esa-sceva/satcom-chunk-collection huggingface dataset:
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 2 Motivation
### Middleboxes' Placement  
Due to network structure limitations and policy constraints, we found that the middleboxes in the surveyed networks are physically located between networks to be able to enforce certain policies on a particular group of services and/or end-hosts, which are defined here as in-scope systems. Despite the fact that this enables middleboxes to be on the path of in-scope traffic, it complicates the network management and hinders the deployment of recent network agility services [1; 10]. In reality, middleboxes, such as load balancers, VPN servers, and WAN optimizers, which are physically installed in the path of bi-directional traffic, are often useless and may add an extra overhead to the exchanged traffic [6]. For instance, the potential speedup from deploying the load balancer is \(\theta(t)\); however, the load balancing algorithm introduces an extra overhead \(L_{overhead}\) to perform its load-balancing function. When the delay of \(L_{overhead}\) is greater than the obtainable speedup \(\theta(t)\), it is better to bypass the load balancer until the obtainable speedup \(\theta(t)\) is greater than the load-balancing overhead \(L_{overhead}\). In fact, researchers found benefits in bypassing the load balancer even in the return path of network traffic [18]. To give another example, let us consider the voice and video traffic as examples of delay-sensitive flows. Holding such traffic back in the WAN optimizer for compression will introduce an unnecessary delay and jitter. Additionally, the video streams are already compressed, so additional compression will not be beneficial. Consequently, VoIP or video should bypass the WAN optimizer.
# 5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models
## 4 Simulation Results
### Comparison of the Proposed Approach with Published Literature  
In Table 13, we compare the proposed approach presented in this paper with the recent published literature (since 2019). From the comparison presented in Table 13, only our proposed approach investigates 4G-LTE and 5G-NR coding performances for all V2X channel scenarios (highway-LOS, highway-NLOSv, urban-LOS, urban-NLOS, urban-NLOSv), as defined by 5G-V2X release 16 standard and for short V2X-compatible frame lengths.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 3 Stray Capacitance of Rogowski Coils
### A Network Model of the Stray Capacitance of Rogowski Coils  
Based on Figure 4, the capacitance network of Rogowski coils is depicted in Figure 5, in which the edge effect of the winding at both ends is ignored [18]. In Figure 5, each marked node represents a single turn of the winding, and the grounding shield is modeled as a reference node \(S\). The stray capacitance \(C_{c}\) is defined as the lumped stray capacitance between the two terminal nodes A and B of Rogowski coils; thus, \(C_{c}\) is referred to as \(C_{AB}\) hereinafter. After (\(N\)-2) times of the star-delta transformation, the intermediate nodes are eliminated step by step, and the \(\pi\)-type equivalent circuit of the stray capacitance network can be obtained, and the stray capacitance \(C_{AB}\) can be calculated accordingly. However, when the number of nodes is large, this approach is very cumbersome.  
Figure 4: A lumped network model of Rogowski coils.  
Figure 5: A network model of the stray capacitance of Rogowski coils.
# Analysis and Synthesis of Architectures for Automotive Battery Management Systems
## 3 Design of a Distributed BMS and Common BMS Functions  
This chapter describes and analyses how a BMS is designed today. Figure 3 shows a signal and circuit diagram of an exemplary BMS with distributed master and slave units including further relevant components. In addition, common functions of a BMS are described in the following as well.  
In general, a BMS collects information about the present state of the battery in a car. Whether in distributed, centralized or modular architectures, its task is to let the battery operate in a safe and efficient way. To do so, it measures voltages, temperatures by its cell measurement or slave modules. Moreover, the current flowing into or out of the battery is measured. On the master unit, more precisely on its micro control unit (MCU), all the information is stored and the SOH, SOP, SOC and the SOL are calculated.
## 2 Materials and Methods
### Study Procedures  
This study employed a crossover design. Participants randomly performed two distinct interventions using an FB and an elastic bandage (EB). The main differences between the two wraps are the material texture and the tension applied in tissue flossing (blood flow restriction). FBs are made of natural rubber or latex and are available in various lengths, widths, and thicknesses [18]. EB is made of a cotton and elastic yarn mix [19]. EB must be applied using gentle, comfortable wrapping because excessive tightness impedes limb circulation. Clinically, EB is used for edema control, wound dressings, and muscle and joint support. Therefore, the wrapping duration tends to be longer than that of FBs. FB is used in myofascal compression interventions and rehabilitation and by sports medicine professionals, where they are used in tissue flossing interventions (lasting 2-5 min) that may also include functional movements of the wrapped limb. For effective tissue flossing, FB exhibits greater tension when stretched (stretch force range of 50% to 90% of the band length) relative to EB [13; 18; 20]. Therefore, precautions should be taken during FB use. Prior to the start of the assessment session, participants underwent a familiarization session in which they were instructed on how to use an FB and an EB. During this orientation, participants were familiarized with the procedures and practiced using the assessment tools and equipment of the study. One day after the familiarization session, each participant completed two assessment sessions in a randomized order, with 48 h of rest between each session. Participants threw a die to the randomized exercise assignment by themselves. Odd numbers represented EB; even numbers represented FB [21].  
