esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrogenation of Adiponitrile to Hexamethylenediamine over Raney Ni and Co Catalysts
## 2 Experimental Section
### Product Analysis  
A product sample taken after the reaction finished was first filtered to ensure the removal of catalyst particles and then diluted 10-fold in ethanol before gas chromatography (GC) analysis on an Agilent 7890B instrument (Agilent Technologies, Santa Clara, CA, USA), equipped with an Agilent DB-1701 column (length: 30 m; inside diameter: 0.32 mm; film thickness: 0.25 \(\upmu\)m) and a flame ionization detector (FID). Argon was used as the carrier gas (flow rate: 30 mL min\({}^{-1}\)). The flow rates of H\({}_{2}\) gas and air were set to 30 mL min\({}^{-1}\) and 300 mL min\({}^{-1}\), respectively. The split ratio was set to 10:1, and the injection volume was set to 1 \(\upmu\)L using a 10 \(\upmu\)L syringe. The sample inlet temperature and detector temperature were set as 230 and 250 \({}^{\circ}\)C, respectively. The oven temperature program was as follows: 80 \({}^{\circ}\)C (held for 1 min); an increase in the temperature from 80 \({}^{\circ}\)C to 250 \({}^{\circ}\)C (ramping rate: 5 \({}^{\circ}\)C min\({}^{-1}\)); and 250 \({}^{\circ}\)C (held for 3 min).  
The characteristic peaks for chemical compounds in the product sample were identified from their retention times. Each peak was integrated while the actual concentrations of the compounds in the product sample were calculated from their respective pre-calibrated plots of peak areas versus concentrations.  
Product samples were also analyzed using an inductively coupled plasma-optical emission spectrometry (ICP-OES) (model: iCAP 7400 Duo, Thermo Fisher Scientific, Waltham, MA, USA) to measure the metal leaching of the solutions during the hydrogenation reaction.
# A Fitting Recognition Approach Combining Depth-Attention YOLOv5 and Prior Synthetic Dataset
## 7 Conclusions  
We propose a fitting recognition approach combining depth-attention YOLOv5 and prior synthetic dataset. The conclusions are as follows.  
1. Datasets are automatically synthesized based on prior series data, expediting the efficiency of algorithm development. Three synthetic datasets are generated, including a dataset of 4000 images with a size of 640 \(\times\) 480 on the test field, a dataset of 4000 images with a size of 720 \(\times\) 1280 on line field, and a dataset of 8000 XP images with a size of 640 \(\times\) 480. The synthetic datasets spend 26.4 h, and the synthetic dataset with the largest volume takes 12.6 h.
2. Depth-attention mechanism is proposed based on the depth difference of collected data, applying to target recognition with depth difference information. The test field experiments verify the feasibility of the depth-attention mechanism to improve the accuracy of the YOLOv5 model. Among them, AP, recall, and precision are 68.1%, 98.3%, and 98.3%, increasing by 5.2%, 4.8%, and 4.1%, respectively.
3. The depth-attention YOLOv5 model is trained using a synthetic dataset for real line experiments, and the results show that the depth-attention mechanism acquires a map of 64.6%, improving mAP by 3.2%. Compared with other attention mechanisms, the depth-attention mechanism improves the mAP in all aspects and reduces omission and misdetection, providing better results and faster inference.  
The limitations of the proposed approach mainly focus on two aspects: (1) \(D_{\alpha}\) and \(D_{\beta}\) are related to the recognition environment, requiring testing to determine the optimal parameters. (2) Depth-attention mechanism relies on depth maps with depth differences; therefore, the values of \(D_{\alpha}\) and \(D_{\beta}\) need to be taken more precisely when the depth difference is small.  
Conceptualization, J.Z. and J.L.; methodology, J.Z., J.L. and X.Q.; software, J.Z.; validation, B.L., Y.Z. and J.S.; formal analysis, H.L.; investigation, J.Z., X.Q., J.L., B.L., H.L., Z.L., J.S. and Y.Z.; resources, J.Z. and B.L.; data curation, J.Z., X.Q., J.L., B.L., H.L., Z.L., J.S. and Y.Z.; writing--original draft preparation, J.Z. and J.L.; writing--review and editing, J.Z., J.L. and X.Q. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (grant nos. 62063030 and 62163032), the Financial Science and Technology Program of the XPCC (grant nos. 2021 DB003, 2022 CB011, and 2022 CB002-07), and the High-Level Talent Project of Shihezi University (grant nos. RCZK2018C31 and RCZK2018C32).  
