esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
### Model Overview  
This paper proposes a personalized scenic tourism route planning model based on urgency. The model overview is shown in Figure 1.  
As shown in Figure 1, this model takes tourist users as the object to plan an optimal travel route that meets the query conditions and whose scenic features are in line with user preferences. The main process of the model can be divided into three stages: user preferences extraction and POI relationship modelling, scenic score calculation, and route generation. The input data for user preference extraction and POI relationship modelling are historical check-in data and scenic spot information data, and the output is the correlation between the user preference vector and scenic spots. Scenic score calculation is the second stage. The input data are road network data, the relationship matrix of POIs, and preference vector of the user. The output is the effective edge scenic score. In the process of the calculation of the edge scenic score, it is necessary to perform the steps of the spatial road network projection, the scenic score calculation of POI, the neighbour analysis table acquisition, and the scenic score calculation of edge. Finally, the edge scenic score is calculated according to the POI scenic score of the edge neighbour. Considering that different users prefer different types of POI, the experience value obtained during sightseeing will also be different. Therefore, user preference information is combined with the scenic score calculation. Route generation is the last stage which uses the improved genetic algorithm to search the effective edge and obtain the best travel route from the starting point to the end point that meets the user's query conditions. This article will explain each stage separately in the following sections.
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 3 Framework
### Carrier Feature Block Attention  
Due to subtle distinctions between the carrier edge and the background, the accurate recognition of carrier positions becomes a challenging task. To address this issue, we  
Figure 2: Overview of CarrDet, which consists of four steps: (1) feature extractor, which inputs images and outputs feature representations; (2) carrier feature block attention module (CFBA), which inputs feature representations and outputs enhanced feature representations; (3) detection head, which inputs enhanced features and outputs candidate frames; and (4) symmetry-based defective carrier detection module (SDCD), which inputs candidate frames and outputs refined candidate frames.  
introduce the carrier feature block attention module, which comprises two essential components: channel attention and sparse attention. The channel attention emphasizes the \"what\" aspect, and the sparse attention prioritizes the \"where\" aspect [28]. This enhancement aims to focus on information related to carriers with shallow edges. By incorporating these attention mechanisms, the network can more accurately detect carriers in challenging scenarios involving color similarity. The following describes the details of each attention module.
## 3 Results and Discussion
### Mechanism Discussion  
#### 3.4.1 Reactive Group Capture Experiments Analysis  
Figure 10 illustrates the impact of scavengers on photocatalytic degradation of TCH. TCH concentrations in all experimental groups gradually decreased through visible light exposure. After 60 min, the degradation rates of TCH in the blank group and the experimental groups with EDTA-2 Na, p-benzoquinone, and isopropanol inhibitors added were 81.29%, 43.71%, 51.57%, and 80.00%, respectively. The notable decline in degradation rates observed after adding EDTA-2 Na and BQ suggests that h+ and -O2- play a central role in the degradation of TCH. A quasi-level kinetic linear fit of the degradation reaction was performed, and the values of k for the rate constant of TCH degradation were 0.0465 min-1, 0.0081 min-1, 0.0144 min-1, and 0.0440 min-1 for the experimental groups with and without EDTA-2 Na, BQ, and IPA inhibitors, respectively, within 30 min. The correlation coefficients for the TCH degradation were 0.9590, 0.9676, 0.9557, and 0.9533, respectively. The degradation rates decreased in the presence of inhibitors, with EDTA-2 Na and BQ having a more pronounced impact, reducing the rates to 17.42% and 30.97% of the blank group, respectively. While the degradation efficiency was slightly reduced by adding isopropanol, the degradation rate was 95.07% of that without the addition of inhibitors. These findings suggest that h+ and -O2- serve as the main reactive species responsible for facilitating TCH degradation, while the contribution of OH is minimal and can be considered insignificant, suggesting that enhancing the generation and stability of these species could further improve the process's effectiveness [48].
