esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Radar Sensor Operation in Different Driving Situations
#### 3.2.3 Roundabouts  
Roundabouts may allow for exploring the effects of sharp road curvature on radar operation. Most roundabout traversals involved straight movements, encountering little to no traffic. Figure 11 shows excerpts from five traversals (pale red shading), all of which occurred in truck order _1-2-3_. Driving periods outside roundabouts have white backdrops. The first traversal in the middle excerpt involved trucks 1 and 2 performing a full revolution to get rid of external vehicles located between trucks 2 and 3. Truck 3 had _Other vehicles_ preceding it before entering this roundabout, so its data were removed by filter 1. The traversal in the lower excerpt involved all three trucks revolving one round. Trucks 2 and 3 both had 10 traversals as followers, with 5 straight, 1 left turn and 4 right turns. For both trucks, only half of the aggregated _Roundabout_ durations had the preceding truck within field-of-view, and even when accounting for field-of-view, 11-12% of data are lost. Figure 11 shows that data points retained in roundabouts generally have scattered distance values which are too large to represent the preceding truck. Sharp curve radii and limited antenna beam width resulted in lost field-of-view to the preceding truck, so radar sensors detected irrelevant objects until field-of-view was regained after the turn.  
Figure 10: Radar data for three separate truck driving excerpts, _Ttunnel_ and _LWRR_.  
Average SNR values were 30 and 32% lower for trucks 2 and 3 in _Roundabouts_ than for _Driving_, respectively, so the radar sensors detected noisier data. Proportions of radar data as a function of _LWRR_-filtered _Roundabout_ durations (88-89%), are greater than for _Driving_ (83-85%). This is likely due to the trucks being grouped closer together when traversing _Roundabouts_. Speeds were also lower, causing smaller inter-vehicle distances and greater spatial concentration of measurements. For trucks 2 and 3, mean distances were 66-62% shorter during _Roundabouts_ than during _Driving_. The exploration shows that preceding trucks are tracked poorly when the platoon passes through roundabouts.
## 3 Results
### Biodegradation of UDMH in Aqueous MSM  
The degrading percentage in MSM with UDMH as the sole carbon source for M12 and P4 is shown in Figure 2a. M12 could effectively degrade UMDH with a high percentage of 86.0% (72 h); however, P4 barely biodegradated UDMH, approaching the control sample with the degrading percentage of 53.0%. The control sample degradation rate was 50.8%. It was shown that P4 was intensively inhibited by UDMH without an extra carbon source.  
Figure 1: The degradation of unsymmetrical dimethylhydrazine (UDMH) by activated sludge: (**a**) the removal of UDMH; (**b**) the removal of chemical oxygen demand (COD).  
Figure 2: The degradation of UDMH in aqueous mineral salt medium (MSM): (**a**) UDMH as sole carbon source; (**b**) with the addition of broth medium.  
When M12 and P4 were added with extra carbon sources of broth medium, as shown in Figure 2b, the degrading capacity of M12 was not obviously influenced, but the degrading capacity of P4 was significantly increased to 82.3%. This showed that broth medium could be used as co-metabolism substrate of P4 degrading UDMH. The conventional carbon sources at suitable concentration could stimulate microbial growth, which led to higher levels of enzymes being available for the degradation of toxic compounds [34].  
Compared with P4, M12 had a stronger ability to tolerate and degrade UMDH in solution. This kind of bacterium is not unique. The research group of [PERSON] [35] isolated a bacterium named _Achromobacter_ sp. that could oxidize hydrazine which had chemical properties similar to UDMH, in a solution of 100 mg\(\cdot\) L\({}^{-1}\), early in 1987.
# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 1 Introduction
The challenges mainly lie in several factors such as uncontrolled illumination, occlusions, and background clutter. To address all mentioned problems, we have made the following contributions (A preliminary version of this manuscript has been presented in a conference [7], where we tackle the commodity classification by only utilizing text information of logos on trucks. In this paper, we leverage both text and image content information and develop an integrated approach to combine predictions from both text-based and image-based solutions):  
* A coarse-to-fine universal logo detector that can estimate the locations of previously unseen logos. Since the detector is class-agnostic and not limited to a certain set of logos, it applies to a wide range of logos;
* An integrated approach to accurately link the detected logos to a company dataset customized for traffic scenarios. It proposes to leverage both text and image information from logos by a combination of texts generated from state-of-the-art solutions and logo types identified from our proposed logo matching method. The developed approach can be effectively extended to new logo classes and companies in which the traffic agency is highly interested;
* A novel end-to-end road video processing system to provide real-time dynamic commodity information by deploying sensors and edge devices in locations of interest. This utilizes the NAICS (North American Industry Classification System) taxonomy with searches aimed at commodity type inference based on the name of the company. In addition, we have developed a new benchmark on commodity classification using logos. To the best of our knowledge, this is the first attempt at doing so, which could be beneficial in helping traffic engineers and researchers better evaluate their developed models. The rest of the paper is organized as follows. Section 2 reviews previous studies that include relevant topics or techniques. Section 3 describes the overall commodity classification pipeline and details of all developed logo detection and recognition approaches. Section 4 describes the experimental settings and results of the different developed approaches. Finally, Section 5 concludes with an overall summary (while discussing limitations) and presents opportunities for future work.