Not applicable.  
We published our dataset at [[https://github.com/zjplanthe/synthetic_data_insulator_blender](https://github.com/zjplanthe/synthetic_data_insulator_blender)]([https://github.com/zjplanthe/synthetic_data_insulator_blender](https://github.com/zjplanthe/synthetic_data_insulator_blender)), accessed on 7 September 2022.  
The authors declare no conflict of interest.
## 2 Experimental Section
### Characterizations  
The morphology and structure were characterized by scanning electron microscope (SEM, S-4800) and transmission electron microscope (TEM, JEM-2100). An X-ray diffraction (XRD, D8 Advance) was used for the phase structure analysis, and the test range was 10-80\({}^{\circ}\) with a sampling step of 0.02\({}^{\circ}\). Raman spectrum (Raman, DXRci) was used for graphization degree analysis, and the laser wavelength was 532 nm, the grating was 1800 L/mm, and the line resolution was 1.6 nm/mm. The X-ray photoelectron spectra (XPS, AXIS SUPRA) were used for the chemical composition and functional group binding analysis. Magnetic properties were measured by the vibrating sample magnetometer (VSM, Model 7400 series), and the magnetic field strength was 2 T. To evaluate the microwave absorption performances of the composite, the mixture containing 15% composite and 85% paraffin was pressed into a coaxial ring with inner diameter of 3.04 mm and outer diameter of 7.00 mm in a mold, and its electromagnetic parameters in the 2-18 GHz range were measured using the vector network analyzer (VNA, N5224A). According to transmission line theory, the reflection loss (RL) can be calculated by the following equations [16]:  
\[Z_{\rm in}=Z_{0}(\mu_{\rm r}/\varepsilon_{\rm r})^{1/2}\ {\rm tan}\ {\rm h}\left[ \beta 2\pi fd/c(\mu_{\rm r}\varepsilon_{\rm r})^{1/2}\right] \tag{1}\]  
\[{\rm RL}=20{\rm lg}|(Z_{\rm in}-Z_{0})/(Z_{\rm in}+Z_{0})| \tag{2}\]  
where \(Z_{0}\) is the impedance of free space and \(Z_{\rm in}\) is the normalized input impedance, respectively, \(\varepsilon_{\rm r}\) and \(\mu_{\rm r}\) are, respectively, the complex permittivity and permeability, \(f\) is the frequency of the incident wave, \(d\) is the thickness of the sample, and \(c\) is the speed of light in free space.
## 2 Flight Parameter Preprocessing
### Extraction of Effective Flight Phases  
In actual flight, the flight phases include ground run, taxiing, take-off, climb, departure, aerobatics, cruise, descent, approach landing and ground taxiing. Compared to airborne flight, the ground run and taxiing phases are subjected to smaller loads. In this study, the effective flight phase is defined as the remaining flight process excluding the ground run, so that the ground taxi take-off and air maneuver cruise phases are analyzed. The judgment of the ground taxi take-off relies on the state of the landing gear, and due to the lack of landing gear information in the data, the ground speed calculated by airspeed and wind direction is used to judge the effective flight data. The correspondence between ground speed and flight phases is shown in Table 2.
## 7 Classification of Mobile Sink based Data Collection Schemes  
In recent years, a number of data collection schemes have been proposed for mWSNs. Based on sink mobility patterns, we classify these schemes into three main categories: Path Constrained Sink Mobility-based Schemes, Path Unconstrained Sink Mobility-based Schemes, and Controlled Sink Mobility-based Schemes. Figure 3 illustrates the taxonomy of sink mobility-based data collection/dissemination schemes covered in this paper. In the following sub-sections, we briefly discuss the relevant schemes in each of these categories.
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 1 Introduction
* We propose and describe the implementation of a spoofing scheme incorporated into the anti-UAV system that consists of first jamming the GPS signal, followed by the transmission of false signals that allow the acquisition and indirect control of the unauthorized drone, enabling the user to land it in a pre-defined location. * We propose a simple jamming scheme incorporated into the anti-UAV system that is activated in parallel with the spoofing functionality and that is directed to the control signals so that the vehicle control cannot be accomplished in manual mode. The remainder of the paper is organized as follows: Section 2 introduces the global architecture of the system, where the different blocks that constitute the system are presented. Section 3 describes the different jamming techniques considered for the systems, whereas the spoofing technology is presented. Section 4 presents the overall prototype, the radiation diagram of the antenna developed for the desired frequencies and the battery sizing. Interference and spoofing tests in a real scenario are described in Section 5, followed by the conclusions in Section 6. Finally, in Section 7 future work is reported.
