esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
## 2 System Model and Optimization Problem
### System Model
Because the transmission performance of DF is better than AF in most instances, the relay uses DF in the second time slot, which is to say that \(R_{i}\) uses the harvested energy to forward the decoded information to \(D\). Assuming that \(R_{i}\) sends the signal \(x_{i}(k)\) to \(D\), the received RF signal at \(D\) is  
\[y_{D}^{i}(k)=\sqrt{P_{i}g_{i}}\cdot x_{i}(k)+n_{D}(k) \tag{4}\]  
where \(\mathrm{E}\Big{[}\big{|}x_{i}(k)\big{|}^{2}\Big{]}=1\), \(P_{i}\) is the transmit power of \(R_{i}\) and \(n_{D}(k)\sim CN\big{(}0,\sigma_{D}^{2}\big{)}\) is the additive white Gaussian noise caused by the antenna reception and radio frequency to baseband conversion at the destination node. Therefore, the signal-to-noise ratio (SNR) of the receiver of \(D\) can be expressed as \(\gamma_{d}^{i}=\frac{P_{i}g_{i}}{\sigma_{D}^{2}}\). According to the maximum merge ratio criterion [17], the total SNR of all the links participating in the communication is \(\gamma=\sum\limits_{i=1}^{N}\gamma_{i}=\sum\limits_{i=1}^{N}\min\big{(}\gamma _{r}^{i},\gamma_{d}^{i}\big{)}\). Therefore, by using the Shannon formula, the total information transmission rate of \(S-R-D\) is  
\[R_{SD}=\frac{1}{2}\log_{2}(1+\gamma)=\frac{1}{2}\log_{2}\Bigg{(}1+\sum\limits_ {i=1}^{N}\gamma_{i}\Bigg{)} \tag{5}\]  
In addition, the total power consumption during the transmission of \(S-R-D\) is  
\[P=P_{S}+2P_{C}+\sum\limits_{i=1}^{N}\Big{(}P_{i}+P_{Q}-P_{H}^{i}(\rho_{i}) \Big{)} \tag{6}\]  
where \(P_{C}\) is the consumption of power of the \(S\) and \(D\) decoding circuits and \(P_{Q}\) is the consumption of power of the \(R_{i}\) circuit.
# Research on Large Divergence Angle Laser Ranging System +
###### Abstract  
This study investigates a laser ranging technology scheme featuring a large divergence angle for both the emitted and received laser beams, focusing on applications where both the measured target and the ranging carrier are high-mobility platforms. A dual-concave beam-reducing lens design is adopted to reshape the original beam divergence angle of 10 mrad from the erbium glass laser into a ranging output beam divergence angle of 26 mrad, while maintaining the Gaussian energy distribution of the original laser beam. A q500 um photosensitive surface APD detector is used, and a combination of aspherical and spherical elements is employed in the receiving optical system to achieve a 30 mrad large field-of-view echo reception within the small photosensitive surface. This laser ranging system addresses the challenge of aiming and tracking for laser ranging between relatively high-speed moving objects and reduces the stability precision requirements for the ranging carrier platform.  
laser ranging; large divergence angle; large field of view; spaceborne; platform stabilities +
Footnote †: This article is a new and expanded version of a paper entitled [Research on Large Divergence Angle Laser Ranging System], which was presented at 10 th Symposium on Novel Optoelectronic Detection Technology and Application, Taiyuan China, 1-3 November 2024.
## 3 Results and Discussion
### Electrical Conductivity
The authors [45] proposed that the increased mobility of polymer chains at higher temperatures would interrupt the random formation of conductive network, thereby increasing the resistivity of subsequent samples. Consequently, the annealing treatment is detrimental to the \(\sigma\) of only CB-containing samples. Unlike PS/CB 10 wt % microparts, the TD \(\sigma\) for the thick section of only CNT or hybrid carbon fillers loaded PS counterparts increased after the annealing treatment. For example, the TD \(\sigma\) for the thick section of 10 wt %-PS/CNT50/CB50 microparts increased from 1.41 \(\times\) 10\({}^{-4}\) to 4.86 \(\times\) 10\({}^{-4}\) S/cm. The annealing-induced enhancement of \(\sigma\) can be explained as follows: as described previously, there would be a preferential alignment of carbon fillers along the flow direction arising from the predominant shearing effect and rapid solidification of as-molded products in \(\upmu\)IM [8]. As a result, the residual stress and strain will exist in the interfacial polymer phases between the added fillers [46]. However, this is not a thermodynamically-favored state and, the frozen-in polymer chains and orientated structure would have a high tendency to reorder or rearrange themselves to a random orientation, provided that the polymer chains gain a certain degree of mobility. In addition, the presence of surrounding insulating polymers would increase the contact resistance and limit the possibility to construct conductive pathways through the direct contact of carbon fillers, thereby impairing the enhancement of \(\sigma\). [PERSON] et al. [47] proposed that the annealing treatment could relieve the residual stress and strain that exists in the interfacial polymers between the added fillers thanks to the increased mobility of polymer chains. As a result, the mean distance between adjacent CNTs decreased slightly with an increase of annealing temperature [47], which in turn leads to a reduction of the 'tunneling' resistance, thereby contributing to the enhancement of \(\sigma\). Moreover, the increased mobility of polymer chains promotes secondary agglomeration of CNT (e.g., loosely packed CNT network) [48; 49], which is favorable for the enhancement of \(\sigma\) as well. Interestingly, a significant increase of the TD \(\sigma\) was observed for the thick section of 10 wt %-PS/CNT50/CB50 microparts, in which case the CB and CNT have equivalent weight fraction in PS, i.e., 5 wt % respectively. As mentioned previously, conductive pathways can be formed in either only CB- or CNT-containing PS microparts at 5 wt %. Thus, it is reasonable to suggest that the synergistic effect of hybrid carbon fillers on the increment of \(\sigma\) arises from the formation of CB aggregates and the restoration of loosely packed CNT network within the host matrix after the annealing treatment.