# Research on the Flight Performance of Biomimetic Moth Based on Flapping Function Control
## 3 Calculation and Simulation
### Lift Calculation of Rigid Wing Flapping Motion
In summary, the variables affecting the average lift include \(f\), \(\phi_{max}\) and \(\phi_{0}\), which are all parameters of the flapping function. \(f\) is the flapping frequency, and its value range is \(f\in[0,+\infty)\). \(\phi_{max}\) is the flapping amplitude, and \(\phi_{0}\) is the mid-position angle; its value typically satisfies \(-\frac{\pi}{2}\leq-\phi_{max}+\phi_{0}\leq 0\leq\phi_{max}+\phi_{0}\leq\frac{\pi}{2}\). Usually, \(\phi_{0}\) is not too large, and it can be considered that its value range is \(\phi_{0}\in\left[-\frac{\pi}{18},\frac{\pi}{18}\right]\). Therefore, it can be considered that the value range of flapping amplitude is \(\phi_{max}\in\left[0,\frac{\pi}{2}\right]\). The relationship between the three and the average lift is shown in Table 1. In short, the larger \(f\) is, the larger \(\phi_{max}\) is, and the smaller \(|\phi_{0}|\) is, the larger \(\bar{F}_{L}\) will be. Further analysis can be seen that \(\phi_{max}\) and \(f\) have a more significant impact on \(\bar{F}_{L}\), while \(\phi_{0}\) does not have a significant impact on \(\bar{F}_{L}\). At the same time, in this calculation model, it is assumed that \(C_{Ld}\) and \(C_{Lu}\) are constants, but in actual conditions, this is not the case. The change in \(\phi_{0}\) will cause a change in the angle of attack and then affect \(C_{Ld}\) and \(C_{Lu}\). Therefore, the impact of the mid-position angle on lift needs to be further explored in the subsequent simulation and experiment.
## 3 Analytical Model
### Mechanical Analysis of Surrounding Rock
\tag{11}\]  
where \(\Delta s_{r}^{\mathrm{R}}(t)\) and \(\Delta s_{\theta}^{\mathrm{R}}(t)\) are the increments of the deviatoric stresses of the surrounding rock in the radial and tangential directions, respectively; and \(\Delta\sigma_{m}^{\mathrm{R}}\) is the increment of the mean stress. The strain of the surrounding rock induced by the increments of the deviatoric stress in the tangential direction can be given as follows:  
\[\varepsilon_{\theta}^{\mathrm{RP}}(t)=J(t)\Delta s_{\theta}^{\mathrm{R}}(0)+ \int_{0}^{t}J(t-\xi)\left[\Delta s_{\theta}^{\mathrm{R}}(\xi)\right]^{\prime}d\xi \tag{12}\]  
Based on the geometric equation, the radial deformation of the surrounding rock induced by the tunnel excavation can be expressed as follows:  
\[\varepsilon_{\theta}^{\mathrm{RP}}(t)=\frac{u_{r}^{\mathrm{RP}}(t)}{r}\Rightarrow u _{r}^{\mathrm{RP}}(t)=J(t)\lambda(0)rP_{0}+r\int_{0}^{t}J(t-\xi)\lambda^{ \prime}(\xi)P_{0}d\xi \tag{13}\]  
Substituting \(r=R_{2}\) and Equation (2) (see expression J(t)) into Equation (13), the radial deformation of the surrounding rock on the tunnel wall can be calculated as follows:  
\[u_{r}^{\mathrm{RP}}(t)\Big{|}_{r=R_{2}}=\frac{R_{2}P_{0}}{2}\Bigg{[}\frac{1}{G _{\mathrm{m}}}+\frac{t}{\eta_{\mathrm{m}}}+\frac{1-e^{-t/t_{\mathrm{k}}}}{G_{ \mathrm{k}}}\Bigg{]}-\frac{\alpha R_{2}P_{0}}{2}\Bigg{[}\frac{1}{\beta\eta_{ \mathrm{m}}}-(\frac{1}{\beta\eta_{\mathrm{m}}}-\frac{1}{G_{\mathrm{m}}})e^{- \beta t}+\frac{e^{-\beta t}-e^{-t/t_{\mathrm{k}}}}{G_{\mathrm{k}}(1-\beta t_{ \mathrm{k}})}\Bigg{]} \tag{14}\]  
1. [label=(2)]