## 4 Results  
### Fuzzy Control  
The characteristics obtained for the fuzzy control system by simulation for the variables, namely position error \(e_{\theta}\), torque \(M\), rotation speed \(\omega\), and position \(\theta\), respectively, are shown in Figures 18-21.  
The position error decreased asymptotically and reached zero value in the steady-state. The fuzzy controller controlled the torque in two stages, with a high positive value of acceleration and an extreme negative value of braking. The speed decreased asymptotically and reached zero value in the steady-state. The position varied asymptotically and periodically, reaching the prescribed value after a response time of about 6 s.  
Analyzing the obtained characteristics, it can be said that with the help of a fuzzy control system, a behavior similar to the linear state control system can be obtained: a zero error in steady state and an asymptotic, aperiodic variation of position.  
Regarding the stability of the fuzzy control system it can be said that due to the fact that the response to the system output to a bounded input signal, it is in turn bounded, the system is externally stable, corresponding to BIBO stability-bounded input-bounded output.  
Figure 21: Position.  
Figure 20: Speed.
# Early Results on GNSS Receiver Antenna Calibration System Development +
## 3 Results and Discussion
Considering the full antenna hemisphere, the LMMT and Geo++ GmbH PCC differences are in the interval from \(-\)2.92 to 0.72 mm, with the middle 50% of \(\Delta\)_PCC_ being within 0.44 mm. The RMS of the differences is 0.52 mm. However, if only the elevation-reduced antenna hemisphere is considered, accuracy measures significantly improve. The range of all \(\Delta\)_PCC_ is 2.36 mm with an RMS value of 0.34 mm whereby the middle 50% of the differences does not exceed 0.41 mm. Therefore, to summarize, with the newly developed LMMT antenna calibration system, an estimated agreement with the accredited Geo++ GmbH calibrations, within 0.52 mm, was achieved.
## 3 Materials and Methodology
### Surface Subsidence Prediction and Impact Analysis
#### 3.4.3 Prediction and Impact Analysis of Ground Subsidence with 80% Filling Ratio  
Compared to caving mining, backfill mining does not cause mining damage above level I to the buildings above the working face. The surface movement and deformation damage level, according to the defense standards of the 2017 \"Regulations on Coal Pillar Retention and Coal Mining for Buildings, Water Bodies, Railways, and Main Wells and Lanes\" in China, are shown in Table 7.  
According to the equivalent mining height calculation method based on the probability integral method, the surface subsidence extreme value of backfill under an 80% filling rate was predicted, and the results are shown in Table 8. An analysis of the extreme values of the data in the table shows that the horizontal deformation and other subsidence indicators at the surface of the backfill working face are within the subsidence indicators specified for level I building damage. The curvature deformation is relatively small, less than 0.01 mm/m\({}^{2}\), and is not listed in the table. Due to the space limitations of this paper, only a contour map of the ground subsidence seen at an 80% filling ratio was developed, as illustrated in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mining Depth** & **Filling Ratio** & **Subsidence** & **Horizontal Movement** & **Tangent of Main** & **Inflection Point** \\  & **Coefficient** & **Coefficient** & **Influence Angle** & **Displacement** \\ \hline
700 m & 80\% & q = 0.74 & b = 0.37 & \(1.8^{\circ}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of the probability integral method used for determining surface subsidence.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Damage Level**} & \multirow{2}{*}{**Damage Classification**} & \multicolumn{3}{c}{**Surface Deformation Value**} \\ \cline{2-4}  & & **Horizontal (mm/m)** & **Curvature (\(\times\)10\({}^{-3}\)/m)** & **Tilt (mm/m)** \\ \hline I & Slight damage & \(\leq\)2 & \(\leq\)0.2 & \(\leq\)3 \\ II & Mild damage & \(\leq\)4 & \(\leq\)0.4 & \(\leq\)6 \\ III & Moderate damage & \(\leq\)6 & \(\leq\)0.6 & \(\leq\)10 \\ IV & Severe damage & \(>\)6 & \(>\)0.6 & \(>\)10 \\ \hline \hline \end{tabular}
\end{table}