## 6 Evaluation of the Proposed Protocol
### Security Evaluation
In this attack the malicious nodes, especially the CHs, insert false sensor readings tricking the BS into accepting aggregated results which are significantly different from the actual data. As mentioned earlier our evaluation does not include attacks launched by compromised nodes, so here we consider only stealth attacks launched by inserted intruder nodes. Since the proposed protocol provides authenticated advertisement messages using digital signatures, we guarantee that the CHs are legitimate nodes and therefore the proposed protocol can address these attacks. Another attack we observed is message tampering attacks. This attack can disrupt data aggregation in ESPDA, particularly when its target is the pattern code messages. ESPDA does not provide any data integrity service for this message, while the proposed protocol provides it by using keyed MAC. Therefore, from Table 4 we can see that ESPDA fails to provide protection against all possible attacks observed but eavesdropping. ESPDA does not employ any countermeasure that can provide data freshness service. Its lack of pairwise key providence among sensor nodes invites CH impersonation attacks and message tampering to take place. These attacks can affect the data aggregation process outcome in very bad way since the BS may receive false reports of the network, while in the proposed protocol, the role of the pair-wise key that is affordable using identity-based key agreement and the counter mechanism gives great contribution in dealing with those attacks.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 5 Single Scanner-Based DoA Estimation  
A lot of research has been conducted in the field of Bluetooth signal localization for indoor navigation and device positioning ([6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19]) with many algorithms using the RSS of the advertising packets to estimate the position of the beacon. In some algorithms, DoA is estimated through triangulation by measuring the difference in ToF between two antennas. Some implementations of triangulation with antenna array can deliver both 2D and 3D tracking with sub-meter precision and a 5\({}^{\circ}\) accuracy. In other research, multiple scanner devices set up in the form of a sensor network are used to act as an antenna array for estimation of DoA by calculating the difference in ToF between each device [17, 18, 19]. In this paper, we explore the estimation of DoA for beacon positioning without the aid of an  
Figure 1: BLE and WiFi channel spectrum (revise the diagram).  
antenna array and using a single scanner device. The scanner device we used is an easily accessible smartphone with Bluetooth capabilities, an in-built magnetometer, and a GPS receiver. As most smartphones are not equipped with an antenna array, the ToF difference calculation cannot be used for DoA estimation. In Sections 5.1-5.3, we further discuss in detail the method of using a single scanner, including the design of the scanner application, the process of capturing data, and the algorithm for filtering and processing the data to estimate the location of the beacon.
## 2 Materials and Methods  
### Chemicals  
Dodecanoic acid (99%) and tetradecanoic acid (99%) were obtained from Acros Organics (Geel, Belgium). Butyric acid (99%), hexanoic acid (99%), \(p\)-nitrophenyl octanoate (96%), and tetracycline were bought from Alfa Aesar (Karlsruhe, Germany). Coomassieie(r) Brilliant Blue R250 and tetramethyltylendiamine (TEMED) were purchased from AppliChem (Karlsruhe, Germany). Acetic acid (100%), agar-agar Kobe I, bovine albumin fraction V (98%), bromophenol blue (sodium salt), citric acid (99.5%), chloramphenicol (98.5%), decanoic acid (99%) disodium hydrogen phosphate (99.5%), ethanol (99.8%), glycine (99%), isopropanol (99.8%), kanamycin sulfate, lysogeny broth (LB) medium, \(\beta\)-mercaptoethanol, octanoic acid (99.5%) terrific broth (TB) medium, 2-[4-(2,4,4-trimethyltpentan-2-yl)phenoxyyl]-ethanol (Triton X-100), tris(hydroxymethyl)-aminomethane (TRIS, 99%), TRIS-hydrochloride (99%), dipotassium hydrogen phosphate (99%), potassium dihydrogenphosphate (98%), ROTI(r) mark standard, Rotiphorese(r) Gel 40, sodium chloride (99.8%), sodium deoxycholate (98%), sodium dodecyl sulfate (99%), sodium dihydrogen phosphate (98%), and sodium hydroxide were provided by [PERSON] (Karlsruhe, Germany). Ammonium persulfate (100%) and hexadecenoic acid (100%) were bought from Merck (Darmstadt, Germany) and LB-SOC (Outgrowth medium) from New England BioLabs(r) (Frankfurt am Main, Germany). Isopropyl-\(\beta\)-d-thiogalactopyranoside (IPTG) was purchased from Serva Electrophoresis (Heidelberg, Germany). Fastblue RR Salt, glycerol (99%), imidazole (99.5%), \(p\)-nitrophenyl acetate (98%), \(p\)-nitrophenyl butyrate (98%), \(p\)-nitrophenyl valerate (98%), \(p\)-nitrophenyl hexanoate (98%), \(p\)-nitrophenyl palmitate (98%), and l-(+)-arabinose (99%) were supplied by Sigma Aldrich (Schnelldorf, Germany). Gum arabic (X-E IRX 75200) was bought from Symrise (Holzminden, Germany) and hydrochloric acid (25%) from [PERSON] (Renning, Germany). Helium (5.0) was purchased from Praxair (Dusseldorf, Germany) and nitrogen (5.0) from Air Liquide (Dusseldorf, Germany).
