esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Image Input Channel Optimization Experiment  
A comparative experiment of four-channel inputs and single-channel inputs was carried out on the four datasets, each with int8 and int16 quantization settings. The experimental results are shown in Table 4, where for the four-channel setting, data processing time includes the image reading, data rearrangement, output decoding, and target association times. For the single-channel setting, data processing time includes the image reading, differential operation, output decoding, and target association times. The experimental results are shown in Table 4.  
According to the table, by converting the image input from four channels to a single channel, the FPS increased from 28.86 to 51.09 under 16-bit quantization, reflecting an improvement of 43.51%. Similarly, under 8-bit quantization, the FPS increased from 32.20 to 55.73, representing an improvement of 42.22%. The results show that by optimizing the image input channel, the memory copy, hardware, and data processing times are reduced in both 16-bit quantization and 8-bit quantization.
# Evaluation of Glottal Inverse Filtering Techniques on
## 2 Analysis Methodology and GIF Methods
### IAIF-Based Approaches  
#### 2.2.1 Iair  
IAIF is a well-known technique to estimate the glottal source in speech signals. It is based on LPC without the need for the detection of the glottal closure phase (e.g., GCI and/or glottal opening instants, from which the percentage of voiced speech periods where the glottis is closed can be derived) [22]. This method is particularly effective in low-signal-to-noise-ratio environments compared to other similar techniques [17]. IAIF involves a two-step iterative process, where the initial estimation of both the GS and VT all-pole transfer functions is followed by a refinement stage. In each iteration, the effect of GS is firstly cancelled by inversely filtering the original speech with a low-order pre-emphasis LPC filter. From its output, the coarse estimation of VT is obtained with a higher-order LPC analysis. The glottal flow estimation derived from the inverse filtering of the original speech signal with this first VT coarse estimation is used as input in the second iteration to improve the accuracy of the low-GS-order LPC model. The glottal source order (henceforth denoted as \(N_{g}\)), fixed at 1 during the first iteration, can be increased during the refinement stage. The vocal tract order (hereafter denoted as \(N_{v}\)) is typically kept constant for both iterations [25; 31]. A simple pre-emphasis FIR filter with a leaky integration coefficient is used to model lip radiation (hereafter lip radiation coefficient is denoted as \(d\)).  
Finally, we also consider including a HPF with a 30 Hz cut-off frequency to remove undesirable fluctuations in the estimated glottal source [23].
# Non-Uniform-Illumination Image Enhancement Algorithm Based on Retinex Theory
## 3 Results
### Illumination Components  
In this section, we conduct a comprehensive evaluation and analysis of the algorithms presented in Section 2 based on subjective visual perception. To lay the groundwork for subsequent processing, we implement color correction as a preliminary step on the input image, which significantly reduces distortion introduced during image enhancement due to color deviation. Figure 6 illustrates the result of color balance correction on the original image (a), represented by (b). This preliminary adjustment significantly contributes to the precise extraction of illumination components in the subsequent images, rendering the entire process more refined and accurate.  
Figure 7b,c are the illumination component maps under different values of the Gaussian function scale factor \(\delta\) (i.e., \(\delta=80,\ \delta=200\)), and Figure 7d illustrates the illumination component extracted by the improved Gaussian function method proposed in Section 2.  
Figure 6: Color cast correction: (**a**) original image; (**b**) image after color correction.  
Figure 7: Extraction of light components at different scales: (**a**) input original grayscale; (**b**) the original Gaussian formula sets the illumination component image when the Gaussian scale factor is 80; (**c**) the original Gaussian formula sets the illumination component image when the Gaussian scale factor is 200; (**d**) the precise light component map solved by the algorithm in this paper.  
Figure 7 demonstrates that the multi-scale Gaussian function employed in this paper adeptly preserves the essential illumination information of the image, which meets the requirements of extracting illumination components.
## 4 Experiment and Results
### Calculation of Sentiment Polarity  
In this section, we performed several comparisons to validate the effectiveness of our approach. Recall that our target is to detect microblogs with risk of violence. Under this condition, SVM is a relatively appropriate classifier due to its good performance for classification of small samples. The results of different methods on the test corpus are listed in Table 4. For the SVM classifier, we selected sentiment words in the lexicon, numbers of each PoS, and emoticons as features to train the sample. Both polarity and number of emoticons were considered in the features. The parameters of the SVM classifier were selected in the training data by 10-fold cross-validation.  