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 2 Independent Pistonphone Simulation
### Results  
#### 2.2.1 Results in Frequency Domain  
The frequency is set from 0.1 mHz to 50 Hz and 1/3 octave for numerical simulation. A probe is inserted at the position where the microphone is installed to get the calibrating sound pressure. The phase difference between the calibrating sound pressure and the piston displacement is the desired phase response of the pistonphone, as given in Figure 5, whose angle is calculated from dividing their time difference by the calibration period and multiplying by \(360^{\circ}\). Note that the constant \(90^{\circ}\) phase compensation has already been done.  
As shown in Figure 5, in the ideal sealing and adiabatic conditions the generated sound pressure is exactly synchronized with the piston displacement. The heat conduction effect will cause the phase of the sound pressure to be ahead of the piston displacement, and it works only at middle frequencies. Meanwhile, the effect of the pressure leakage acting on the sound pressure is more obvious and will cause the sound pressure to be \(90^{\circ}\) ahead of the piston displacement.  
The results are clear, but the phase lead phenomenon is quite incomprehensible. Therefore, the following transient simulation is conducted to explain this.  
Figure 5: Phase difference between the calibrating sound pressure and the piston displacement.
## 4 Experimental Results and Discussion
### Evaluation Metrics  
The output variable of the model is the PM\({}_{2.5}\) concentration in the future. Therefore, this paper selects seven representative evaluation metrics to assess the model's performance, including root mean square error (RMSE), Mean Squared Error (MSE), mean absolute error (MAE), symmetric mean absolute percentage error (SMAPE), Normalized Absolute Error (NAE), coefficient of determination (R\({}^{2}\)), and the Index of Agreement (IA). Seven representative evaluation metrics were selected to assess the model's performance, which include root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE), symmetric mean absolute percentage error (SMAPE), standardized absolute error (NAE), coefficient of determination (R\({}^{2}\)), and integrated assessment indicator (IA). The seven metrics are formulated as the following Equations (9)-(15).  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}} \tag{9}\]  
\[MSE=\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{10}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}{|\hat{y}_{i}-y_{i}|} \tag{11}\]  
\[SMAPE=\frac{100\%}{n}{\sum_{i=1}^{n}}\frac{{|\hat{y}_{i}-y_{i}|}}{(|\hat{y}_ {i}|+|y_{i}|)/2} \tag{12}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}\frac{{|\hat{y}_{i}-y_{i}|}}{max(|\hat{y}_{i}|,|y_{i}|+\varepsilon)} \tag{13}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}{|\hat{y}_ {i}-y_{i}|}} \tag{14}\]  
\[IA=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}({|\overline{ y}_{i}-y_{i}|+|\hat{y}_{i}-y_{i}|}^{2}} \tag{15}\]  
Figure 6: Pearson correlation between features related to PM\({}_{2.5}\) in monitoring station 1001.  
where \(y_{i}\) is the true value, \(\hat{y}_{i}\) is the predicted value, \(\overline{y}_{i}\) is the mean of the true values, and \(\varepsilon=1\times 10^{-8}\).  
Among the seven evaluation metrics, smaller values for RMSE, MSE, MAE, SMAPE, and NAE indicate better estimated the performance of the model, while smaller values of \(R^{2}\) and IA indicate worse estimated performance of the model.  
In addition, the training time required for different models on the same data are used to assess model complexity and highlight the advantages of the proposed model.
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput
### Long Short-Term Memory Networks
#### 4.2.2 Stacked Bidirectional LSTM  
A time series provided to an LSTM ANN is arranged chronologically: the information is passed in a positive direction from the time step \(t-1\) to the time step \(t\) along the chain  
Figure 4: Stacked architecture—a hidden layer is multiplied and stacked on top of each other creating a grid of cells.  
like structure. However, it is also possible to process sequence data in both forward and backward directions. This idea is utilized in Bidirectional LSTM (BdLSTM) ANNs, which connect two hidden layers: a forward LSTM layer and a backward LSTM layer, to the same output layer, as illustrated in Figure 5. Thus, a BdLSTM ANN considers additionally backward dependencies, which pass information in a negative direction from the time step \(t\) to \(t-1\). This allows a BdLSTM ANN to capture more patterns, which a unidirectional LSTM could filter. The BdLSTM ANN computes its forward layer output \(\overrightarrow{h}\) iteratively from the positive sequence's inputs from time \(t-n\) to \(t-1\). Using the reversed inputs from time \(t-1\) to \(t-n\), the BdLSTM calculates the backward layer output \(\overleftarrow{h}\).  
