esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Grid Cell Construction  
Following trip identification, all valid freight trips were aggregated into a uniform \(1\times 1\) km spatial grid covering the entire administrative boundary of Shanghai, including Yangshan Port, which is offshore. Although administratively affiliated with a neighboring city, Yangshan Port functions as an essential node in Shanghai's multimodal logistics system. In previous studies of HDT mobility in Shanghai, the port was frequently excluded due to administrative boundary constraints, resulting in the omission of a substantial volume of port-related freight activities. This exclusion limited researchers' ability to comprehensively capture the full scope of urban freight flows in the region.  
To address this gap, this study explicitly incorporates Yangshan Port into the spatial framework, thereby enabling a more holistic representation of urban freight mobility. In order to preserve the functional coherence of the port, it was designated as a single unified spatial unit rather than subdivided into standard grid cells. This methodological decision reflects the port's role as a cohesive logistical complex, physically connected to Shanghai's mainland via the Donghai Bridge, a dedicated highway corridor. By maintaining the spatial continuity of this key logistics hub, the analysis avoids the artificial fragmentation of freight activity and more accurately captures Yangshan Port's influence on regional transport dynamics. Figure 3 illustrates the study area, the constructed spatial grid, and several key logistics zones, providing a clearer understanding of the analytical context and spatial structure employed in this research.  
The remainder of Shanghai territory was partitioned uniformly using \(1\times 1\) km grid cells, a resolution selected to balance spatial granularity with computational efficiency for analyzing urban-scale freight mobility. We visually validated the grid resolution by integrating satellite imagery, demonstrating that it effectively covers Shanghai's major freight nodes. This ensures the consistency of functional analysis without fragmenting key zones. Trip endpoints were geolocated to their corresponding grid cells based on geographic coordinates. To ensure a focused analysis of locally relevant freight activity,  
Figure 2: Spatial distribution of identified freight trips at district level.  
spatial filtering was applied to retain only those trips that both originated and terminated within the study area. This refinement yielded a final dataset comprising approximately 1.25 million freight trips, forming the empirical foundation for the subsequent modeling and comparative analysis of HDT movement patterns.
## 3 Experiment Setup and Data Acquisition  
In order to validate the effectiveness and superiority of the proposed method for sliding bearing fault diagnosis, the experimental platform called Bently Nevada rotor kit is shown in Figure 7. The experimental platform is consisted with four parts marked by 1-4 respectively and including data transmitter, oil pump, rotating controller, and rotating device. Therein, a partial enlarged view of part 3 is shown in the lower left corner of Figure 7. When the button on the left in the lower right corner is on the upper side, the rotating speed is set, and the opposite is displayed as the current rotating speed. The middle button is used to change the speed that the upper switch is speeding up quickly, down is speeding reduction. The right button is controller that can determine the state of the experimental platform that is on or off, and the red button is emergency stop button. The oil whirl (O) happens when the rotation frequency is greater than the natural frequency; the static unbalance and dynamic unbalance are physically modeled with horizontal hammers and flanges; the contact rubbing is modeled by the bolt contacting the rotation axis.  
Moreover, a partial enlarged view of part 4 is shown in the lower right corner of Figure 7. The right side of the rotating machine is the motor, and the left part is the bearing body that is integrated with the pump. The accelerometer is used to measure the vibration signals of the vertical direction by locating on top of the bearing housing. The sampling frequency is 100 kHz and the sampling time is 10 s. The PCB MA352A60 accelerometers (PCB Piezotronics Inc., Depew, NY, USA) are used to measure the signals of the vertical direction by locating on top of the bearing housing, and they have 10 mV/g sensitivity in the bandwidth from 5 Hz to 60 kHz. The raw signals measured with the accelerometer are transformed into an oscilloscope (Scope Coder DL750) after being magnified by a sensor signal conditioner (PCB ICP Model480C02). Finally, the detailed information of data is described in Table 1.  
Figure 7: Experimental platform of sliding bearing.