Table 7: China Coal Association’s brick–concrete structure damage grade system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subsidence** ** **(mm)**} & \multicolumn{3}{c}{**Horizontal Movement (mm)**} & **Horizontal Deformation (mm/m)** & **Inclined Deformation (mm/m)** \\ \cline{2-5}  & **Vertical** & **Horizontal** & **Vertical** & **Horizontal** & **Vertical** & **Horizontal** \\  & **Direction** & **Direction** & **Direction** & **Direction** & **Direction** \\ \hline \(-\)281 & \(-\)110/122 & \(-\)86/122 & \(-\)1.19/0.56 & \(-\)0.89/0.53 & \(-\)1.2/0.6 & \(-\)0.9/0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Extreme values of movement and deformation of surface buildings and structures after mining and backfilling.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### Transmission Tower matching Network
#### 2.3.4 Matching Network Loss Function  
During the early stages of model training, using only triplet loss might lead to difficulties in network convergence. Thus, a common approach is to integrate multi-class  
Figure 8: Schematic diagram of online triplet generation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field** & **Data Type** & **Description** & **Source** \\ \hline ID & int & Primary key & Auto-increment \\ features1 & varchar(4000) & Image feature vector & Matching network result \\ tower\_ID & int & Transmission tower identifier & Manual entry \\ GPS\_longitude & float(20,10) & Image longitude info & Image info or “null” \\ GPS\_latitude & float(20,10) & Image latitude info & Image info or “null” \\ \hline \hline \end{tabular}
\end{table}
Table 3: Template library structure(features1 is a 128-dimensional feature vector).  
cross-entropy loss to facilitate network convergence [35]. In this study, we utilize a softmax activation function in the network's final layer to map the feature vector to a probability distribution across different transmission tower instances (categories), compute the cross-entropy loss, and subsequently optimize the model in conjunction with the triplet loss. As a result, the composite loss function of the model is shown in Equation (15).  
\[S=\frac{1}{N}\sum_{i=1}^{N}\left[\parallel f(x_{i}^{a})-f\Big{(}x_{i}^{p} \Big{)}\parallel_{2}^{2}-\parallel f(x_{i}^{a})-f(x_{i}^{a})\parallel_{2}^{2 }+a\right]_{+}+\frac{1}{m}\sum_{i=1}^{m}\sum_{c=1}^{C}y_{i,c}\text{log}(p_{i,c}) \tag{15}\]  
where \(N\) represents the number of online-generated triplet samples, and \(f\big{(}x_{i}^{a}\big{)}\) represents the embedding vector of the anchor image obtained through the network. \(f\Big{(}x_{i}^{p}\Big{)}\) represents the embedding vector of the positive sample obtained from the network. \(f\big{(}x_{i}^{a}\big{)}\) represents the embedding vector of the negative sample procured from the network. \(a\) represents the enforced margin between positive and negative sample pairs. \(m\) represents the number of images in the online-generated triplets. \(C\) represents the number of categories. \(y_{i,c}\) represents the actual label of the \(ith\) sample in the \(cth\) category. \(p_{i,c}\) represents the predicted probability of the \(ith\) sample in the \(cth\) category.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Sinkhole Attack  
Reactive routing protocols, such as the AODV, rely on a query-response mechanism for route discovery. When a source node requires a route to a destination, it broadcasts a route request (RREQ) packet through the network. Intermediate nodes forward this RREQ, incrementing a hop count to track the path length. Upon receiving an RREQ, the destination node or a node possessing a fresh route to the destination responds with a route reply (RREP) packet, which is traced back to the source via the reverse path established by the RREQ. The source selects the optimal route based on metrics such as hop count and route freshness, indicated by a sequence number. A sinkhole attack exploits this process by maliciously crafting a RREP message that falsely advertises a shorter path and a higher sequence number. This deceptive information lures the source node into redirecting traffic through the attacker, effectively creating a \"sinkhole\" that absorbs incoming packets [30].  
Once a sinkhole attack is successfully executed, the malicious node gains control over the network traffic destined for the targeted destination. The attacker can then manipulate this traffic in various ways. A common tactic is to simply drop the packets, effectively launching a black hole attack, as illustrated in Figure 5. This results in complete data loss for the intended recipients [31]. However, the malicious node's capabilities extend beyond this, allowing for a range of other malicious activities, such as eavesdropping, data modification, or launching further attacks on the network [32].