## 4 Experimental Results
### Results and Analysis
#### 4.2.2 Results of Object Detection  
For our object detection model, the benchmark dataset MS COCO 2017 was selected to test. The object detection results of different loss functions on MS COCO 2017 are demonstrated in Table 3.  
Compared with IoU, GioU, and CIoU, YOLOv5-DeepSORT, with the alpha-IoU loss function in our method, achieves impressive results. The metric AP, AP\({}_{50}\), AP\({}_{75}\), mAP, and mAP\({}_{75:95}\) are improved by 2.87%, 2.70%, 2.26%, 2.29%, and 2.58%, respectively. Such a high object detection accuracy of the model is easily estimated to be appropriate for the detection task of the shot boundary objects.  
Figure 7 reveals the results of using our boundary object detection module to detect objects at the shot boundary in some classroom videos of the self-built dataset CLV.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**LaSOT**} & \multicolumn{3}{c}{**GOT-10k**} \\ \cline{2-7}  & **AUC** & **P\({}_{Norm}\)** & **P** & **mAO** & **mSR\({}_{50}\)** & **mSR\({}_{75}\)** \\ \hline AiATrack & 0.690 & 0.794 & 0.738 & 0.696 & 0.800 & 0.632 \\ SwinTrack-B-384 & 0.702 & 0.784 & 0.753 & 0.724 & 0.805 & 0.678 \\ MixFormer-L & 0.701 & 0.799 & 0.763 & 0.756 & **0.8573 1**  & 0.728 \\ OSTrack-384 & 0.711 & 0.811 & 0.776 & 0.737 & 0.832 & 0.708 \\ NeighborTrack-OSTrack & 0.722 & **0.818** & 0.780 & 0.757 & 0.8572 & 0.733 \\ Ours & **0.731** & 0.814 & **0.787** & **0.763** & 0.854 & **0.739** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art models on two SOT benchmarks: LaSOT and GOT-10k.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loss Function** & **AP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** & **mAP** & **mAP\({}_{75:95}\)** \\ \hline L\({}_{\text{IoU}}\) & 47.61\% & 67.52\% & 53.48\% & 48.76\% & 34.51\% \\ L\({}_{\text{GIoU}}\) & 50.10\% & 69.49\% & 55.58\% & 51.22\% & 36.04\% \\ L\({}_{\text{GIoU}}\) & 49.38\% & 68.84\% & 54.69\% & 50.59\% & 35.58\% \\ Ours & **52.97\% 1 ** & **72.19\%** & **57.84\%** & **53.51\%** & **38.62\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection results of YOLOv5-DeepSORT in our method with different loss functions.  
Figure 7: Detection results of objects at the shot boundary. It shows the results of object detection for objects at the shot boundary in some classroom videos after the target leaves the shot. It can be seen that objects within a certain range of the shot boundary have been detected.
# Semi-Supervised Taxonomy Expansion and Completion in Dynamic Taxonomies
## 2 Problem Definition  
We define the problem as the automated expansion and completion of a taxonomy with the following objectives:  
* Dynamic Taxonomy Maintenance: Taxonomies are inherently dynamic, requiring the integration of new concepts, relationships, and subcategories without disrupting the existing structure.
* Vertical Taxonomy Expansion (New Category/New Hypernym): This involves identifying and adding higher-level categories (hypernyms) to accommodate new concepts that introduce broader generalizations. A hypernym is a broader term that encompasses multiple specific instances. For example, \"Programming Languages\" is a hypernym that includes more specific skills like \"Python\" or \"Rust\".
* Horizontal Taxonomy Expansion (New Variation/New Hyponym): This involves adding new subcategories (hyponyms) or variations under existing categories to improve coverage and granularity. A hyponym is a term that represents a subset of a broader category. For instance, \"Python\" and \"JavaScript\" are hyponyms of the category \"Programming Languages.\"
* Taxonomy Completion (Existing variation/Existing hyponym): This involves identifying the most likely skill hyponym relationships between existing concepts in the taxonomy to ensure structural completeness and consistency.  
Given an existing taxonomy \(T_{0}=(N_{0},E_{0})\) where \(N_{0}\) represents the set of nodes (concepts) and \(E_{0}\) the directed edges representing \"is-a\" relationships, and \(C=\{n_{1},n_{2},\ldots,n_{m}\}\) is a set of new concepts to be integrated into \(T_{0}\), the task is to generate an updated taxonomy: \(T=(N,E)\), where \(N=N_{0}\cup C\) and \(E=E_{0}\cup E^{\prime}\). The newly added edges \(E^{\prime}\) represent the appropriate hypernym and hyponym relationships for concepts in \(C\).  