###### Abstract
Therefore, this study aims to realize the comprehensive utilization of P2G technology, demand response, and carbon capture and storage means, optimize the operation of the energy system in the industrial park, and achieve the win-win research goals of saving energy, reducing carbon emissions, and gaining economic benefits. To do so, this study utilizes the aforementioned research and develops a thorough energy system model for an industrial park that integrates P2G and CCS. The model considers the carbon reduction advantages of substituting green hydrogen, linking power-to-gas with carbon capture and storage, implementing a hierarchical carbon trading system, and the influence of complete demand response on the economic and environmental performance of the system. A scheduling model for optimization is formulated, aiming to minimize the total cost of procurement, operation and maintenance, carbon emissions, and wind abandonment. The case study incorporates many control groups to validate the superiority of the proposed low-carbon scheduling technique. This paper's primary contributions are as follows:  
1. Suggesting a production technique that makes use of several hydrogen sources. This study explores the substitution of green hydrogen for a portion of traditional gray hydrogen as the hydrogen source, combined with a comprehensive assessment of the carbon reduction benefits of green hydrogen using a stepwise carbon trading approach. Furthermore, the study conducted an in-depth investigation into the effects of substituting green hydrogen for a portion of gray hydrogen on \"demand response\" and the coupling of P2G and CCUS. 2. The research examines the effects of incentive-based demand response and price-based demand response on the economic performance of the system. It investigates the energy-saving potential of various types of demand response. This study measures the impact of incentive strategies and price systems on the energy consumption of the industrial park. 3. Implementation of a highly efficient CCS technique to capture and recycle CO\({}_{2}\), hence improving the economic viability of the system. The integration of P2G technology with CCS allows for the creation of a closed-loop system that effectively reduces carbon emissions in the industrial park. This integration also enhances the economic efficiency of the overall system. The remaining sections of this paper are organized as follows: Section 2 introduces the full energy system model incorporating hydrogen, which encompasses the modeling of key equipment; Section 3 provides frameworks for price-based and incentive-based demand response; Section 4 explores the optimization goals and limitations, establishing the framework for the optimization scheduling model; Section 5 performs simulated verification of the proposed optimization scheduling approach to showcase its efficacy; Section 6 examines the merits, drawbacks, and advancements of the suggested approaches; and Section 7 provides a concise overview of the research conducted in this research.
## 2 Engineering Background
### Model Establishment  
In order to determine the failure proximity of the concrete lining, and the crown deformation characteristics occurring in the rocks from the roof of the tunnel caused by extending the tunnel using the pipe-jacking method, a series of numerical simulations were carried out with a 3D-\(\sigma\) software that can calculate the stress conditions, displacement, failure, and various possible predictions of the rocks in this engineering problem.  
The failure proximity and crown deformation are considered in order to determine the possible length of the tunnel and the endurance of the concrete pipe. The failure proximity coefficient can reach up to one, which shows the possibility of damage and breakage in a concrete pipe. On the other hand, the full length of the tunnel can be determined using a failure proximity coefficient of one, in which the pipe-jacking process cannot continue further. After the boundary conditions, failure criterion, and model design were considered, the model was established, producing conditions similar to actual field conditions as illustrated in Figure 4. In addition, to reduce the number of errors and to control the effects of each variable, the model boundaries were pre-set until their effect was minimal or non-existent.  
Figure 2: Correlation between the uniaxial compressive strength of claystone and mining depth.  
Figure 4: (**a**) Schematic view of the model: (**b**) circular pipe and (**c**) rectangular pipe.  
Figure 3: Simplified geological stratigraphy column.  
Moreover, the three different tunnel inclination angles, the three highwall slopes, and the two types of tunnel shapes were assumed to be \(5^{\circ}\), \(10^{\circ}\), and \(15^{\circ}\); \(7.5^{\circ}\), \(15^{\circ}\), and \(30^{\circ}\); and circular and rectangular, respectively. In addition, the model's boundaries were constrained in all directions, except for the top boundary, which was free, and gravity was set as an initial stress condition. In the civil engineering of soft rock tunnels, some types of lubricant or grouting materials can be used and injected into over-cutting areas to decrease the frictional resistance of the concrete pipe and the tunnel surface during the thrusting process. It can also reduce the jacking force and energy consumption and can increase the jacking efficiency.  
The mechanical properties of claystone and other materials in Tables 1 and 2 that were used in the numerical simulation were obtained from the mining company with permission. The uniaxial compressive strength measured using the point load test on the borehole cores was obtain by some of the authors during field investigations at the coal mine.  
In this paper, we aim to study the effect of tunnel inclination, highwall slope, and pipe shape rather than the material properties and complex geology. Therefore, our model is homogenous, and the different rock layers and mechanical properties were not considered in this study.