Participants were requested to avoid vigorous activities for 24 h before each assessment session. Before beginning the experiment, each participant was instructed to perform five sets of knee flexion and extension at an angular velocity of 180\({}^{\circ}\)/s on a Biodex system (Mirion Technologies, Shirley, NY, USA) as a general warmup. Subsequently, participants performed the preintervention measurements in the following order: knee joint ROM, proprioception, isokinetic muscle strength test, and Y-balance test (YBT). After completion of the preintervention measurements, participants completed the intervention (FB or EB) in a randomized order. Postintervention measurements were conducted in the same order as preintervention measurements: immediately after the intervention and 20 min later.
# Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Drivers
## 6 Conclusions  
A new lane-changing path planning method for unmanned vehicles is proposed in this paper. It is developed to generate a path that can make the vehicle finish the lane-changing operation smoothly and safely based on the information of vehicle speed, steering intention, obstacle and driving style. First, to obtain the lane-changing paths of the excellent drivers under different conditions, many real vehicle tests were carried out. Then, the final point and intermediate feature point of each lane change trajectory were extracted, with the expression of each path obtained. Next, the excellent driver model based on the GA-BP neural network was established and trained with experimental data. Finally, the path planning algorithm for imitating the lane-changing operation of excellent drivers was developed and presented.  
The path developed in this paper can imitate real excellent driver lane-changing path to meet the kinematic constraints of the vehicle and avoid the obstacles at the same time. Simulation results verifies that the path planning method proposed in this research can generate an optimal lane-changing trajectory according to the vehicle driving condition and the driving styles selected by the passengers, which is basically the same with the trajectory selected by the excellent driver under the same conditions and the lateral error between the two trajectories is small. Therefore, the lane-changing path planning algorithm proposed in this paper can imitate the excellent driver to change the lane, improve the stability of the unmanned vehicle, improve the riding comfort of passengers, and reduce the probability of passengers suffering from motion sickness.  
[PERSON] and [PERSON] conceived of and designed the method. [PERSON] and [PERSON] performed the experiments and analyzed the experimental data. Finally, [PERSON] wrote the paper with the help of [PERSON], [PERSON] and [PERSON].  
This work was financially supported by The National Natural Science Fund (No. U1564201 and No. 516/5235), Six Talent Peaks Project Fund of Jiangsu Province (No. 2015-XYQC-002), and Senior Talent Fund of Jiangsu University (No. 15 JDG075).  
Figure 19: The simulation results at the speed of 40 km/h under the free lane-changing conditions of the conservative driver: (**a**) simulation trajectory and actual trajectory; and (**b**) lateral deviations between the simulated and real trajectories.
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Experimental Platform Data Collection  
The experiments in this paper are carried out on an experimental transmission platform of a three-phase asynchronous AC motor, which is arranged as shown in Figure 5. The experimental platform mainly includes a motor, reducer, torque sensor, eddy current brake, various sensors, and data acquisition card (model: USB2085, 16 channels analog input, the sampling frequency is the highest: 250 kHz). The sensors mainly include voltage sensors (range: 0-500 V, accuracy is 0.2%), current sensors (range: 0-10 A, accuracy is 0.2%), vibration sensors (range: 0-50 mm/s, sensitivity: 20 mv/mm/s, response frequency: 10-1000 Hz) and temperature sensor (range: 0-50 mm/s, accuracy: 0.2%). The platform collects various sensor data of the motor under different fault types as the input of the fault diagnosis model. We set a sample of 1 million data for each fault category.
###### Abstract  
A narrowband dual-band bandpass filter (BPF) with independently tunable passbands is presented through a systematic design approach. A size-efficient coupling system is proposed with the capability of being integrated with additional resonators without increasing the size of the circuit. Two flag-shaped resonators along with two stepped-impedance resonators are integrated with the coupling system to firstly enhance the quality response of the filter, and secondly to add an independent adjustability feature to the filter. The dual passband of the filter is centered at 4.42 GHz and 7.2 GHz, respectively, with narrow passbands of 2.12% and 1.15%. The lower and upper passbands can be swept independently over 600 MHz and 1000 MHz by changing only one parameter of the filter without any destructive effects on the frequency response. According to United States frequency allocations, the first passband is convenient for mobile communications and the second passband can be used for satellite communications. The filter has very good in- and out-of-band performance with very small passband insertion losses of 0.5 dB and 0.86 dB as well as a relatively strong stopband attenuation of 30 dB and 25 dB, respectively, for the case of lower and upper bands. To verify the proposed approach, a prototype of the filter is fabricated and measured showing a good agreement between numerically calculated and measured results.  
