esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment and Results
### Localization Result
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Layout 1 (1000 Hz)**} & \multicolumn{2}{c}{**Layout 2 (3000 Hz)**} \\ \hline
**Sound Source** & **Radiated Sound** & **Sound Source** & **Radiated Sound** \\
**Localization (\(x\), \(y\), \(z\))** & **Power Level (dB)** & **Localization (\(x\), \(y\), \(z\))** & **Power Level (dB)** \\ \hline
599, 454, 1644 & 49.33 & 497, 197, 1703 & 82.40 \\
608, 447, 1644 & 49.75 &  
The change of the calculated depth of sound source with time-angle is shown in Figure 9. The x-coordinate in these figures is the time and the corresponding angle of the swing arm calculated from the localization of the proposed method. The depth of the sound source varies from 1598 to 1665 mm in layout 1. The sound source depth value is stable at about 1630 mm, which is consistent with the layout. The depth of the sound source varies from 1651 to 2060 mm in layout 2, the sound source depth changes from far to near, and then from near to far. Although the swing arm shakes randomly and the camera has imaging errors, it can still be seen that this trend is consistent with the trend of the theoretical depth curve of the sound source. Figure 10 shows the curve of the sound pressure level (SPL) of the channel at the center of the array versus time-angle. The SPL of layout 1 is within 27.36-29.43 dB, and the range of layout 2 is 53.42-59.48 dB. In layout 1, since the sound source depth is relatively stable, the value of SPL is also relatively stable. Due to the direction of the source and the reflection from the floor, when the Bluetooth speaker at the end of the swing arm rotates to certain angles, the sound pressure level of some channels in the array changes significantly in layout 2 (e.g., the sound pressure level at 1.2 s). However, according to the sound pressure level fitting curve, it can be seen that the overall trend is decreasing with the increase of the distance. By combining the sound source depth change and the sound intensity scaling method, the sound power level versus time-angle curve in Figure 11 is obtained. The sound power level range of layout 1 is stable at 81.02-82.48 dB. The sound power level is within 81.02-82.48 dB in layout 2, and is more stable than the sound pressure level. The sound intensity scaling method is to scale the entire array signal, and the influence of abnormal signals of certain microphones is reduced on the sound intensity of the entire array.
## 4 Results and Discussion
### Verification of CFIP Model
#### 4.3.3 Simulation  
The AE wave shown in Figure 3a was chosen as the model for the simulation. The frequency components and their observed weights are listed in Table 1. In the simulation, these data were introduced into Equation (71). Figure 6a shows the simulated \(u_{z}\) displacement and corresponding FFT spectrum for D0.2P4n10.001 (FRS = 250 N/m\({}^{2}\)) using the eight frequencies listed in Table 1. Since \(g(\xi,\vartheta,\eta)\) diverges to infinity at \(\vartheta=0\), we set the minimum angle to 0.001\({}^{\circ}\) in the simulation. As shown in Figure 6b, the FFT spectrum of the simulated AE wave is in good agreement with the observed spectrum (Table 1).  
Here, we demonstrated the validity of our approach by evaluating the multi-frequency \(u_{z}\) as a function of \(\vartheta\) for angular dependence and a function of \(\eta\) for axial dependence. The maximum value was taken from the simulated \(u_{z}\) displacements. For the angular dependency, both experimental values and calculated values (after baseline correction) were converted to relative values for a value of \(\vartheta=120^{\circ}\). A baseline correction to the experimental values was necessary to match the two values, because the calculated minimum was nearly zero (Figure A4). As shown in Figure 7, the results for the angular dependency show good agreement between the observed and simulated values.  
For the axial dependence, the simulation was also performed at \(\vartheta=0.001\) instead of \(\vartheta=0\). Simulated values given in meter were converted to those given in mV by multiplying the appropriate factor. The results for D0.2 and D1.2 are shown in Figure 8, and the others  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(v_{i}/\)kHz & 70.2 & 102.3 & 120.8 & 145.6 & 173.4 & 244.5 & 270.3 & 284.5 \\ \(w_{\text{emp},i}\) & 0.091 & 0.045 & 0.069 & 0.18 & 0.45 & 0.097 & 0.015 & 0.058 \\ \hline \end{tabular}
\end{table}
Table 1: Frequency components and their weights used in the simulation.  
