esa-sceva/satcom-chunk-collection huggingface dataset:
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 5 Experimentation and Evaluation
### Experimentation Settings  
We conducted experiments using 802.11n enabled off-the-shelf WiFi devices. Specifically, we used a Lenovo laptop as a receiver equipped with an Intel 5300 network interface card and an Ubuntu 11.04 LTS operating system to collect CSI data. The laptop connects to a commercial WiFi Access Point (AP); TP-Link router as transmitter operating at 2.4 GHz. The receiver can ping the AP at rate of 80 packets/s. The transmitter has single antenna, whereas the receiver has three antennas, that is, \(\mathbb{N}_{Tx}=1\) and \(\mathbb{N}_{Rx}=3\) (\(1\times 3\) MIMO system) generating 3 CSI streams of 30 subcarriers each. We run 802.11n CSI Tool [45] on the receiver to acquire and record CSI measurements on 30 subcarriers of 20 MHz channel. The required signal processing is performed using MATLAB R2016a.  
To evaluate the robustness of our proposed scheme, we choose following three scenarios:  
* Scenario-I (Indoor environment)--In this scenario, all prescribed gestures are performed in an empty room of size \(11\times 12\) feet, while sitting on a chair between Tx and Rx, separated by a distance of 2 m.  
* Scenario-II (Vehicle standing in a garage)--In this scenario, all gestures are performed in a vehicle standing in a garage of size \(14\times 16\) feet.
* Scenario-III (Actual driving)--In this scenario, all prescribed gestures are performed while driving a vehicle on a straight road of 30 km inside university campus, with average speed of 20 km/h. During gesture performance, no other activity is performed to avoid interference.  
For in-vehicle scenarios, we set up our testbed in a local manufactured vehicle which was not equipped with pre-installed WiFi devices. Due to the unavailability of the WiFi access point in our test vehicle, we configured the commercial TP-Link router as AP, placed on the dashboard in front of the driver. The receiver was placed at the co-pilot's seat to collect CSI data.  
In each experiment, 16 possible human gestures, as shown in Table 1, were performed by five volunteers (2-females and 3-males university students). Each volunteer repeated all gestures 20 times for each experiment, and a single gesture was performed within a window of 5 s. In total, the data set comprised of 1600 samples (5-volunteers \(\times\) 16-gestures \(\times\) 20-times repeated) for each experiment; of which 50% were used for training and 50% for testing. In our experiments, the training data do not contain the samples from the testing data, and we keep the testing samples out for cross validation. Furthermore, we also tested the generalization of our model using an unbiased Leave-One-Participant-Out Cross-Validation (LOPO-CV) scheme.
## 4 Discussion
However, the use of volumetric reconstructions of the entire CBCT volume was reported. In our study, segmentation techniques were applied in order to obtain selective information on the structures of interest. Several studies reported on the application of different segmentation techniques. [PERSON] et al. [21] reported the use of a morphological contour interpolation algorithm for the reconstruction of synthetic contours and anatomical structures following guided bone regeneration. [PERSON] et al. [22] segmented the mandible, teeth, maxilla, and zygomatic bones using an algorithm which reached an accuracy of 0.5 mm. [PERSON] et al. [23] applied content-based image retrieval for the assessment of maxillofacial lesions. However, when compared to manual segmentation, automated methods appeared slightly less accurate in feature extraction. [PERSON] et al. [24] employed standardized threshold segmentation to generate 3D surface models of the maxilla and the mandible with voxel-based superimposition, and reported this method to be fast and accurate. [PERSON] et al. [25] applied segmentation techniques to three different CBCT datasets acquired with different protocols, and found high accuracy for tooth segmentation as compared to anatomical tooth morphology. Another ex-vivo experience was reported by [PERSON] et al. [26], who employed mandibular canal tracking to locate and trace the nerve canal in order to improve surgical performance. Although the variability in the protocols and the limited number of adequate studies hinders the drawing of firm conclusions on the validity and reliability of CBCT-generated 3D models [13], overall it seems that image post-processing may be advantageous in providing additional information, especially in cases of presurgical assessment. In our study, we employed semiautomatic segmentation as it combines the efficiency and repeatability of automatic segmentation with manual outlining of the region of interest [25]. The use of ITK-SNAP for segmentation has been previously reported and validated, being a highly reliable and efficient alternative to manual tracing [26; 27]. With a 3D image-based planning software, tracking of the inferior alveolar nerve canal course can be obtained, creating a virtual replica of the canal, which can give information on shape, curve, direction, and diameter of the nerve. The present study has some limitations. Firstly, it was retrospectively performed on imaging datasets in the absence of clinical correlation. Secondly, manual segmentation requires training and, although supported by automatic methods, it could require some experience in image post-processing. Nonetheless, these preliminary results suggest that the presurgical evaluation through semiautomatic segmentation of impacted third molars and the surrounding structures may be effective in delineating the relative relationship between third molar roots, inferior alveolar nerve canal, and mandibular cortical bone.