2. Lined tunnel The lining is assumed to be installed at time \(\tau=t-t_{0}\). The mechanical analysis of the surrounding rock under the fictitious support pressure is similar to that under the supporting pressure provided by the lining. Therefore, combining Equations (1)-(3) and Equation (12), the radial deformation of the surrounding rock can be given as follows: \[u_{r}^{\mathrm{RQ}}(\tau)=r\bigg{[}Q(0)J(\tau)+\int_{0}^{\tau}J(\tau-\xi)\frac {dQ(\xi)}{d\xi}d\xi\bigg{]}\] (15)  
The radial deformation of the surrounding rock, induced by the supporting pressure provided by the lining on the external surface of the tunnel wall, can be obtained as follows:  
\[u_{r}^{\mathrm{RQ}}(\tau)\Big{|}_{r=R_{2}}=R_{2}\bigg{[}Q(0)J(\tau)+\int_{0}^ {\tau}J(\tau-\xi)\frac{dQ(\xi)}{d\xi}d\xi\bigg{]} \tag{16}\]  
According to the Riemann-Stieltjes integral [40], Equation (16) can be transformed as follows:  
\[u_{r}^{\mathrm{RQ}}(\tau)\Big{|}_{r=R_{2}}=R_{2}\bigg{[}Q(\tau)J(0)+\int_{0}^ {\tau}Q(\xi)\frac{dJ(\tau-\xi)}{d(\tau-\xi)}d\xi\bigg{]} \tag{17}\]  
After the lining is installed, the increment of the radial deformation of the surrounding rock on the tunnel wall, caused only by the tunnel excavation, can be obtained as follows:  
\[R_{2}h(\tau)=\left.u_{r}^{\mathrm{RP}}(\tau+t_{0})\right|_{r=R_{2}}-u_{r}^{ \mathrm{RP}}(t_{0})\Big{|}_{r=R_{2}} \tag{18}\]
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 5 Conclusions  
In this paper, we propose a hierarchical blockchain-based trust measurement model for drone cluster nodes, which addresses the issues of low accuracy in node trust evaluation and untrustworthy cross-domain sharing of drone trust values caused by high dynamic entry and exit of nodes in the trust cluster. Introducing the PBFT and PoW two-layer blockchain consensus mechanism and the dynamic evaluation method of UAV node trust based on task perception ensures the trustworthy evaluation of trust values and the inheritability of historical trust values. Through the double-layer blockchain system framework, it is possible to achieve multiple blocks at the same time as the packaging chain, improving the efficiency of the blockchain data chain. At the same time, in view of the limited computing power and communication resources of drones, the resource allocation optimization design of layered blockchain architecture under resource-limited conditions is constructed. By formulating the data throughput maximization problem and solving it by convex optimization, the throughput of blockchain data on the chain is improved. In this paper, NS3 and MATLAB simulation platforms are used to build a simulated UAV cluster scenario, and trust value evaluation algorithms are deployed on UAV nodes. Meanwhile, compared with other existing schemes, the effectiveness of the measurement algorithm in this paper is verified through indicators such as the accuracy rate of malicious nodes, robustness of trust value changes, blockchain system time delay, and throughput under different UAV cluster scenarios. The simulation results show that the scheme has good performance.  
Conceptualization, J.Z.; Formal analysis, J.Z.; Investigation, Z.W., J.L. and L.Z.; Software, J.Q. and P.G.; Supervision, R.C. and Y.L.; Validation, J.L. and Y.L.; Writing--original draft, J.Z., J.Q., P.G. and Z.W.; Writing--review and editing, J.Z. and R.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2022 YFB3104901 and 2021 YFB3101903.  
The data presented in this study are available on request from the corresponding author.