Figure 6: (**a**) Displacement field, \(u_{z}\), simulated with \(\text{FRS}=250\,\text{N}/\,\text{m}^{2}\) and \(\kappa_{z}=9\), and (**b**) corresponding FFT spectrum.  
Figure 7: Relative values of observed mean amplitudes (solid circles) and \(\sigma\)s (bars) of simulated maximum \(u_{z}\) displacements (lines): (**a**) D0.2P4n1 (black) and D0.3P4n1 (red), (**b**) D0.5P4n1 (black), D0.8P4n1 (red) and D1.2P4n1 (green). In the simulation, \(\kappa_{z}=9\) and \(\text{FRS}=250\,\text{N}/\,\text{m}^{2}\).  
are shown in Figure A5. When \(\eta\) approaches zero, there is a sudden increase in the observed AE amplitude. The experimental results show that the AE signal increases abnormally when approaching the leakage point.
## 3 Results and Discussion
### Reactive Azo Dye Oxidation
#### 3.2.7 Data Comparison with Previous Fenton Studies  
A comparison of various data presented in previous studies cited in the literature using the Fenton reaction to treat various types of dyes was carried out, and the results were compared with those of the current investigation. The comparison study is based on the modified Fenton system to examine the adequacy of the modified current study, and the comparison data are exhibited in Table 5. Regarding the oxidation removal efficiencies of the different processes, according to the data tabulated in Table 5, the Fenton reagent could achieve almost complete oxidation of various dye types. However, some systems are characterized by a low efficiency, such as the H\({}_{2}\)O\({}_{2}\)/Fe-zeolite and H\({}_{2}\)O\({}_{2}\)/Co-Fe\({}_{2}\)O\({}_{4}\) systems. Additionally, it is worth mentioning that, in the current study of the modified Fenton system based on the use of Fullers' earth as a naturally occurring catalyst, only a limited time is needed in the current investigation for the reaction (20 min) compared to the other systems. Moreover, the current modified Fenton system is an efficient, superior mode of treatment since it is based on a naturally abundant material that is considered a minimal or costless catalyst. Thus, this current suggestive study is much better and cheaper, especially when the Fullers' earth catalyst is used as the source of the photo-Fenton system. Additionally, the process is environmentally friendly compared to the other techniques listed in Table 5, as they are not based on the use of naturally occurring substances as catalysts.  
Although the other oxidation reactions also exhibited high dye removal efficiencies that almost resulted in complete removal, it is significant to mention that the catalyst source in the other studies was a metal-based synthesized catalyst. In the current study, the catalyst was based on a naturally abundant material. The efficiency of the current method being higher than that of the other Fenton technologies in Table 5 might also be associated with the occurrence of multiple metals in the catalyst, which increases the oxidation rate. Further, the catalyst dose in the current investigation was quite reasonable in comparison to that in the other studies, and the hydrogen peroxide reagent was not quite as high, with some studies reaching 2720 mg/L. However, in the other studies, only a minimal amount of hydrogen peroxide was essential, reaching 0.68 mg/L. Moreover, a higher reaction time was essential, reaching 150 min with a treatment efficiency of 79%. Additionally, it is worth mentioning that such data are attained when applying a costless, naturally abundant catalyst in treatment in comparison to the other mentioned systems in Table 5. Typically, the use of natural materials is superior in treatment due to their advantages of having environmentally benign characteristics.  
Figure 12: Catalyst reusability efficiency for cyclic use.
## 4 Results and Discussion
### Consensus on Challenges
#### 4.1.8 Environment Related Challenges  
AR implementation highly depends on an internet connection. Signal coverage in rural areas is a challenge that is crucial for the AR application, and without it, no synchronized communication between stakeholders can occur [35; 39]. Other weather and environmental issues have been reported to affect the use of AR, for example, noise and heat. Although these challenges seem to have been encountered in real time during one uncontrolled environment study, they were, in fact, also mentioned in other papers.
# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 2 Related Work
### Cloud-Edge Collaboration  
To reduce the burden of DNN inference on edge devices, the computationally intensive parts of the inference process are offloaded to cloud servers [9; 10; 11; 20; 21]. In [9], a full offloading approach, in which multiple variants of the DNN model were created, was proposed to facilitate the selection of different models based on the device's resource constraints; however, with limited resources, the inference performance of the selected model inevitably declined to some extent. In [10], a partial offloading solution was introduced to identify an intermediate partition point within the DNN structure; the initial layers were processed locally, whereas the subsequent layers were offloaded to the cloud. In [11], a method similar to that in [10] was proposed, focusing on mapping different components of the DNN to various layers in the cloud-edge hierarchy to reduce communication overhead and optimize resource usage. However, unlike our proposed method, the aforementioned approach required retraining the DNN model across different levels. In [20], a context-adaptive and dynamically composable DNN deployment framework was proposed. In this work, DNN models were pre-divided into DNN atoms, and during subsequent inference, offloading plans were adaptively formulated to reduce latency and save memory. The study in [21] explored how to achieve rapid DNN inference in UAV swarms through collaboration among multiple UAVs. The DNN model was first partitioned into multiple segments, with each UAV performing inference on a specific segment, and the results were then aggregated. Overall, these studies were focused on leveraging cloud servers to ease the inference load on edge devices. However, cloud-edge collaborative approaches exhibit bandwidth limitations in edge networks, leading to high round-trip latency and data privacy concerns. Therefore, cloud-edge collaborative methods are not suitable for edge-computing systems that demand strict real-time performance.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 6 Conclusions  
The potential coverage at reasonably high rates exhibited by IEEE 802.11 ah makes it an attractive alternative in fulfilling the needs of future IoT communications. In this article, we provide a comparison between different technologies contending to cover the IoT communications framework, and thus indicate IEEE 802.11 technology as one of the strongest contenders.  
We evaluate the main characteristics and benefits provided in terms of throughput and transmission range by the most notable IEEE 802.11 specifications compared to IEEE 802.11 ah amendment. The analysis of the results presents IEEE 802.11 ah with more than 8 times improvement in coverage range against any other IEEE 802.11-based amendment and shows that it can provide throughput close to 100 kbps in the worst case, which is enough to cover most IoT applications.  
We give a thorough analysis of the requirements of many typical IoT applications (classified as permanent connectivity, event-based applications, audio, video, data and biometrics), assessing the number of supported devices per AP, with up to 1 km of coverage. In the cases where the required coverage distance is larger than 1 km, IEEE 802.11 ah can be used to build a multi-hop distribution system.  
We also provide an analysis of the implementation and infrastructure costs that make IEEE 802.11 ah very attractive in front of other IEEE 802.11 specifications and competing wireless technologies. Overall, the expected performance of IEEE 802.11 ah asks for a remarkable place in the IoT landscape.
# Heavy Metal Ion Detection Platforms Based on a Glutathione Probe: A Mini Review
###### Abstract  
Globally, heavy metal ion (HMI) contamination is on the rise, posing an ever-increasing risk to ecological and human health. In recent years, great research effort has been devoted to the sensitive detection and quantitative analysis of HMIs. Low cost, sensitive, selective, and rapid methods for HMI detection are of growing demand, and HMI biosensors have great potential in meeting this need due to their timeliness, cost-effectiveness and convenience in operation. Glutathione is known for its strong ability to bind with toxic heavy metal ions, in addition to its water solubility, stable activity and ready availability. As a result, glutathione is becoming a molecular probe of choice in the preparation of sensors for sensitive, affordable, and accessible HMI detection. This review summarizes the results from various glutathione-based HMI detection strategies reported in recent years, which are categorized according to their signal transduction methods. Their operation and implementation, along with figures of merit such as limit of detection, selectivity, and response time, are discussed and compared. Based on the review, both individual HMI detection and simultaneous detection of multiple HMIs can be realized under specific reaction conditions, showing the great potential of glutathione-based detection to realize various types of practical HMI detection.  