In addition, a CNN model was also used for comparison. The CNN model has 128 convolutional units, with a context window of three. The number of hidden units is 300. The Adam optimization algorithm is used to update the parameters at the training stage, and features are extracted via a mean pooling algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **T = 0.3** & **T = 0.4** & **T = 0.5** \\ \hline N = 10 & 64.3 & 65.7 & 64.8 \\ N = 20 & 65.7 & 67.2 & 66.5 \\ N = 30 & 65.9 & 68.5 & 67.1 \\ \hline \end{tabular}
\end{table}
Table 3: Parameter setting.  
The first four methods utilize the semantic rules to calculate the final score because it is the criterion to decide the sentiment tendency of a text. Firstly, we compared three different methods of sentiment words selection, i.e., the dictionary method (dic), similarity method (sim), and a combination of the two (dic + sim). From the results, we can see that the combined method performs best. Then, taking the emoticons into consideration, the proposed approach of this paper is expressed by dic + sim + emo, which outputs the best results. Finally, compared with the machine learning methods, SVM and CNN, the accuracy of our approach is improved in both positive and negative corpuses. The training accuracy of the CNN is higher, but its predicative accuracy is lower than that of SVM and that of our approach. This can be accounted for by the overfitting at the training stage on our small datasets. The results show that more features can effectively improve sentiment classification performance in topic-independent datasets.  
In the next step, the violence- or terrorism-related words in the negative sentiment microblogs were detected. The resulting microblogs will be treated as key users for further validation.
# Influence of Linear Springing on the Fatigue Damage of Ultra Large Ore Carriers
## 5 Results and Analysis
### Influence of Wave Heading  
The definition of wave heading is shown Figure 12, where 0\({}^{\circ}\) is defined as the wave heading direction. Figure 13 shows the RAOs of vertical bending moment of the four OCs at different wave headings. The peak of the RAOs of vertical bending moment in the low frequency range appears at different wave headings, which corresponds to the characteristics of the rigid motion of the hull. The wave encounter frequency can reach a relatively very high level when the vessels advance at the service speed in head waves, as well as in quartering waves. Springing occurs when the wave encounter frequency is close to the first order wet natural frequency of the hull. Therefore, another peak in the high frequency range can also be observed in head and quartering waves, which is caused by the wave induced vibration. The springing is prominent for the wave heading range \(-60^{\circ}\) to \(60^{\circ}\) (300\({}^{\circ}\) to 360\({}^{\circ}\) and 0\({}^{\circ}\) to 60\({}^{\circ}\)), see Figure 14.  
Figure 11: The proportion of fatigue damage ratios in total damage at different navigational speeds.  
Figure 12: Definition of wave heading.  
The corresponding fatigue damage to the deck longitudinally at midship for each wave heading are shown in Figure 14. It can be seen that, in the heading range of \(\pm 60^{\circ}\) (head waves and bow quartering waves), the OCs experience large fatigue damage. This is because under these conditions, the encounter wave frequency will increase leading to the occurrence of springing. For the 400,000 DWT, 380,000 DWT and 300,000 DWT OCs, the fatigue damage is increased by 16.28%, 16.95%, and 16.41% due to the springing in bow quartering waves, respectively. For 250,000 DWT OCs, the fatigue damage is increased by 21.18%. Since the fatigue damage is insignificant and the influence of the wave induced vibration is not obvious in beam and stern quartering waves, these wave directions can be neglected in the analysis of the influence of springing on the fatigue damage of ultra large ore carriers.  
Figure 13: Transfer function of vertical bending moment in full loading condition.
