas MPPEnigne is about 96.3% for the 27 bugs. Here, again, MPPEnigne outperforms jGenProg.
## 2 Method
### SA Monitoring  
Ambient noise in active conditions was recorded over a day of lessons for each hall. The equipment used for these measurements consisted of two sound level meters positioned in the student seating areas.  
Short-term A-weighted equivalent noise levels were recorded every 100 milliseconds to capture background noise during speech pauses. The sound level meters were placed at the center of the lecture halls, maintaining a minimum distance of 1 m from nearby surfaces to avoid strong reflections. The height was set to 1.2 m above the floor, corresponding to the approximate ear level of a seated person.  
The lessons were supervised by an operator positioned among the students. Background noise primarily originated from students' whispering, movement, and conversations. No behavioral observation methods, such as video recordings or questionnaires, were employed as supplementary data to assess the explanatory power of acoustic measurements regarding student behavior and psychological states. The operator recorded the start and end times of the lessons, details on teaching style, and any unusual student behaviors. Pauses between and within lessons, as well as unforeseen noise peaks, were excluded during the post-processing phase.  
Short-term A-weighted equivalent levels Leq were extracted from the recordings, and their occurrences were analyzed using the Gaussian Mixture Model (GMM) method [32]. The probability density functions of Leq occurrences were examined for each lesson ob  
\begin{table}
\begin{tabular}{c c c c} \hline
**Hall** & **Lesson** & **Occupancy \%** & **Recording Time (h:m)** \\ \hline \multirow{4}{*}{A} & 1 & 125 & 01:16 \\  & 2 & 80 & 01:22 \\  & 3 & 60 & 01:38 \\  & 4 & 75 & 01:24 \\ \hline \multirow{4}{*}{B} & 1 & 125 & 01:32 \\  & 2 & 80 & 01:24 \\  & 3 & 60 & 01:19 \\  & 4 & 75 & 01:30 \\  & 5 & 100 & 01:29 \\ \hline \multirow{4}{*}{C} & 1 & 65 & 01:35 \\  & 2 & 50 & 01:30 \\ \cline{1-1}  & 3 & 105 & 01:35 \\ \cline{1-1}  & 4 & 105 & 01:27 \\ \hline \end{tabular}
\end{table}
Table 2: List of monitored lessons, for each lecture hall.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Date** & **Mean ext. T** & **Max ext. T** & **Min ext. T** \\ \hline
27 October & 14.1 & 19.1 & 10.2 \\
30 October & 12.1 & 17.3 & 7.9 \\
31 October & 11.0 & 17.3 & 6.6 \\ \hline \end{tabular}
\end{table}
Table 3: The external values of T, recorded during the days of classroom monitoring.  
served. Since the occurrence curves exhibited two distinct peaks, they were modeled as a mixture of two Gaussian distributions, representing two separate contributions [33]. At each listener position, the Gaussian distribution with higher sound pressure level values was attributed to the teacher's speech level. Conversely, the Gaussian distribution with lower sound pressure level values was linked to student activity.
## 4 Experimental Setup  
### Dataset Setup  
The experimental setup is shown in Figure 11. The transmitted packet constructed from random data was fed into a field-programmable gate array (FPGA, Xilinx Spartan 6, XC6 SLX16). The symbol rate was set from 32 kbaud/s to 46 kbaud/s. We used the output signal from I/O port to drive an RGB-LED. A mobile phone (HUAWEI P20 PRO) with a CMOS-based optical camera was set at the data receiver side after a 40-CM free-space signal transmission. Note that a convex-convex lens and a diffuser were set in front of the phone. For the experiment, the mobile phone records a video at a frame rate of 60 fps, and the resolution is 1080 \(\times\) 1920. For the parameters of an optical camera, the sensitivity (ISO) was set to 250, and the other parameters were set automatically by the software.
# A DEM Study on Bearing Behavior of Floating Geosynthetic-Encased Stone Column in Deep Soft Clays
## 3 Results and Discussion
### Effect of Encasement Length
#### 3.4.1 Effect on Load-Bearing Capacity of FGESC  
Figure 17 shows the load-bearing capacity of the FGESC with different encasement depths. It can be seen that with the increase in the encasement depth, the bearing capacity of the FGESC increased. When the settlement was 18 mm, the bearing capacities of the FGESC with encasement depths of 100 mm, 180 mm, 280 mm, and 360 mm were 58.0 kPa, 64.5 kPa, 73.0 kPa, and 77.4 kPa, respectively. When the encasement depth increased from 280 mm to 360 mm, the bearing capacity increased by 4.4 kPa, which was a small increase.
# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
## 3 Conceptual Design Based on the Theoretical Methodology  
Figure 9 shows the overall block diagram of the sensor fusion method described in Section 2. Two types of homography matrices are estimated by calibrating data obtained from a thermal IR camera, an FMCW radar system, and a 3D ultrasound system. We use the estimated matrices to map data sets of \((u,v)\) corresponding to \((x_{s},y_{s},z_{s})\) and \((x_{r},y_{r},z_{r})\) to a common plane. The object information is then visualized and displayed along with updated \((u,v)\) sets of objects and their distances.  