Additionally, the system identifies concepts from \(C\) that are already part of the taxonomy (\(N_{0}\)) and refers to them as matches. This ensures that the taxonomy is not redundantly expanded and that existing concepts are appropriately matched to their most relevant positions. The Figure 1 visually represents the problem described.  
The solution must:  
* Predict the most suitable parent(s) (hypernyms) for each new concept in \(C\).
* Identify potential subcategories (hyponyms) or relationships for each concept in \(C\), if applicable.
* Match concepts from \(C\) to existing nodes in \(N_{0}\) if they are already part of the taxonomy.
* Ensure the updated taxonomy \(T\) remains logically consistent and satisfies the requirements for vertical and horizontal expansion.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### Application of Blockchain Technology between Road Users and RSUs
#### 3.2.1 Registration and Initialization  
Each road user with an electronic device must register with the authority, which is the trusted official unit that manages the security parameters and keys of all organizations, and obtain a public key, a private key and identity certificates. Each participant is also assigned a reputation based on offenses and experience (for drivers of vehicles); the rating is generated from 0.1 to 1.0, where 1.0 is the highest rating indicator value.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 3 The Proposed Methodology
### Watermark Extraction  
The watermark extraction is much simpler than the embedding, which is shown in Figure 6.  
The extraction end firstly receives the audio \(\mathbf{\hat{x}}_{w}(n)\) that needs the watermark extracted. Subsequently, the received audio is fed into the cascade model of PTM and WL to obtained the whitened feature. The feature is then passed through a binary decision maker with a threshold of 0, and its output is the extracted watermark. The above procedure can be formulated as follows:  
\[\mathbf{\tilde{w}}=\text{binary}\left(f_{\Theta}(\mathbf{\hat{x}}_{w}(n))\right) \tag{17}\]  
where \(f_{\Theta}(\cdot)\) is defined as in Equation (8), and  
\[\text{binary}(\mathbf{a})=\begin{cases}1,\ \ a_{i}\geq 0\\ 0,\ \ a_{i}<0\end{cases}\]  
where \(a_{i}\) is an element in the input vector.  
It can be argued that the whitened feature lies in a high-dimensional space and the polarized watermarks are vertices of a hypercube in that space. Equation (17) shows that the vertex nearest to the whitened feature is the extracted watermark.
## 2 Introduction to Acoustic Feature Extraction Methods
### Power-Normalized Cepstral Coefficients  
PNCC is a recently developed voice recognition technology that exploits 50-120 ms frames for additional medium-time processes for existing single-segment spectra using 20-30 ms frames, making it stronger for noise, channel distortion, and reverberation. A medium-time process asymmetrically suppresses the noise in the voice signal. Figure 2 shows the PNCC feature extraction block diagram.  
A brief description of each block in Figure 2 is as follows.  
* Pre-Emphasis: This performs the role of a kind of high-pass filter and emphasizes high frequency components.
* STFT: The length of the frame is divided into about 20 to 40 ms (short time), and a frequency component is obtained by performing Fourier transform for each frame.
* Magnitude Squared: Finds the energy for each frame.
* Gammatone Frequency Integration: For each frame, a gammatone filterbank is applied.
* Medium-Time Power Calculation: The energy of the spectrum is obtained for a frame of 50 to 120 ms (medium-time).
* Asymmetric Noise Suppression with Temporal Masking: The predicted signal is subtracted from the input signal by predicting the level of background noise for each frame through asymmetric-nonlinear filtering.
* Weight Smoothing: This goes through the smoothing process for the transfer function.
* Time-Frequency Normalization: Takes the normalization process for time-frequency.
* Mean Power Normalization: This goes through the normalization process for the average power.
* Power Function Nonlinearity: This goes through a process of non-linearization of power.
* DCT: Performs the discrete cosine transform operation to make the cepstral coefficient.
* Mean Normalization: Takes an average to reduce the impact on fast-changing components.  
PNCC performs pre-emphasis when a voice signal is received and weighs the short-time Fourier transform (STFT) output to positive frequencies by a frequency response associated with gamma-toned filter banks. This helps to obtain power from the spectrum in the 40 analytical bands. If background noise and channel distortion are present compared to the existing single-segment spectrum, the performance is increased by predicting the level of background noise for each frame through asymmetric-nonlinear filtering and then subtracting the predicted signal from the input signal. The time-frequency, average power, and power function normalization process are followed by DCT and subsequently, the average normalization to extract PNCC on signals that have undergone a medium-time process.  