# Brain-Inspired Data Transmission in Dense Wireless Network
## 3 Ultra-Dense Wireless Networks and Their Biological Counterparts
### The Bio-Inspired Vision of the Dense Wireless Network  
As mentioned previously, we consider a highly dense wireless network, where each wireless node may act as a relay. Although the mechanisms known from HNS have been already widely in communications, it was done mainly in the context of wired connections. Moreover, inspirations of neural networks are successfully applied in practice as a highly efficient and reliable tools in the artificial intelligence domain. However, the main idea of the authors was to project the nervous system onto a wireless telecommunications network, which is not that straightforward [13; 18]. Summarizing our prior findings, we assumed the following relations, which specify the rationale of our proposed highly simplified transmission scheme:  
* This idea treats a single network node as a neuron, thus simplifies its functions to a minimum, i.e., to the simple transfer of information. Consequently, no channel coding is applied, and no retransmission is allowed in the normal functioning of the network. The transmit power is reduced to the required minimum, and the message is treated as delivered correctly when the required signal power (over noise and interference) is observed at the reception node. In detail, we assume the required threshold for the signal-to-interference-plus-noise (SINR) level, necessary to demodulate transmitted signals. Furthermore, typically, the simplest modulation schemes are considered (e.g., binary or quadrature phase-shift keying, denoted as BPSK and QPSK, respectively).
* The proposed network includes additional devices that reflect the functioning of the microglia in the neural system. These are redundant devices that do not take part in the transmission and are designed to support the transmission that takes place between the basic network nodes (neurons).
* The transmission within the network is not controlled centrally. Moreover, the nodes transmit their messages immediately when the message is ready for further transmission. Consequently, it may happen that many signal transmission may be initiated by various nodes deployed in a certain area resulting in performance degradation of the network due to increased interference power.  
One may observe that the above assumptions relax numerous solutions applied typically in contemporary wireless systems, such as advanced channel coding. They simplify the processing (and finally energy consumption) in the network but may result in deterioration of the overall system performance. Thus, it is mandatory to verify whether the presented inspirations guarantee the required level of performance of the wireless transmission system. Thus, the ultimate goal of our study is to consider a highly simplified transmission scheme and check how it influences the system reliability.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
###### Abstract  
The Global Satellite Navigation System (GNSS) used in various location-based services is accurate and stable in outdoor environments. However, it cannot be utilized in an indoor environment because of low signal availability and degradation of accuracy due to the multipath distortion of satellite signals in urban areas. On the contrary, LTE signals are available almost everywhere in urban areas and are quite stable without much variation throughout the year. This is because of the fixed location of base stations and the well-maintained policy of mobile communication service providers. Its varied stability and reliability make LTE signals a more viable method for localization. However, there are some complexities in utilizing LTE signals including signal interference distortion phenomena during propagation multipath fading, and various types of noise. In this paper, we propose a surface correlation-based fingerprinting method to utilize LTE signals for localization in urban areas. The surface correlation converts timely measured signal strength into spatial pattern using the walking distance from a Pedestrian Dead-Reckoning (PDR). The surface correlation is carried out by comparing the spatial signal strength pattern of a pedestrian's movement trajectory with a fingerprinting database to estimate the location. A reference trajectory of the moving pedestrian is chosen to have a greater correlation among the multiple trajectory candidates generated from a link-based fingerprinting database. By comparing spatial signal strength patterns, the proposed method can improve robustness in localization overcoming the accuracy degradation problem due to RF multipath and noise that are dominant in the conventional RSS measurement-based LTE localization scheme. The test results in urban areas demonstrate that the proposed surface correlation-based fingerprinting method has improved performance compared to the other conventional methods, thus proving to be a useful complementary method to the GNSS in urban areas.  