## 1 Introduction
The reported group is composed of individuals who have been diagnosed as positive confirmed cases. Also, in order to answer the specific question about required hospital resources, our model tracks the number of hospitalisations and admissions to an Intensive Care Unit (ICU) of COVID-19 patients. Some parameters, such as the latent period or the average residence times in each compartment or group, are maintained constants as in traditional SEIR models. A key feature that distinguishes our model is to allow the transmissibility and proportions of individuals who need hospital beds or ICU, to vary daily with a moving time-window strategy. Most of the mathematical models developed in the context of the COVID-19 pandemic were applied to large countries or those with early outbreaks, with a large amount of available data. However, in the context of limited data availability in low-to-middle-income countries, specific models and strategies are needed to overcome this difficulty. In particular, the mathematical model developed in this work is based on limited data available from Paraguay, where except around the capital city, there is no regular testing policy in the rest of the country, nor a good record of region-based mobility. The data used in order to calibrate the model was provided by the Ministry of Public Health and Social Welfare (MSPyBS: _Ministerio de Salud Publica y Bienestar Social_) and Health Monitoring Centre in Paraguay (DGVS: _Direccion General de Vigilancia de la Salud_) ([[http://dgvs.mspbs.gov.py/](http://dgvs.mspbs.gov.py/)]([http://dgvs.mspbs.gov.py/](http://dgvs.mspbs.gov.py/)), accessed on 20 May 2021). The spread of COVID-19 in Paraguay is characterised by a strongly enforced initial lockdown, which kept the amount of new positive cases low for several months. In this sense, it is worth discussing the effect of early lockdown in the dynamics of spread, as well as, how the time-varying parameters can explain different scenarios observed in Paraguay. The feature of time-varying parameters allows a user to draw future scenarios and use our model as a prediction tool to guide decision-making to contain outbreaks and plan hospital resources accordingly. The work is organised as follows: the proposed model is presented in Section 2 including the spread dynamics in Section 2.1.1 and the dynamics of hospital resources in Section 2.1.3, as well as the parameter estimations in Section 2.3; Section 3 shows the results obtained and the forecast capability of the model, and it is followed by the discussions in Section 4, the relevance of the present work in Section 5, and final remarks in Section 6.
###### Abstract  
This study explores the feasibility of using low-intensity focused ultrasound (LIFU) to induce rotational sensations in the human semicircular canal (SCC) through the acoustic streaming effect. Existing vestibular stimulation methods, such as galvanic vestibular stimulation (GVS), caloric vestibular stimulation (CVS), and magnetic vestibular stimulation (MVS), face limitations in spatial and temporal resolution, with unclear mechanisms. This study investigates whether LIFU can overcome these limitations by modulating endolymph motion within SCC. A 3D finite element model was constructed to simulate the effects of LIFU-induced acoustic streaming on SCC (particularly the endolymph), with thermal effects evaluated to ensure safety. Fluid-structure interaction (FSI) was used to analyze the relationship between endolymph flow and cupula deformation. By adjusting the focal point of the ultrasound transducer, we were able to alter fluid flow pattern, which resulted in variations in cupula displacement. The results demonstrated that LIFU successfully induces fluid motion in SCC without exceeding thermal safety limits (\(<\)1 \({}^{\circ}\)C), suggesting its potential for controlling rotational sensations, with cupula displacement exceeding 1 \(\upmu\)m. This novel approach enhances the understanding of LIFU's thermal and neuromodulatory effects on the vestibular system, and thereby offers promising implications for future therapeutic applications.  
numerical analysis; focused ultrasound; transcranial ultrasound; vestibular stimulation; vestibular system; neuromodulation; rotational perception +
Footnote †: journal: Applied sciences  
[PERSON], [PERSON], [PERSON].  
Simulation Study of Low-Intensity Focused Ultrasound for Modulating Rotational Sense Through Acoustic Streaming in Semicircular Canal: A Pilot Study. _Appl. Sci._**2024**, 14, 11432. [[https://doi.org/10.3900/app142311432](https://doi.org/10.3900/app142311432)]([https://doi.org/10.3900/app142311432](https://doi.org/10.3900/app142311432))