Figure 2: Block diagram of the Power-Normalized Cepstral Coefficients (PNCC).
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 3 Results
### Use of Bandwidth  
For each experiment, we averaged the number of UL resource blocks normalized to the bandwidth (BW). We assumed bandwidths of 10 MHz, 20 MHz, and 50 MHz for 4G LTE, 5G LTE, and 5G NR, respectively. In general, we observe in Figure 5 that data-intensive applications, i.e., FTP upload and iPerf, consume a larger bandwidth than low-data applications, i.e., Ping and WhatsApp, and that 5G NR uses fewer frequency resources than LTE:  
* WhatsApp: median value of 8.7% for 5G NR versus 11% for LTE;
* Twitch: median value of 24.4% for 5G NR versus 49% for LTE;
* FTP: median value of 63.5% for 5G NR versus 68% for LTE;
* iPerf: median value of 63.9% for 5G NR versus 68% for LTE.  
Figure 5: _Cont._  
5G NR only consumed slightly larger bandwidth resources than LTE for Ping, with median values of 2% and 3% for LTE and 5G NR, respectively. The 4G call or VoLTE used a very small fraction of the bandwidth (median value of 1%) compared to the WhatsApp call (11% for LTE and 8.7% for 5G NR).  
We did not observe a trend in the bandwidth usage as a function of distance to the base-station antenna.
# Monitoring Theophylline Concentrations in Saline Using Terahertz ATR Spectroscopy
###### Abstract  
To assess the possibility of terahertz technology for the determination of drug concentration in blood, we endeavored to apply a terahertz (THz) attenuated total reflection (ATR) method to measure the levels of theophylline in saline. A change in reflected THz wave amplitude was observed in a theophylline concentration-dependent manner. This result was obtained with simple measurements of comparisons of the amplitude of the reflected wave, and suggests that it is possible to monitor concentration changes of drugs in liquid material using THz ATR measurements.  
Keywords:terahertz; attenuated total reflection; therapeutic drug monitoring +
Footnote †: journal: Applied Sciences
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 2 Methods  
In this section, the correction strategies of the precise real-time PPP-B2b orbit and clock offset are presented, along with the methods for evaluating the accuracy of PPP-B2b orbits, clock offsets, and DCB corrections. In addition, the B2b-RTPPP time transfer mode is proposed.
## 4 Validation of Inelastic Seismic Analysis
### Analysis Modeling
#### 4.1.2 Structural Damping Value  
To be used for inelastic seismic analyses, the actual structural damping value corresponding to the test mockup is required. As shown in Figure 13, the test mockup is considered to be a very light damped system with dominantly well-separated modes. Therefore, the well-known half-power bandwidth method [18] can be used to identify the structural damping ratio. Figure 14 illustrates the concept for the dominant first natural frequency of the EW direction. In this method, it is assumed that half the total power of dissipation in this mode occurs in the frequency band between \(f_{1}\) and \(f_{2}\), where \(f_{1}\) and \(f_{2}\) are the frequencies corresponding to an amplitude of \(f_{\text{c}}/\sqrt{2}\).  
Figure 13: Resonance test results for each direction: (**a**) EW (\(\chi\)); (**b**) NS (\(Z\)); (**c**) vertical (\(\gamma\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Directions** & **Mode 1 (Hz)** & **Mode 2 (Hz)** \\ \hline \multirow{2}{*}{EW (\(\chi\))} & Test & 11.75 & 28.75 \\  & Analysis & 11.99 & 27.63 \\ \hline \multirow{2}{*}{NS (\(Z\))} & Test & 11.75 & 27.38 \\  & Analysis & 11.74 & 25.89 \\ \hline \multirow{2}{*}{V (\(\gamma\))} & Test & 11.56 & 28.56 \\  & Analysis & 11.99 & 27.63 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of resonance frequencies between tests and analyses.  
As illustrated in Figure 14, the critical damping ratio, \(\xi\) can be approximately determined by the following relationship;  
\[\xi=\frac{f_{1}-f_{2}}{2f_{\rm c}} \tag{5}\]  
From Equation (5), the obtained critical damping values based on the first natural frequency mode for each direction are 0.5% for EW, 0.4% for NS, and 0.8% for the vertical direction. Then, the averaged value of 0.57% for the three directions is used in this study.