localization; fingerprinting; LTE; correlation; pedestrian; PDR +
Footnote †: journal: Sensors
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 3 Experiment and Analysis  
### Experimental Data Collection  
Experimental data collection was carried out using experimental equipment at multiple experimental sites. This paper designed three experiments in different scenarios: between trees, on the roadside, and indoors, as shown in Figure 2. These three scenes have different environmental noise or interference, bird calls, car honking, and multi-path propagation interference. The experimental site and equipment are shown in Figure 3.  
A total of approximately 10,000 s of audio data was collected, encompassing various UAV sounds and environmental noise. The data specifications are as follows: a sampling rate of 48,000 Hz, a bit depth of 16-bit, and acquisition via the LM111 acoustic sensor. The sound sources include multiple UAV models from the laboratory: DJI Phantom 4 Pro V2.0, DJI AIR 2S, and DJI Spark. All recordings are labeled with corresponding distance metadata.  
\begin{table}
\begin{tabular}{c c c} \hline
**Layer Level** & **Layer Name** & **Layer Type** \\ \hline
5 & maxpool1 & 2-D data max pooling layer \\
6 & conv2 & 2-D data convolutional layer \\
7 & bn2 & Batch normalization layer \\
8 & relu2 & Activation layer \\
9 & fc1 & Full connected layer \\
10 & relu3 & Activation layer \\
11 & fc2 & Full connected layer \\
12 & softmax & Softmax function \\
13 & classification & Output of classification \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Experimental equipment: (**a**) Sound recording equipment (**b**) Flying UAV.  
Figure 2: Experimental places: (**a**) Between trees, (**b**) Roadside, (**c**) Indoors.  
For the DJI Phantom 4 Pro V2.0, audio data includes hovering at medium-to-long distances of 40, 50, 60, and 70 m, as well as hovering at long distances of 100, 110, and 120 m. The DJI AIR 2S dataset comprises hovering recordings at 1 m intervals from 5 to 20 m and additional recordings at 10 m intervals from 40 to 100 m. The DJI Spark data includes hovering sounds at distances ranging from 30 to 70 m. The volume of UAV sound data recorded in the experiment is shown in Table 3.  
The waveform and spectrogram plots for the three UAVs are shown in Figure 4. The three upper plots (a,b,c) represent the acoustic waveforms of hovering flight sounds from different UAVs, while the three lower plots (d,e,f) display the corresponding spectrogram plots of the signals. The left plots (a,d) correspond to the DJI Air2S UAV, the center plots (b,e) to the DJI Spark UAV, and the right plots (c,f) to the DJI Phantom4 Pro UAV.
# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
## 3 Results
### Mode Curvature Difference as CNN Input  
Because the mode curvature difference is more distinguishable at the damage location than the mode shape, it is used as the CNN input and the locating accuracy is investigated.  