To implement the methodology described in Section 2 and Figure 9, a hardware sensor system, as shown in Figure 10, was conceptually designed. The conceptual system consists of five 3D ultrasonic modules (the green points of Figure 10), a single thermal IR camera (the red point of Figure 10), and a single FMCW radar system (the blue point of Figure 10). The three types of sensors are related to the sensors in Figure 9. Based on Equation (5), the distance between the ultrasonic sensors in each 3D ultrasonic module is 20 cm. This distance is related to the values of \(`a\)' and \(`b\)' in Figure 4. In addition, the angle \(\theta\) between modules, as shown in Figure 5, was selected as 45 degrees according to Equation (7). Specifications for the selected ultrasonic sensors, thermal IR cameras, and FMCW radar systems in Figures 9 and 10 are given in Table 2. The presented sensor fusion system is currently under construction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensors** & **Category** & **Specification** & **\# of Sensors Used** \\ \hline Ultrasonic sensor & Frequency (kHz) & 40 & \\ (Hargisonic, & Input pulse & TTL or Pulse & \\ HG-M40) & Output signal (V) & 5 & 20 \\  & Detection range (m) & 0.3\(\sim\)3 & \\ \hline  & Field of View (degree) & 19 \(\times\) 76 & \\ FMCW radar & Carrier frequency (GHz) & 24 & \\ (Infineon, & Bandwidth (MHz) & 200 & \\ BGT24 MTR12) & Sweep time (\(\upmu\)s) & 300 & 1 \\  & Max. IF (kHz) & 10 & \\  & Detection range (m) & 3–10 & \\ \hline Thermal IR camera & Focal plane (pixel) & 320 \(\times\) 256 & \\ (FLIR A35) & Pixel space (\(\upmu\)m) & 25 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specification of the chosen sensors.  
Figure 9: Overall block diagram of the proposed sensor fusion.
## 3 Results and Discussion
### Aggregate Properties
#### 3.1.2 Chemical Compositions  
Figure 6 displays the chemical compositions of type A limestone, type B limestone, diabase, granite, and basalt as determined by XRF analysis. The acid-base property of aggregate is one of the key factors affecting the adhesion of an asphalt binder--aggregate system. Magnatic rock's acid-base characteristics are determined by its SiO\({}_{2}\) concentration. A rock is classified as acidic if its silicon dioxide content is greater than 65% and neutral if it is between 52% and 65%. Basic rock and ultrabasic rock have silica contents between 45% and 52%, respectively. Diabase and basalt are hence neutral rocks, whereas granite is an acidic rock. According to the criteria listed above, limestone from sedimentary rock cannot be classified [25]. However, because it contains a lot of CaO, limestone is a basic rock. The chemical compositions and phase compositions of rock aggregates are completely random due to the environmental influence during rock formation. The chemical composition of the same rock varies from one origin to another and from one layer to another. However, the composition of the main minerals of the same rock is generally the same. Therefore, the analytical results of XRD are used in combination with the XRF results.  
Figure 5: Calculation results of shape parameters.  
Figure 6: Proportions of different oxides for the five aggregates.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Tools and Frameworks
#### 3.4.5 Language, Audio and Video Processing  
A range of tools have been applied in MMLA to extract and analyse information from text, audio, and video sources. For natural language processing, models such as embeddings from language model (ELMo) generate deep contextual embeddings that help identify emotional and semantic patterns in educational discourse. Audio-related tools include FFmpeg, which facilitates audio extraction and preprocessing from multimedia files, and Google Cloud Speech-to-Text, which supports automatic speech recognition for analysing classroom interactions. EmotionCLIP enables joint analysis of visual and textual data to infer learners' affective states. On the visual side, OpenCV is widely used for video processing and feature detection, while OpenFace supports facial landmark and gaze analysis. Additionally, OpenPose provides robust body pose estimation, allowing researchers to capture posture and gesture dynamics in educational environments. An overview of these tools and their respective applications is provided in Table 5.
## 2 Material and Methods
### Biodegradable Synthetic Vascular Grafts
#### 2.1.2 Pla  
Poly (L-lactic acid) (PLA) is another biodegradable polymer widely used in tissue engineering. PLA is more hydrophobic than PGA due to the presence of an extra methyl group. Their lower affinity to water leads to longer degradation times (6-12 months) [14]. Thus, studies have reported that PLA takes from months to years to lose its mechanical properties in vivo.  
As a result of its hydrophobic characteristics, it displays a tendency towards early thrombogenicity. Therefore, researchers have tried to reduce PLA's thrombotic tendency through cell seeding and/or making chemical surface modifications. [PERSON] et al. investigated an electrospun, biodegradable PLA TEVG in the arterial circulation of a rat aorta [13]. They suggested that graft patency could be improved by seeding bone marrow mesenchymal cells. The same group saw a 25% improvement in patency when a PLA graft containing hirudin, a thrombin inhibiting polypeptide, was implanted in the abdominal aorta of rats [17].  
Figure 1: Graft classification by scaffold materials in animal model and clinical studies. Various combinations of biological-based scaffolds and biodegradable synthetic-based scaffolds have been developed and evaluated in large animal models and clinical studies. BM-MNCs, bone marrow mononuclear cells; EC, endothelial cell; PCL, poly (\(\varepsilon\)-caprolactone); PGA, polyglycolic acid; PLA, poly (lactic acid); PGS, poly (glycerol) sebacate; SMC, smooth muscle cells.  
Additionally, most biodegradable synthetic arterial TEVGs use electrospun nanofibers to withstand the high pressures of the arterial system. Specifically, we have shown electrospun PLA nanofiber grafts have good mechanical properties with high patency in small animal models [18].  
Unfortunately, thin fibers and small pore electrospun nanofibrous scaffolds inhibit cell migration which leads to delayed neotissue formation. In contrast, thick fiber and large pore electrospun grafts can enhance the neovascularization and remodeling process by mediating macrophage polarization towards the M2 phenotype [19]. However, electrospun grafts with large pores cause significant blood leakage and exhibit premature loss of mechanical properties. Despite success in small animal models, our current application of these designs to the higher pressures of large animal models have been prone to thrombus formation and graft rupture.