# Magnetic Nanoparticles as Mediators for Magnetic Hyperthermia Therapy Applications: A Status Review
## 2 Syntheses of MNPs
Ni\({}_{1-x}\)Cu\({}_{x}\) MNPs (x = 40, 30, 27.5, 27, 25, 20) were prepared by mechanical milling. We milled Cu (grain size \(<\) 63 \(\upmu\)m) and Ni (grain size \(<\) 150 \(\upmu\)m) in a SPEX (Metuchen, NJ, USA) 8000M and 8000D at 1425 rpm, using steel vials, while the balls-to-powder ratio was 20:1. We milled for 20 h under an inert N\({}_{2}\) atmosphere. We also added NaCl during milling to avoid agglomeration of MNPs [55]. Cr\({}_{x}\)Ni\({}_{1-x}\) MNPs were prepared by water-in-oil microemulsions and mechanical milling. The titration method was used to determine the stability range of the microemulsion, with the phase diagram of the composition of water/CTAB and n-butanol/isooctane serving as a starting point. We prepared two microemulsions, the first was by dissolving aqueous Ni\({}^{2+}\) (0.4 M) and Cr\({}^{3+}\) (0.1 M) ions, and the second by adding NaBH\({}_{4}\) (0.8 M) to a mixture of CTAB, n-butanol, and isooctane. Both microemulsions were of the same volume; subsequently, after two hours of mixing in an inert N\({}_{2}\) atmosphere, a black solution was obtained. The mixtures were centrifuged to separate the black MNPs and washed several times with methanol. Finally, the as-prepared alloy powder was heat-treated at 200, 300, 400, and 600 \({}^{\circ}\)C. Cr\({}_{\rm x}\)Ni\({}_{1-x}\) MNPs were also prepared by mechanical milling. Cr (particle size \(<\) 74 \(\mu\)m) and Ni (particle size \(<\) 150 \(\mu\)m) were milled in a SPEX 8000M mill at 1425 rpm, where we used steel vials, while the ratio between balls and powder was 20:1. We milled for 20 h in an inert N\({}_{2}\) atmosphere [107].
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 5 Targeted Safety Interventions
### Vehicle Features: ABS, AirBags, and ADAS  
[PERSON] [100] found that frontal airbags cut driver fatalities by 22% for belted occupants but provide no net benefit to unbelted drivers, contradicting earlier claims of airbag-induced risk.  
[PERSON] and [PERSON] [101] evaluated three pre-collision system (PCS) algorithms; the most comprehensive one (FCW + PBA + PB) reduces injury severity by up to 34% and could prevent 3.2-7.7% of rear-end crashes.  
[PERSON] et al. [102] compared 1001 SAE-L2 ADAS and 548 SAE-L4 ADS crashes, finding L2 events cluster on highways and L4 in urban areas; low mileage and new-technology generation correlate with lower injury odds.  
Vehicle safety technology research reveals important patterns in system effectiveness and user interaction dependencies. The effectiveness of safety systems varies dramatically with user behavior--airbags provide a 22% fatality reduction for belted drivers but no benefit for unbelted drivers ([PERSON]), illustrating that passive safety systems require complementary protective behaviors. More advanced systems show greater effectiveness, with comprehensive pre-collision systems achieving an injury reduction of up to 34% ([PERSON] and [PERSON]) compared with airbags' 22% benefit. However, [PERSON] et al.'s analysis of automated driving systems reveals that the deployment context significantly influences the outcomes, with different automation levels experiencing distinct crash patterns (L2 on highways and L4 in urban areas). The correlation between low mileage and lower injury odds suggests learning effects or selection bias in early adopters. These findings indicate that vehicle safety technology effectiveness depends on the interaction among system sophistication, user behavior, and deployment context rather than technology capabilities alone.
## 3 WSN Topology  
In this work, we aim to reduce unnecessary energy consumption and to prolong the battery life of the network, along with the sensor and router nodes that are distributed across a farm field. Figure 1 shows the proposed topology of the wireless nodes in the farm field with a layout of 200 \(\times\) 200 m\({}^{2}\), which represents the available tested area in this study. This area can be potentially extended to actual medium-size commercial crop farm areas. The farm field consists of 16 sensor nodes, four router nodes, the main router node, and the coordinator node. Each sensor node is responsible for collecting climate conditions, such as air temperature, air humidity, and soil moisture, from a square area of 50 \(\times\) 50 m\({}^{2}\). The sensor node is located at the center of the square area. Four sensor nodes communicate wirelessly to one of the router nodes. The collected data (i.e., climate conditions) from the sensor nodes are transmitted wirelessly to the central router via the router nodes.  