## 4 Experiments
### Comparison with Baseline Techniques
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Dataset**} & \multicolumn{8}{c}{**Accuracy (\%) of Each Emotion**} \\ \cline{5-16}  & & & & & **A** & & **B** & **C** & **D** & **F** & **H** & **N** & **S** & **U** & **Avg** \\ \hline
[38] &
\begin{tabular}{c} Melspectrogram + MFCC + \\ Chroma + tonnetz + SC \\ \end{tabular} & CNN & EMO-DB & 100 & 61 & \(\times\) & 67 & 67 & 100 & 100 & 87 & \(\times\) & 86 \\ \hline
[39] & Features extracted using AlexNet. & DCNN & EMO-DB & 92 & 88 & \(\times\) & 99 & 92 & 92 & 90 & 93 & \(\times\) & 90 \\  & & SAVEE & 90 & \(\times\) & 48 & 50 & 47 & 82 & 58 & 53 & 67 \\ \hline
[40] &
\begin{tabular}{c} ZCR + MFCC + F0 + Energy \\ + HNR \\ \end{tabular} & fuzzy RF & EMO-DB & 77 & 71 & \(\times\) & 92 & 57 & 82 & 69 & 81 & \(\times\) & 81 \\ \hline
[41] & MFCC,MFM, LPCC, LFPC & SVM & EMOVO & 79 & \(\times\) & \(\times\) & 74 & 65 & 68 & 76 & 85 & 67 & 73 \\
[42] & Features extracted using CNN. & CNN & EMO-DB & 91 & 90 & \(\times\) & 87 & 92 & 66 & 85 & 88 & \(\times\) & 85 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Our \\ model \\ \end{tabular} } & SC + MFCC + \(\Delta\)MFCC + Melspectrogram + tonnetz + chroma & transformer & BAVED & 96 & \(\times\) & 94 & \(\times\) & 96 & 93 & 96 & \(\times\) & 95 \\  & & EMO-DB & 95 & 94 & \(\times\) & 100 & 94 & 78 & 98 & 97 & \(\times\) & 93 \\  & & SAVEE & 79 & \(\times\) & \(\times\) & 81 & 79 & 77 & 93 & 96 & 83 & 84 \\  & & EMOVO & 97 & \(\times\) & \(\times\) & 83 & 96 & 93 & 87 & 85 & 99 & 91 \\ \hline \end{tabular}  
* A = Anger, B = Boredom, C = Calm, D = Disgust, F = Fear, H = Happy, N = Neutral, S = Sad, U = Surprise, RF = Random Forest.  
\end{table} 
Table 6: Comparison of the proposed model and the baseline models using BAVED, EMO-DB, SAVEE and, the EMOVO datasets.
## 3 Methods
### Description of the Calibration Process  
The first step in the calibration process [43; 44] corresponded to the model made in Design Builder, based on the characteristics of the two buildings as they were built. This 3D model version was based on the available data of the building once executed: plans, details, component lists of the different installation systems, visits to the building under study, analysis of the building's energy management, etc. It also involved collecting data on site using measuring equipment. The ranges of accuracy, i.e., of error in this case, depended on the accuracy of the sensors and loggers. This process corresponded to experimental analysis, on-site data collection and monitoring of both roofs. With these data, a location-specific climate file was drawn up and entered as data in the Design Builder program. Measurements of surface and layer temperatures, indoor and outdoor air temperatures, relative humidity, etc. were obtained by simulating thermal behavior. They were compared with the actual data obtained in situ and the necessary parameters were adjusted so that simulation and actual behaviors were as close as possible. The second and final step was adjusting the values of ventilation or renewal of the interior air and infiltrations. Different simulations were performed varying the values of interior air renewal and infiltration, until the annual energy demand coincided with the results of actual energy consumption obtained from the meter.  
The only way to validate the results was the calibration process and its comparison with real experience. The introduction in the 3D model of new construction solutions--and their simulation after a calibration process--made the comparison with the actual constructive solution more reliable. In the case of the housing block's walkable inverted flat roof, the deviation between the simulated behavior and the actual behavior obtained during the monitored annual period was 5%. The deviation in the case of the water-covered roof of the MUA's Sempere Hall was 3%.