Through many trial calculations, it is found that the increase of the convolutional layers has slight effect on the training results. Figure 6 shows the training process for the convolutional layers being 1, 2, 3, and 4, respectively, the stride is 1 and there are no pooling layers.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Prediction Accuracies**} \\ \hline \multirow{2}{*}{**Type**} & \multicolumn{6}{c}{**Damage Degree**} \\ \cline{2-9}  & **0\%** & **4\%** & **6\%** & **10\%** & **30\%** & **50\%** & **70\%** & **90\%** \\ \hline Front lower rod & & 100\% & 100\% & 100\% & 100\% & 100\% & 92\% & 89\% \\ Front upper rod & & 100\% & 100\% & 100\% & 100\% & 100\% & 96\% & 92\% \\ Front vertical rod & & 100\% & 92\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ Front diagonal rod & & 100\% & 100\% & 100\% & 100\% & 100\% & 96\% \\ Rear lower rod & & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% & 96\% \\ Rear upper rod & & 50\% & 46\% & 38\% & 42\% & 46\% & 42\% & 46\% \\ Rear vertical rod & & 63\% & 77\% & 81\% & 96\% & 92\% & 92\% & 92\% \\ Rear diagonal rod & & 92\% & 92\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ Transverse lower rod & & 58\% & 58\% & 44\% & 65\% & 48\% & 58\% & 34\% \\ Horizontal upper diagonal rod & & 96\% & 92\% & 96\% & 100\% & 100\% & 100\% & 100\% \\ Transverse upper rod & & 14\% & 25\% & 37\% & 63\% & 81\% & 77\% & 40\% \\ Horizontal lower diagonal rod & & 84\% & 96\% & 80\% & 92\% & 96\% & 92\% & 100\% \\ Transverse diagonal rod & & 66\% & 63\% & 55\% & 55\% & 51\% & 85\% & 74\% \\ Overall & 100\% & 79\% & 80\% & 79\% & 85\% & 85\% & 87\% & 81\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prediction accuracies of the first order mode shapes.  
Figure 6: Training processes.  
Finally, the CNN with only one convolutional layer is selected for damage detection and the convolution kernel size is \(1\times 1\). As shown in Figure 6, the network reached a high accuracy after 100 iterations. As for its test sets, the prediction accuracies are shown in Table 2:  
Table 2 shows that, selecting the first order mode curvature difference as the CNN input is unable to correctly distinguish the intact and damage scenarios sometimes. Its damage prediction accuracy is only 33% in the low damage degree (4%), but the prediction accuracy increases with the increase of damage degree, the accuracy reaches the maximum value of 90% when the damage degree increases to 50%. For the rods around the observation points, such as the front lower rod, the front upper rod and the front diagonal rod, the damage prediction accuracy is also significantly higher than other rods.
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
## 2 Materials and Methods
### Preparation of HDPE Samples  
Two types of contaminated plastic matrices were selected for experimental evaluation. The first group consisted of six samples extracted from the internal side walls of three gray HDPE tanks, each with a capacity of 1000 L. These tanks had been in service for seven years and had been exposed to the environment for more than two years without any subsequent maintenance or intervention. The second group comprised six samples from three white HDPE tanks of the same brand, capacity, and service duration but with significantly less environmental exposure, under two months, referred to as non-weathered.  
Both tank types exhibited visible diesel fuel residues, remained intact (without perforations), and had functional closure systems. All were retrieved from authorized industrial disposal sites in the Metropolitan Region of Santiago, Chile. Sampling focused on the lower third of the internal side walls, where higher accumulation and adherence of hydrocarbon contaminants were observed.  
Additionally, six control samples were obtained from a new, uncontaminated gray HDPE diesel fuel tank of identical characteristics. All samples were processed in triplicate. In total, 18 sets of three circular plastic discs, each 65 mm in diameter, were prepared using a carbon steel hole saw attached to an industrial drill, ensuring uniformity in dimensions and exposed area.  
Prior to inoculation with _Aspergillus flavus_, all samples underwent a microbiological decontamination protocol using ultraviolet (UV) radiation, with 48 h exposure on each side. The effectiveness of UV-C decontamination (254 nm, 30 min per side) was verified by incubating a subset of sterilized HDPE discs on PDA without inoculation for 7 days at 25 \({}^{\circ}\)C. No microbial growth was observed, confirming the efficacy of the decontamination procedure. This procedure, based on previous studies [16; 19; 20] aimed to eliminate pre-existing microorganisms and ensure that observed growth was exclusively attributable to the inoculated strain.
## 2 Wave Model and Wave Force Calculation  
In this paper, the vibration characteristics of offshore wind turbines under the influence of wind and waves are mainly studied. As is shown in Figure 1, the influence factors of wind include wind shear, tower shadow and turbulent wind, and the influence factors of waves include wave height and wave period. Some studies focus on the vibration characteristics of wind turbine under the influence of wind, especially the impact of wind shear and tower shadow [2; 3]. To suppress the wind turbine vibration caused by wind, several control strategies have been studied [5; 8; 14; 17]. The influence of waves on the vibration characteristics of the wind turbine needs further study, and the control strategy to suppress the vibration of the wind turbine caused by wind and waves needs to be proposed. To this end, a wave model and wave force calculation are studied.  