heavy metal ions; glutathione; biosensor; nanoparticle +
Footnote †: journal: Applied Physics Letters B
## 3 Results and Discussion
### Biomass Gasification  
Figure 8 shows the producer gas composition from gasification of pelletized olive cake and chipped pruning for different moisture contents. The results indicate that the moisture content affects the composition of the producer gas. In particular, the H\({}_{2}\) and CO\({}_{2}\) concentrations increase with higher moisture content, whereas the N\({}_{2}\) concentration decreases with higher moisture content. The CH\({}_{4}\) concentration is virtually constant and considerably lower. It is interesting to note that the CO formation decreases significantly with higher moisture contents of the biomass feedstock. Once the volume flow rate of producer gas consumed by the engine-generator set to produce the required electric power is known, the biomass consumption needed to produce the required amount of gas can be calculated. The results are reported in Table 2, where the biomass consumption was determined by the required electrical power output and the gas yield was calculated for each biomass feedstock (olive cake pellets and olive pruning chips) at several levels of moisture content ranging from 5 to 20%. When biomass contains high levels of moisture, the water present in the biomass absorbs a significant amount of heat during gasification, which increases the feedstock consumption, decreases the producer gas yield, and affects the producer gas composition.  
Figure 8: Producer gas composition from gasification of olive cake pellets (**left**) and olive pruning chips (**right**) for different moisture contents.
## 3 Research Analysis
### Application Domains  
AR is widely applied in ACE education for general (50%) and domain-specific learning (50%) (Figure 4). Related papers include an overview of academic performance using AR, and discuss how this type of technology can improve students' 3D spatial skills in tools, geometries, representations, and graphics [24; 35]. By using AR to understand domain-specific contents of textbooks and assignments, students can reduce the cognitive gap between academics and practice in construction, estimation, structure analysis, exterior wall design, building design, and assembly [28; 36]. The most applied course is \"informatics tools,\" followed by \"construction methods and materials.\" \"Informatics tool\" refers to a general computer tool, such as 3D modeling. Students can take this class to create 3D models for a real representation of an object or a space to improve a student's 3D spatial skills. The goal of  
Figure 3: The number of studies published by year.  
Figure 2: The number of studies published by country and regions.  
the \"geometry course\" is to learn how to develop skills to understand visual perception and spatial expression [37; 38].  
Domain-specified or general applications of AR knowledge vary in emphasis on a course or computer skill. The former relates to a domain that applies AR technology, in which a course number is usually given under a common name, such as \"Construction methods and materials\", as part of the \"interface\" using AR to deliver knowledge. The latter relates to a course that supports other courses, such as \"3D modeling\", as a computer-related skill without a specific application goal. In general, the latter is usually given in a computer science department related to hardware, interface, or computer graphics. A similar course can also be offered by a subject's department for more direct learning support.  
The terms used to name a course can differ by country, university, and department. In general, the major part of a course is similar. The application of AR across departments can be related or referred to as a fundamental requirement for students to enhance their computer knowledge in general, or as a skill or tool learned without a specific course in mind, such as an informatics tool, geometry course, representation system, engineering graphics knowledge, graphical and spatial applications, etc. It can also be given as an assignment in which a domain-specific subject can be applied and learned, such as construction methods and materials, exterior wall design, history of western architecture, primary structure analysis, building design and assembly, etc.
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 1 Introduction
In this paper, based on the existing image quality evaluation methods, we draw the human visual attention region mechanism and the HVS characteristics into the no reference image quality assessment method. Then, we propose a universal no-reference image quality assessment method based on visual perception. The rest of this paper is organized as follows. In Section 2, we describe the current research about no reference image quality assessment methods and visual saliency. In Section 3, we introduce the extraction model of visual region of interest. In Section 4, we provide an overview of the method. In Section 5, we describe blind/reference image spatial quality evaluator (BRISQUE) algorithm and the prediction model is provided in Section 6. We present the results in Section 7, and we conclude in Section 8.