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 4 Discussion
### MIE of Driving Warnings Reveals a Positive Trend with the Number of Modalities
#### 4.3.3 MIE in Driving Warning Systems  
The current study compared the effectiveness of utilizing unimodal and multimodal driving warning systems and explored the facilitatory effect of multisensory integration on drivers' response time. In general, it is clear that MIE can be observed in the implication of multimodal driving warning systems as illustrated by the multisensory RSE hypothesis, where the implication of auditory-tactile bimodal warnings was found to facilitate drivers' response time to the best extent; also, drivers' response time under trimodal warning conditions was shorter than that under bimodal warning conditions, but not in a significant level. The reason why trimodal warnings were unable to provide a guaranteed MIE may be the potential influence by cognitive or sensory overload, or potentially ceiling effect of warning benefits.  
What's the attentional mechanism that allow bimodal warnings generate significantly quicker response time than unimodal warning, and trimodal warnings generate slightly quicker response time than bimodal warnings? If the sensory modalities (such as visual, auditory, tactile modalities) are independently processed according to the assumption of Multiple Resources Theory [115], the response time in multimodality warning conditions should not be quicker than the quickest unimodal warning condition. To generate quicker response time in multimodality warning conditions, multimodality information should be summarized from time to time and accumulated as cognitive processing proceeds. Thus, multimodality attention should not be independent, but linked together with information exchanged and combined. This theory is called _Attentional Co-Racing Theory_, which can explain and allow the multimodality warnings to generate quicker response time than unimodal warning (Figure 10).
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 5 Simulation Results and Analysis  
### Simulation Scenario and Parameter Settings  
As an example, a typical airborne scenario for distributed wideband interference was set for simulation. As shown in Figure 6, the orange points in the air craft are the receiver's array elements and the jammers are located on ground. When the air craft approaches the jammers, there is a handing over of the guidance mode. While other navigation methods might be faced with error accumulation or weather influence, it is crucial to keep GNSS receivers available before the handing over.  
This scenario determines the incident angle and received interference powers. A flight track of 270 km was set according to the flight height and earth curvature. If the length is longer than 270 m, the air craft and the jammers are invisible to each other in line of sight. A jammer distribution range along the x axis was set according to the practical scenario and 95 km in the figure above is an example. The air craft is at least 50 km from the jammers before the handing over of the navigation mode. The jammer incident angles in azimuth and elevation are shown in Figure 7a,b for the point in the simulation when the air craft flies over the jammers on the ground. During the initial period and midcourse of the flight, the approaching distance is much farther than the flight height, so the incident angles in  
Figure 5: Iteration algorithm flow chart.  
elevation are no larger than 15\({}^{\circ}\). The interference azimuth distribution \(\Delta\Psi\) is defined as the maximum difference between interference azimuth angles and a typical value taken from Figure 7b is \(\Delta\Psi\leq 90^{\circ}\). In the view that the distribution and number of jammers are usually uncertain, an assumption was made that the jammer incident angles are random and that they obey uniform distribution. Usually, the EIRP (equivalent isotropic radiated power) of jammers on the ground can be larger than 60 dBW. Considering the propagation loss, the \(JSR\) in Figure 7c is at 50 km. With the air craft shelter of around 20 dB, the \(JSR\) parameters in the simulation were set to 60-70 dB for each jammer.  
Figure 6: Sketch map of airborne scenario.  
Figure 7: Jammer incident angles and \(JSR\) during the flight. (**a**) Incident angles in elevation. (**b**) Incident angle in azimuth. (**c**) \(JSR\)s increase as air craft gets closer to jammers.  