# LEO Precise Orbit Determination with Inter-satellite Links
## 2 ISL Observation Model
After epoch naturalization and corrections of PCOs, PCVs, and relativistic effects, we can obtain the pure dual pseudo-code measurements at the same time, which are expressed as follows:  
\[\overline{\rho}_{A}^{B}(t_{0})=\overline{\rho}_{A}^{B}(t_{A})+\Delta\rho_{A}^{B} =\rho_{AB}(t_{0})+\delta_{B}(t_{0})-\delta_{A}(t_{0})+l_{B}^{R}-l_{A }^{S}+\varepsilon_{AB} \tag{4}\]  
\[\overline{\rho}_{B}^{A}(t_{0})=\overline{\rho}_{B}^{A}(t_{B})+\Delta\rho_{B}^{A} =\rho_{AB}(t_{0})+\delta_{A}(t_{0})-\delta_{B}(t_{0})+l_{A}^{R}-l_{B }^{S}+\varepsilon_{BA} \tag{5}\]  
where \(\rho_{AB}(t_{0})\) is the distance between satellite \(A\) and \(B\) at epoch \(t_{0}\); \(\Delta\rho_{A}^{B}\) and \(\Delta\rho_{B}^{A}\) denote the differences of the satellite distance and clock bias between the real observation epoch (i.e., \(t_{A}\) and \(t_{B}\)) and the naturalization epoch \(t_{0}\). They are defined as follows:  
\[\Delta\rho_{A}^{B}=\rho_{A}^{B}(t_{0})-\rho_{A}^{B}(t_{A}+\Delta t_{AB})+\delta _{B}(t_{0})-\delta_{A}(t_{0})-[\delta_{B}(t_{A}+\Delta t_{AB})-\delta_{A}(t_{A })] \tag{6}\]  
\[\Delta\rho_{B}^{A}=\rho_{B}^{A}(t_{0})-\rho_{B}^{A}(t_{B}+\Delta t_{BA})+\delta _{A}(t_{0})-\delta_{B}(t_{0})-[\delta_{A}(t_{B}+\Delta t_{BA})-\delta_{B}(t_{ B})] \tag{7}\]  
According to the analysis of BDS-3 ISL data, using broadcast orbits and clock biases to compute \(\Delta\rho_{A}^{B}\) and \(\Delta\rho_{B}^{A}\), the errors are less than 1.0 cm [24]. After the above data processing procedure, a clock-free observation can be formed by the sum of the dual measurements, which is:  
\[\overline{\rho}_{AB}(t_{0})=\frac{\overline{\rho}_{A}^{B}(t_{0})+\overline{ \rho}_{B}^{A}(t_{0})}{2}=\rho_{AB}(t_{0})+\frac{\left(l_{A}^{R}-l_{A}^{S}+l_{B }^{R}-l_{B}^{S}\right)}{2}+\frac{\varepsilon_{AB}+\varepsilon_{BA}}{2} \tag{8}\]  
From the above equation, we can see the final observation \(\overline{\rho}_{AB}(t_{0})\) does not have clock bias parameters, which are used in the POD process [37].
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 2 Materials and Methods
### Methods of the Prediction Model
#### 2.5.3 PCA-SVR Coupling Algorithm  
When SVR is used for regression prediction, the selection of penalty parameter \(c\) and kernel function parameter \(g\) critically affect the prediction accuracy of the model. First, the dataset was imported into PCA for analysis to obtain the principal component dataset in descending order of information, and the top K principal components were selected according to the cumulative information ratio. Next, the SVR regression model with radial basis kernel function was imported, and various combinations of \(c\) and \(g\) parameters were sequentially configured and generated for the SVR model by the grid search algorithm [33] while using cross-validation [34] to obtain each value. At the end of the experiment, the coefficient of determination (\(R_{\text{c}}\)\({}^{2}\)) and root mean square error of calibration (RMSEC) of the calibration set and the coefficient of determination (\(R_{\text{p}}\)\({}^{2}\)) and root mean square error of prediction (RMSEP) of the prediction set were used as the evaluation models. The closer the \(R_{\text{c}}\)\({}^{2}\) and \(R_{\text{p}}\)\({}^{2}\) are to 1, the smaller are the RMSEC and RMSEP, and the higher is the model accuracy. Figure 3 shows the execution process of the coupled PCA-SVR algorithm used in this experiment.  
Figure 3: Operational process of the PCA-SVR coupling algorithm.
## 5 Experiments and Results
### Quality of the Rebalancing Operations
#### 5.5.1 Best Performing Configuration  
Among all the possible configurations, the chosen reference configuration is the following:  
* \(minSupport=10\%\);
* \(minConfidence=50\%\);
* \(N_{t}=5\);
* _data partitioning strategy_ = Per day of the week partitioning.  
The default configuration achieves the best result in terms of fixed critical stations/critical status. Specifically, it was able to fix 225 critical station status out of 1377 by performing 151 rebalancing operations. Thus, on average, each rebalancing operation fixed 1.49 stations in a critical status.