The main router node passes the climate conditions to the coordinator node, which is located at the base station (farmhouse). Our proposed topology considers only fixed router nodes for communication between sensor nodes and the coordinator node. In addition, the changing roles of the router nodes are not considered, as shown in Figure 1, because the focus of this study is the physical layer approach for achieving energy efficiency, whereas changing roles involve higher layers, such as media access control (MAC) and network. Thus, the changing roles can provide a future opportunity to continue the current study to further understand the benefits of energy savings based on cross-layer dimensions.  
The distance between the farmhouse and the crop field is approximately 200 m. Therefore, an appropriate wireless communication protocol, such as Zigbee (XBee S2C), must be used to ensure data delivery. This module can theoretically guarantee 1.2 km in outdoor environments [38], low power consumption, low cost, and battery operation in WSNs. In terms of power consumption, Zigbee (XBee S2C), which consumes 36.9 mW as reported in [13], is better than LoRa, SigFox, WiFi, and GPRS, which consume 100, 122, 835, and 560 mW, respectively. Nevertheless, the current work can be extended to cater to future wireless technologies, including LoRa.
## 2 Materials and Methods
### Principal Component Analysis  
Issues such as high dimensional input space, variables correlation, and insufficient training samples can create problems in the learning process, and the conditions might become worse when we want to spatially interpolate values for various locations within a city, but with few observation points [75]. It becomes inevitable to implement dimension reduction methods to reduce the number of correlated variables into the uncorrelated ones. Through application of PCA, while maintaining the highest variation and dispersion in the data, one can transform the input variables into a set of new uncorrelated variables called the principal components [76; 77]. Equations (5) and (6) are used to provide linear transformation from the input space to the principal component space. Here, the orthogonal linear transformation matrix is defined by P, Z represents the original data matrix, according to which, each row denotes a variable, and Y represents the transformed matrix. In this matrix, each row denotes the uncorrelated principle components.  
\[PZ=Y \tag{5}\]\[\left[\begin{array}{ccc}P_{1.T_{1}}&\ldots&P_{1.T_{m}}\\ \cdot&\hskip 28.452756 pt\cdot\\ \cdot&\hskip 28.452756 pt\cdot\\ \cdot&\hskip 28.452756 pt\cdot\\ P_{m.T_{1}}&\ldots&P_{m.T_{m}}\\ \end{array}\right]\hskip-28.452756 ptZT_{1}(x_{1})\ldots&ZT_{1}(x_{n}) \tag{6}\] \[=\left[\begin{array}{ccc}y_{1}(x_{1})&\ldots&y_{1}(x_{n})\\ \cdot&\hskip 28.452756 pt\cdot\\ \cdot&\hskip 28.452756 pt\cdot\\ \cdot&\hskip 28.452756 pt\cdot\\ y_{m}(x_{1})&\ldots&y_{m}(x_{n})\\ \end{array}\right]\]  
The transformation matrix (P) is obtained from the eigenvalues (\(\lambda 1,\lambda 2,\ldots,\lambda 1\)) of the covariance matrix of the original variables by applying PCA. The rows of this matrix represent the corresponding eigenvector. The eigenvectors specify the directions of the new space, and the eigenvalues specify their magnitude [77; 78]. In order to find which eigenvector(s) could be removed without much affecting the information needed for building a subspace with lower dimensions, we should inspect their corresponding eigenvalues. Those eigenvectors which have smaller corresponding eigenvalues are those that have lower information on the data distribution and can be removed.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges
#### 7.2.8 Potentials of THz Wireless FSO Link Communication  
One of the key wireless technologies recently being considered for data transfer rates of higher than tens of Gbps and a few Tbps is Terahertz (THz) wireless communication. Data rates up to 10 Gbit/s below 300 GHz and below 10 Gbit/s above 500 THz have been demonstrated [500]. THz beam propagation is significantly affected by attenuation due to molecular absorption (mainly due to oxygen and water vapor) and random fluctuations of a THz signal caused by atmospheric turbulence. A THz communication system's performance was investigated in terms of BER and channel capacity under various atmospheric attenuation and turbulence conditions [501]. The effects of pointing errors on average BER was also reported in [501]. The authors in this paper compared the average BER between UOC (at 780 nm wavelength) and THz links, showing that as the gain of the Tx/receiver antennas in the THz link increases, the BER performance of the THz link may provide a better performance than the UOC link under similar atmospheric conditions and path lengths. The details can be found in their paper. The effect of atmospheric turbulence on Terahertz communication as a specific type of additive noise and its influence on THz communication is reported in a recent paper [502]. The authors also investigated how different atmospheric turbulence conditions affected the upper limit of bandwidth efficiency.