There are two main research directions in the theory of wave modeling. One is regular wave theory, which uses hydrodynamics to reveal the motion of material particles in liquid. The other is random wave theory, which shows the distribution characteristics of wave energy in sea waves by studying the randomness of sea surface fluctuations. The random wave theory is derived from the former one.
## 4 Nonlinear Finite Element Analysis Considering Fire Damage
### Details of FE Models  
Table 2 and Figure 11 show the details of the members modeled for the FE analysis. In the slab model name, the number after \"D\" indicates the HCS depth, the number after \"S\" the span length (mm), the number after \"H\" the hollow ratio of the section (%), the number after \"C\" the cover thickness of concrete (mm), and the number after \"L\" the load ratio. For example, D200-S6-H45-C40-L0.4 is a member in which a load corresponding to 40% of the ultimate load is applied to the HCS unit with a depth of 200 mm, a span length of 6 m, a 45% hollow ratio, and a cover thickness of 40 mm. It should also be noted that D200-S6-H45-C35-L0.1 and D265-S6-H45-C35-L0.1 members were used to verify the accuracy of the FE analysis based on a comparison with the fire resistance test performed in this study. Figure 11b shows that in the FE analysis, a load was applied to the slab, and then a temperature load was applied in the same manner as the actual fire resistance test. Figure 10: Specimens after fire resistance test. As shown in Figure 12, concrete, prestressing strands, and reinforcing bars were modeled using a coupled temperature-displacement element (C3D8T) that enables the heat transfer analysis and structural analysis simultaneously. In the analysis model, the interaction conditions between concrete and prestressing strands or reinforcing bars were assumed to be fully bonded, which was modeled by the embedded option. In the specimens fabricated in this study, the reinforcing bars were not placed in the topping concrete with a thickness of 50 mm. Accordingly, the D200-S6-H45-C35-L0.1 and D265-S6-H45-C35-L0.1 members were modeled to have the same details of topping concrete, whose purpose was to verify the accuracy of the FE analysis. Both ends of the member were modeled as simply supported, which is the same as the actual experimental test condition. In addition, an uniformly distributed load was applied to the top of the slab via the pressure option in ABAQUS/CAE. Static simulations allowing for nonlinearity arising from both the constitutive law and the large geometric deformations were performed. The convergence criteria have been set as the default convergence criteria provided by Abaqus, i.e., 0.5% of the force acting on the structure over time.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 4 The Proposed Method
### LBIA Algorithm
#### 4.3.1 Algorithm Process  
The algorithm process is as follows:  
1. Assign initial information to each node in the network. The initial information \(M_{x}\) for each source node \(x\) in the LBIA algorithm is defined as shown in Equation (7): \[M_{x}=\log(1+\frac{d_{x}}{d_{max}}+PK_{s})\] (7)
2. Select a node and calculate the information propagated by all its neighboring nodes. The propagation information \(LB_{v}\) from node \(j\) to node \(i\) is defined as shown in Equation (8): \[LB_{v}(i)=\frac{d_{i}-1}{\sum_{j\in N(i)}JS_{(i,j)}+c_{j}+\sum_{j\in N(i)}c_{j }}+M_{i}\] (8) where \(M_{i}\) represents the initial information of node \(i\), \(JS_{(i,j)}\) represents the Jaccard coefficient between node \(i\) and node \(j\), \(c_{i}\) represents the clustering coefficient of node \(i\), and \(N\)_(i)_ is the first-order neighbor set of node \(i\);
3. Aggregate the propagated information from all neighboring nodes, and the aggregation information (AI) of node \(i\) is defined as shown in Equation (9): \[AI_{i}=\sum_{i\in N(x)}LB_{v}(i)\] (9)
4. Considering both the node's own information and the aggregation information from its neighbors. The final value LBIA is defined as shown in Equation (10): \[LBIA_{x}=LB_{v}(x)+AI_{i}\] (10)
5. Finally, repeat steps II, III, and IV to calculate the LBIA values for all nodes in the network.