## 5 Discussion
### Implications for Indoor-Outdoor Campus Navigation App Design
#### 5.4.2 Real-Time Location  
There is a high demand to provide real-time indoor navigation solutions as most location-based service users spend the majority of their time (70-90%) indoors, increasing public attention to these potential applications [78]. In the present study, most participants faced wayfinding issues related to live current location, especially during indoor navigation with MazeMap (see Figure 2). Based on interview results, some of the suggestions for improvement included: \"Indicate my current position\", \"Location dot moving in real time\", \"It would be helpful if the navigation feature in the map was real time showing the path you are following\", \"Updating real time location of the person\", \"It could be helpful to highlight both the start and target floor in the bar so that one knows where to navigate and from where\". [PERSON] and [PERSON] [79] suggested that floor indication design in mobile-based indoor navigation interfaces should mark the current floor and be arranged according to the sequence of floors from the bottom up. MazeMap provides such floor indication; however, according to qualitative analysis in the present study, some participants felt that the floor bar in the app was confusing and should be improved in design with respect to marking the current floor.  
A lot of research is focusing on indoor location determination technologies for making indoor positioning systems reliable and highly accurate [80]. An ideal navigation application should present real-time location and direction [81]. According to [PERSON] et al. [73], it is important that navigation apps are designed with knowledge of the user's currentlocation to help them identify their location in the environment, as they have expectations that maps should operate from their viewpoint.
## 3 Results and Discussion
### Electrochemical Characterization of IIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE Sensors  
#### 3.2.1 Differential Pulse Voltammetry (DPV) Characterization  
The electrochemical activity of the electrodes on the different sensors was examined by differential pulse voltammetry (DPV) under optimal conditions (Figure 3). There exists no obvious reduction peak of UO\({}_{2}\)\({}^{2+}\) on the exposed MCPE (Figure 3d). The smaller reduction peak current at the N-MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE (carbon paste electrode modified by non-ion imprinted membrane) (Figure 3c) indicates that the adsorption of uranyl ions on the electrode surface remains relatively low. However, the clear and strong peak observed at the MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE (Figure 3b) was due to the accumulation of UO\({}_{2}\)\({}^{2+}\) on the sensor surface. Meanwhile, the removal of UO\({}_{2}\)\({}^{2+}\) and recombination (Figure 3a) exhibited high currents compared with other electrodes, indicating that the ion-imprinted cavities have superior memory recognition and binding ability for template ions. Besides, it can also be found that the conductive copolymer layer in the IIP manifests significant electrocatalytic effect by accelerating the electron transfer and electroreduction phenomena. The peak current of UO\({}_{2}\)\({}^{2+}\) at MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE is evidently higher than those at bare MCPE and N-MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE, manifesting that there is a selective cavity and normal operation in the IIP formed during polymerization. Besides, the  
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Complexes** & **Binding Energies (\(\Delta\)E/kJ mol\({}^{-1}\))** \\ \hline
1 & 1H-PPCA- UO\({}_{2}\)\({}^{2+}\) & \(-\)85.07 \\
2 & 2-HP- UO\({}_{2}\)\({}^{2+}\) & \(-\)35.28 \\
3 & 3-HP- UO\({}_{2}\)\({}^{2+}\) & \(-\)31.19 \\
4 & H\({}_{2}\)SA- UO\({}_{2}\)\({}^{2+}\) & \(-\)42.58 \\
5 & DHP- UO\({}_{2}\)\({}^{2+}\) & \(-\)49.13 \\
6 & 3-PCA- UO\({}_{2}\)\({}^{2+}\) & \(-\)67.01 \\
7 & H\({}_{2}\)Pdc- UO\({}_{2}\)\({}^{2+}\) & \(-\)100.32 \\ \hline \end{tabular}
\end{table}
Table 1: Binding energy (\(\Delta\)E) of functional binding ligand complexes of different template molecules.  
Figure 2: Optimized conformation of UO\({}_{2}\)\({}^{2+}\) and functional binding ligand.  
template-free MIIP/Fe3O4@SiO2/MCPE did not show the signal response after removing the template ions (Figure 3e), demonstrating that UO2\({}^{2+}\) was effectively removed. Moreover, the reduction peak of UO2\({}^{2+}\) appears at \(-\)0.22 V on MIIP/Fe3O4@SiO2/MCPE. Compared with the N-MIIP/Fe3O4@SiO2/MCPE, the increase of peak current on the MIIP/Fe3O4@SiO2/MCPE show the electrocatalytic activity of IIP for the reduction mechanism of uranyl ion. According to the obtained results, MIIP/Fe3O4@SiO2/MCPE provides a promising electrochemical sensor for the determination of UO2\({}^{2+}\).