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 4 Discussion
### Eigenvalue Extraction from Sub-Squares  
In analyzing L-shaped membrane vibrations, the domain decomposition method offers powerful strategies for eigenvalue extraction [45]. The 3-SDD and 2-SDD strategies enable the extraction of specific eigenvalues from the exact spectrum of rectangular or square membranes when the subregions' side length ratios are rational. This extraction process, governed by the following relation [46], proves particularly effective for the symmetric case:  
\[k_{m,n}^{2}=\pi^{2}\Bigg{[}\left(\frac{m}{L_{a}}\right)^{2}+\left(\frac{n}{L_ {b}}\right)^{2}\Bigg{]}, \tag{29}\]  
where \(k_{m,n}^{2}\) represents the eigenvalue; \(m\) and \(n\) are positive integers denoting the mode numbers; and \(L_{a}\) and \(L_{b}\) are the length and width of the rectangular membrane, respectively.  
The 3-SDD strategy, which divides the L-shaped membrane into three equal-sized square submembranes, allows for a straightforward correlation with the eigenvalues of a single square membrane. As seen in Table 3, the third eigenvalue (\(k_{3}^{2}=19.7392\)) corresponds to the fundamental mode (\(m=1\), \(n=1\)), while the eighth and ninth eigenvalues (\(k_{8}^{2}=k_{9}^{2}=49.3480\)) align with the second and third modes (\(m=1\), \(n=2\) and \(m=2\), \(n=1\)), respectively.  
In contrast, the 2-SDD strategy, which partitions the membrane into one rectangular and one square submembrane, necessitates a more intricate process. It requires computing eigenvalue spectra for both shapes and a meticulous cross-comparison to identify shared values. This method also demands the removal of certain eigenvalues from the rectangular  
Figure 3: Comparison of the first six eigenmode shapes for the asymmetric case: (**a**) 1 st mode; (**b**) 2 nd mode; (**c**) 3 rd mode; (**d**) 4 th mode (**e**) 5 th mode; (**f**) 6 th mode [38].  
submembrane's spectrum, such as those corresponding to modes (\(m=1\), \(n=1\)), (\(m=3\), \(n=1\)), and (\(m=1\), \(n=2\)).  
While both strategies yield accurate results, the 3-SDD method offers a more direct and elegant solution for handling these \"extra\" eigenvalues, highlighting its computational efficiency in this particular scenario. Moreover, the 3-SDD strategy demonstrates superior capability in identifying repeated roots or degenerate modes (i.e., those with identical eigenvalues [45; 47]). This advantage is particularly significant in nanomechanical mass spectrometry, where degenerate mechanical modes are standard in bi-axisymmetric resonators [48]. The 3-SDD strategy's efficiency in this regard could lead to more precise calculations of analyte mass and adsorption positions, potentially improving these sensing devices' overall performance and reliability.  
Ultimately, while the choice between 3-SDD and 2-SDD strategies depends on specific problem characteristics and the desired balance between simplicity and versatility, the 3-SDD strategy's advantages in handling degenerate modes position it as a promising tool for advancing the field of nanomechanical mass spectrometry and sensor design.
# LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
## 2 Materials and Methods  
In this paper, a neural network-assisted visual-inertial person localization method is proposed. The overall framework of the system is shown in Figure 2, which contains 3 parts: data preprocessing, initialization, and sliding window nonlinear optimization mechanism to estimate the positional posture. Based on RoNIN, a harness is used to attach the camera device to the body and an IMU is placed on the wearable undershirt to collect 6-dimensional inertial data. Running visual-inertial SLAM with the camera device on the body generates ground truth information (containing position information, velocity information, acceleration data, and gyroscope data). In this paper, the generated ground truth information is used as the label, the IMU data on the wearable undershirt as the training set, and the trained LSTM neural network is used to regress the 6-dimensional IMU data \((a,\omega)\). Each network window is one window with N frames of IMU data, and each frame of IMU data is 6-dimensional. The input dimension of the network is \(N\times 6\), which consists of N samples of IMU data in gravity-aligned frames. Each window of the network output is N 6-dimensional IMU data, and two-position loss functions are proposed to improve the training accuracy. Finally, using vision as the observation quantity, point features and trained IMU data are combined in the sliding window to perform nonlinear optimization to solve the inertial measurement error caused by the moving base, thus improving the localization accuracy.  
Figure 1: Wearable visual-inertial positioning undershirt equipment.