Keywords:dual-band bandpass filter (BPF); end-coupled transmission lines; flag-shaped resonator; lumped circuit model; microstrip technology; microwave passive components; tunable passbands +
Footnote †: journal: Electronics Electronics
## 4 Conclusions  
1. On the basis of the leave-one-out cross-validation method, the homotopy continuation method was used to optimize the LSSVM model parameters with the goal of minimizing the sum of squares of the prediction errors of the full sample retention one, and then the HC-LSSVM model was constructed, which solved the problems of low search efficiency in the search process and lack of global optimal solution in the search results of the existing LSSVM models.
2. Comparing with training samples and test samples, the HC-LSSVM model can accurately predict soft soil settlement, and the prediction result is significantly better than that of ordinary LSSVM model.
3. The research results provide a new method for the prediction of soft soil settlement. The prediction of future settlement amount based on the existing observation data can effectively prevent the occurrence of disasters.  
Conceptualization, C.Z. and Z.L.; methodology, Z.L.; software, G.C. and S.X.; validation, Z.L. and G.C.; formal analysis, Z.L. and G.C.; investigation, G.C. and S.X.; resources, C.Z. and Z.L.; data curation, G.C. and S.X.; writing--original draft preparation, G.C. and S.X.; writing--review and editing, G.C. and Z.L.; visualization, G.C. and Z.L.; supervision, G.C. and Z.L.; project administration, C.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Project, Grant Number 2017 YFC1501203 and 2017 YFC1501201; the National Natural Science Foundation of China (NSFC), Grant Number 41977230; and the Special Fund Key Project of Applied Science and Technology Research and Development in Guangdong, Grant Number 2015B090925016 and 2016B010124007.  
Not applicable.  
The data presented in this study are available in the article.  
The authors would like to thank the anonymous reviewers for their very constructive and helpful comments.  
Figure 5: Comparison between the HC-LSSVM model and other research results.
## 4 Experiments and Results
### Clustering  
We performed clustering analysis to identify distinct learning behavior patterns among students based on their interactions with the course, using the features presented in Section 3.6. All variables were z-score normalized (\(\mu=0\) and \(\sigma=1\)). We created four student groupings, one for each CWA\({}_{U}\) threshold, using the k-means algorithm. Since k-means require a predetermined number of clusters, we utilized the Elbow and Silhouette methods to determine the optimal number of clusters [39]. We found that the optimal number of clusters for each CWA\({}_{U}\) threshold is \(k=4\). ANOVA revealed significant differences among the four groups (\(p\)-value < 0.05) for all features across every CWA\({}_{U}\) prediction threshold, except for forum_searched. Table 7 presents the mean and standard deviation of a subset of the features at the centroid of each cluster and for different CWA\({}_{U}\) values. The index of the clusters grows with the weighted average, i.e., Cluster 0 and Cluster 3 correspond to the clusters where the centroid has the smallest and largest weighted average grade, respectively. The clustering analysis across different CWA\({}_{U}\) thresholds reveals distinct patterns in student engagement with multimedia, video, simulations, and workshops, which correlate with their performance, as measured by the weighted average (see Table 7). These findings align with the previous feature importance analysis, highlighting the critical role of early interaction with multimedia and video resources in predicting student success. Students in higher-performing clusters consistently engage more with multimedia and video resources at early CWA\({}_{U}\) stages, suggesting that early and frequent interaction with these materials improves outcomes. As students progress to higher CWA\({}_{U}\) thresholds, the importance of simulations and workshops becomes more pronounced, particularly in
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### Parallel scheme  
The nodes in the parallel structure's reservoir can be implemented by SOA [35], silicon-based micro-ring resonators [36], silicon-based waveguide delay lines [37], etc. Optical reservoirs based on SOA and MRR, respectively, utilize the gain saturation of SOA and the nonlinear effects of MRR (free carrier dispersion and thermo-optical effects). No matter what kind of device, the simplest way to realize interconnection is to return the output signal to the input node. In this way, feedback can be achieved within the network. When there are multiple inputs, parallel operation is realized by inputting different signals.