# Wireless Networks under a Backoff Attack: A Game Theoretical Perspective
## 2 CSMA/CA in IEEE 802.11  
The IEEE 802.11 standard [19] defines the MAC and physical (PHY) layer specifications for a wireless local area network (WLAN). Each device connected using this standard is known as station (STA). The access to the shared medium can be regulated using the Distributed Coordination Function (DCF), which uses CSMA/CA to access the medium.  
The basic mechanism used by the DCF in IEEE 802.11 standard is CSMA/CA to control the medium access and a positive acknowledgment frame (ACK): if no ACK is received, there is a retransmission. CSMA/CA operates using two procedures: a carrier sense (CS) which determines whether the channel is busy (i.e., other station is transmitting) or idle (i.e., no other station is transmitting); and a backoff procedure which determines when a station should start transmitting.  
A station willing to transmit invokes the CS mechanism to determine whether the channel is idle or not. If it is busy, the station defers the transmission until the channel is idle without interruption for a fixed period of time. After, the station starts a counter, called backoff, for an additional deferral time before transmitting: the station transmits when its backoff counter reaches 0. This procedure minimizes collisions among multiple stations that have been deferring to the same event. The backoff follows a uniform random variable in the interval \([0,CW-1]\), where CW stands for contention window. If a collision is detected when a station transmits, its CW is duplicated (binary exponential backoff) and the backoff procedure starts over. When the station has transmitted the packet, it waits for an ACK; if none is received in a certain time (ACK timeout), the station starts the transmission procedure again. This mechanism is known as Basic Access (BA), and is based on a two-way handshaking.  
The standard also defines an alternative procedure, based on a four-way handshaking, called request-to-send/clear-to-send (RTS/CTS). In this case, the transmitter station sends a RTS frame to the receiver, using the BA mechanism described above. The RTS frame is used to reserve the medium: when the receiver station receives a RTS, proceeds to reserve the channel for some time, sending a CTS frame to indicate that the channel reservation was successful. When the transmitting receives the CTS frame, starts transmitting its packet; when it finishes, if the transmission was successful, the receiving STA sends a positive ACK. While the channel is reserved, the rest of stations remain silent. The RTS/CTS procedure helps easing the hidden node problem [20,21] and provides a higher throughput than the BA mechanism when the MAC payload is large [22].
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scholars** & **Research Object** & **Research Achievement and Limitations** \\ \hline \hline \multirow{8}{*}{[PERSON] et al. [15]} & & It overcomes the problem of local strain measurement and corrects the assumption of uniform deformation. The non-contact measurement method has the advantage of avoiding soil sample disturbance. However, the experimental objects are all unsaturated soils. \\ \cline{1-1}  & & Conducting triaxial tests on different materials (such as stainless steel cylinders, saturated sand, and aluminum cylinders), measuring the deformation of soil samples under different test conditions (different confining pressures, different loading paths, etc.), and comparing and analyzing the measurement results with traditional methods or theoretical values can evaluate the measurement accuracy and reliability of this method. \\ \cline{1-1}  & & The local measurement problem of traditional triaxial measurement was solved, and the application scope of triaxial photogrammetry was further expanded. However, the soil samples were all reshaped, and the structural properties were not considered. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Other researchers’ conclusions in previous studies. not only improves the effectiveness and reliability of test data but also provides more detailed deformation analysis and stress-strain information, which helps to deeply understand the mechanical properties of soil and its behavior in engineering. In contrast, photogrammetry technology can provide more comprehensive, accurate, and visual soil sample deformation information by capturing the deformation image of the soil sample surface in real time and using computer vision and image processing technology.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
###### Abstract  
This paper focuses on the noise transmission process, presenting a comprehensive noise transfer model for velocity broad-band seismometers, which elucidate the transmission mechanisms of five distinct noise sources. We analyzed the spectral characteristics of the noise transfer functions across the forward path, feedback path, and data acquisition stages, with a focus on gains, corner frequencies, and the 0 dB point. Numerical simulations and experiments using the CS60 seismometer showed excellent agreement with theoretical predictions, validating the proposed model and associated noise optimization strategies. This study identified effective methods to reduce noise transfer gains, including optimizing the input and feedback mechanical constants and refining gains at various stages.  
velocity broad-band seismometer; self-noise; noise model; noise transmission path