On the basis of the above scenario, the Monte Carlo method was adopted in the simulation. The simulation parameters are shown in Table 3. In addition, the carrier frequency of 1268.52 MHz is one of the central frequencies of the BeiDou Satellite Navigation System. It was taken as an example, while other GNSS carrier frequencies in the L-band obey the same analysis rules and have similar simulation results.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 2 System Model
### System Description and RA Procedure
#### 2.1.3 Traffic and RF Channel Model  
The arrival of composite PTs (including initial transmissions and retransmissions) is modeled by a Poisson process with rate \(G\), which is also known as the offered traffic. Let \(N\) be the number of (legitimate) SNs contending for the same access slot. The distribution of \(N\) is therefore:  
\[\Pr\{N=n\}=\frac{G^{n}e^{-G}}{n!},\quad n=0,1,2,\cdots. \tag{1}\]  
Let us denote a representative SN by SN-A (i.e., anonymous SN) for the purpose of presentation. Let \(s\) and \(K\) be the number of available access codes and the number of SNs selecting the same code with SN-A for accessing the same slot, respectively. Given \(s\) and \(N=n\), the probability that SN-A collides with other \(K-1\) SNs that choose the same code is given by:  
\[\Pr\{K=k|s,N=n\}=\binom{n-1}{k-1}\bigg{(}\frac{1}{s}\bigg{)}^{k-1}\bigg{(} \frac{s-1}{s}\bigg{)}^{n-k},\quad 1\leq k\leq n. \tag{2}\]  
We assume that PRACH suffers from frequency-selective multipath Rayleigh slow-fading. Assuming that the shadowing and attenuation effects can be compensated by the open-loop power control used in UMTS RACH [19], the envelope of the received signal in one path is therefore a Rayleigh random variable. Let us assume that a perfect RAKE receiver with \(L\) fingers is used at HS. Additionally, we also assume that \(\mu\) is the average received power from each path, and the received power of each finger \(\mathcal{P}^{(l)}\) has an exponential distribution, i.e., \(f_{\mathcal{P}^{(l)}}(x)=e^{-x/\mu}/\mu\) (\(x>0\)). Then, the signal powers distributed in \(L\) independent paths can be aggregated together so that the total received power \(P=\sum_{l=1}^{L}\mathcal{P}^{(l)}\) has a gamma distribution, i.e.,  
\[f_{\mathcal{P}}(x)=\frac{x^{L-1}e^{-x/\mu}}{(L-1)!\mu^{L}},\quad x>0. \tag{3}\]  
It is noted that if we assume a narrow-band transmission like in narrow-band IoT, then PRACH can be assumed to suffer from a frequency-non-selective Rayleigh channel. In this case, \(L=1\) in (3) builds the probability distribution.
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epoch** & **Mini-Batch Accuracy** & **Vald. Accuracy** & **Mini-Batch Loss** & **Vald. Loss** \\ \hline
1 & 37.54\% & 57.22\% & 1.48 & 1.10 \\
4 & 68.53\% & 67.03\% & 0.82 & 0.94 \\
8 & 74.08\% & 70.44\% & 0.68 & 0.74 \\
12 & 79.09\% & 72.62\% & 0.55 & 0.68 \\
16 & 82.30\% & 75.20\% & 0.50 & 0.69 \\
20 & 83.55\% & 73.98\% & 0.41 & 0.75 \\
24 & 88.20\% & 74.52\% & 0.40 & 0.77 \\
27 & 88.09\% & 74.39\% & 0.32 & 0.84 \\
31 & 91.62\% & 74.39\% & 0.26 & 0.78 \\
35 & 92.04\% & 76.84\% & 0.23 & 0.83 \\
39 & 93.08\% & 76.29\% & 0.18 & 1.06 \\
43 & 94.56\% & 72.62\% & 0.16 & 1.14 \\
47 & 93.89\% & 76.98\% & 0.18 & 0.90 \\
50 & 94.48\% & 76.16\% & 0.15 & 1.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network training on 512-core Volta GPU result summary. Figure 10: Model performance without data augmentation. model fits new data. Very successful rates on both training and validation loss are achieved at a 30% dropout, which are well below 0.3 and 0.8, respectively. Figure 11: Model performance with data augmentation. Figure 12: _Cont._  
To study the precision over various label groups, the confusion matrix (error matrix) is plotted, which is shown in Figure 13. The label imbalance noted in the training set is an issue in the classification accuracy. The confusion chart (matrix) illustrates higher precision and recall for walls and confuses most tables with doors. Even if there are very few miss-predicted objects, the overall performance of the model to predict an object that was not contained in the training or validation sets is successful. Since the purpose of this study is to demonstrate a basic classification network training approach with raw data, possible next steps that could be taken to improve classification performance, such as re-sampling the training set or achieving better label balance or using a loss function more robustly to label imbalance (e.g., weighted cross-entropy,) will be explored in future studies. Figure 12: Improvement in validation accuracy and loss function of the model architecture. Figure 13: Confusion chart illustrating precision for each class.