## 3 Materials and Methods
### Biological Data Collection
#### 3.1.1 Bacteria Distribution  
Bacteria distribution data were collected using sand columns. All experiments were conducted with technical triplicates. Syringes (100 mm length, 25.585 mm ID) were used to facilitate column experiments. A filter paper (2.5 \(\upmu\)m particle retention) and cotton layer were positioned at each end of the syringes, and sand (50.9 g) was packed in between these layers. A rubber stopper was used to plug the syringe, and tubing (3 mm internal diameter, 5 mm external diameter) was connected to each end to facilitate the input and output of solutions which were pumped using a peristaltic tubing pump (CTP100, Fisher Scientific, Loughborough, UK) up through the sample from the bottom of the tube. _S. pasteurii_ was grown to early stationary phase growth and pumped (1.5 pore volumes (PVs)) upwards into syringes and left undrained for 24 h. Following this, cementation media (3 g L\({}^{-1}\) nutrient broth (NB), 187 mM NH\({}_{4}\)Cl, 333 mM urea, 250 mM CaCl\({}_{2}\)) were applied to syringes every 24 h for a total of 12 applications. Three syringes were sampled 24 h after each application of cementation media as well as 24 h after the application of _S. pasteurii_. Before sampling, syringes were flushed with 1.5 PVs of 18.2 M\(\Omega\)-cm H\({}_{2}\)O, then were cut into the top, middle and bottom sections (2.5 cm height). From each section, 1 g of sample was taken and suspended in 800 \(\upmu\)L 2.5% PEG (polyethylene glycol) 5000 and shaken on a vortex at maximum speed using a microcentrifuge attachment for 1 h. Following this, samples were centrifuged at 950\(\times\)\(g\) for 15 min to pellet sand. Inoculate (100 \(\upmu\)L) was serially diluted in 18.2 M\(\Omega\)-cm of H\({}_{2}\)O to 10\({}^{-1}\) and 10\({}^{-2}\) dilutions, which were spread onto 1.5% NB (13 g L\({}^{-1}\)) of agar supplemented with 333 mM of urea in duplicate. Plates were incubated at 30 \({}^{\circ}\)C for 42 h. Following incubation, plates were checked for colonies. Colony forming units (CFUs) were calculated using the following equation:  
\[\text{Colony}\text{ forming units mL}^{-1}=\text{(colonise number $\times$ dilution factor)}/\text{sample volume (mL)} \tag{7}\]  
For the model, only peak values obtained for top column sections were used. The determined concentrations likely represent an underestimation, as not all bacteria may detach from the sand into the solution. However, this error is assumed to be negligible.
# Morphological Study of _Bacillus thuringiensis_ Crystals and Spores
## 2 Materials and Methods
### Scanning Electron Microscopy (SEM) Image Acquisition  
A Hitachi S3200N scanning electron microscope was used to capture the crystal and spore images. With a voltage of 15 KV, the distance between the sample and the target was set at 4 mm. A simple preparation protocol was put in place in order to always have the same concentration of crystals and spores for the different strains when taking SEM images: 100 \(\upmu\)L of the stock solution (solution of pure crystals or spores, or a mixture of crystals and spores) was added to 1900 \(\upmu\)L of distilled water, to obtain a total of 2 mL. The tube was sonicated for 5 min at a frequency of 37 kHz to break up the aggregation and then the tube was vortexed. After that, 50 \(\upmu\)L was taken from the tube and placed on the sample holder covered with cleaved Mica. The sample holder was then placed in an oven at 37 \({}^{\circ}\)C to dry. After drying, the sample was metallized, forming a 2.5 nm gold-palladium deposit to allow the crystals and spores to become conductive. Finally, the sample was placed in the vacuum chamber of the SEM and images were taken.
the bipyramid base (BS) were measured (Figure 1a,b). For cubic crystals, the side of the square (SS) was measured (Figure 1a). For spherical crystals, the perimeter of the circle observed in the images was measured and the mean radius (\(r\)) of the sphere synthesized by the _israelensis_ strains and strain H3 was calculated (Figure 1c). Finally, as shown in Figure 1d, the lengths (L) and the widths (W) of the spores were measured.  
The aspect ratio for the bipyramidal crystals was calculated by dividing the base of the square of the bipyramid (BS) by its height (2 h). For the spores, the aspect ratio was determined by dividing the width of the spore (W) by its length (L) [35]. The uncertainty (Unc) was calculated according to Student's law while taking into account the number of measurements \(N\)(larger than 100) and the standard deviation of the \(N\) measurements \(\sigma(X_{i})\) as follows: \(Unc=\frac{1.96\times\sigma(X_{i})}{\sqrt{N}}\). The histograms were then established by choosing the number and widths of bins according to [PERSON]'s rule [36].