The standard LSTM updating method based on the equations presented in Section 4.2 computes both forward and backward layer outputs. A BdLSTM layer generates an output \(h_{t}\), calculated as  
\[h_{t}=\rho(\overrightarrow{h},\overleftarrow{h})\,,\]  
where the \(\rho\) function is used to combine the two output sequences. Similar to the LSTM layer, the final output of a BdLSTM layer can be represented by \(\hat{y_{T}}\), which is an expected unprocessed forecast.
###### Abstract  
This study proposed a novel and low-cost adsorbent prepared from dredging sediment (DSD) for effective removal of dye in aqueous solutions. The adsorption efficiency and behavior of the DSD adsorbent toward the crystal violet (CV), a cationic dye, were investigated via batch experiments. The results showed that DSD samples contain mainly clay minerals (illite and kaolinite) and other mineral phases. In addition, DSD is a mesoporous material (V\({}_{\text{mesopore}}\) = 94.4%), and it exhibits a relatively high surface area (\(\sim\)39.1 m\({}^{2}\)/g). Adsorption experiments showed that the solution's pH slightly affects the adsorption process, and a pH of 11 gave a maximum capacity of 27.2 mg/g. The kinetic data of CV dye adsorption is well described by the pseudo-second-order and the Avrami models. The Langmuir and Liu isotherm models provide the best fit for the adsorption equilibrium data. The monolayer adsorption capacity of Langmuir reached 183.6, 198.0, and 243.6 mg/g at 293, 308, and 323 K, respectively. It was also found that the adsorption process was spontaneous (\(-\Delta\)G\({}^{\circ}\)), exothermic (\(-\Delta\)H\({}^{\circ}\)), and increased the randomness (\(+\Delta\)S\({}^{\circ}\)) during the adsorption operation. The primary mechanisms in CV dye adsorption were ion exchange and pore filling, whereas electrostatic attraction was a minor contribution. In addition, three steps involving intraparticle diffusion occur at the same time to control the adsorption process. The results of this study highlight the excellent efficiency of DSD material as an coefriendly sorbent for toxic dyes from water media.  
dredged sediment; characterization; adsorption; crystal violet; mechanisms +
Footnote †: journal: Medical Imaging
## Materials and Methods
### Data Analysis  
The data was analyzed by MassHunter software (Version B.07.02.1938, Agilent Technologies, Beijing, China). Chromatogram plots were created in R with the \"mzR\" package [42; 43]. Compounds were identified by NIST MS library match (\(\geq\)80) supported by linear retention indices, as calculated according to [PERSON] and [PERSON] from injection of a 1 \(\mu\)g/mL C\({}_{7}\)-C\({}_{30}\) alkane standard (Supelco) in hexane, and by chemical standards (see Chemicals) [29; 44]. The literature retention indices used for comparison were obtained from the NIST Standard Reference Database according to the most similar system and instrument conditions [28]. Semi-quantitation of the analytes in the milk was performed and the results have been presented as \"apparent concentrations\" as detailed by [PERSON] and [PERSON] (1990) [45]. The peak areas of the analytes were normalized to the peak areas of the internal standards and multiplied by the concentration of the internal standard in the reconstituted milk according to Equation (1):  
\[\textit{Apparent Conc.}(\textit{analyte, milk})\frac{\mu\text{g}}{\text{kg}}= \frac{\textit{Peak Area }(\textit{analyte})}{\textit{Peak Area }(\textit{IS})}\times\textit{Conc }(\textit{IS, milk})\frac{\mu\text{g}}{\text{kg}} \tag{1}\]  
As the study sought only to compare the methods to each other, the actual response factors for each compound were not determined relative to the internal standards as they would not impact the method to method comparisons. Instead a response factor of 1 was assumed for all analytes. Blank injections were performed for each method. Carryover was not observed for blank injections. However, as noted by [PERSON] et al., the sorptive stir bar methods did appear to pick up small amounts of some compounds from the environment [24]. When a compound did appear in the blank, the peak area in the blank was subtracted from the peak area of the sample prior to normalization.  
Conceptualization, R.H., G.E., B.K. and P.B.; method development, R.H. and G.E.; validation, R.H., G.E. and B.K.; data collection and analysis, R.H.; writing--original draft preparation, R.H.; writing--review and editing, G.E., B.K., and P.B.  
This research received no external funding.  
The authors would like to thank Blue River Dairy (Invercargill, NZ) for their support and in-kind donation of spray-dried sheep milk for this project. Additionally, the authors would like to thank [PERSON] and [PERSON] for their technical support. Finally, the authors thank the University of Otago for support via the University of Otago Doctoral Scholarship.  
The authors declare no conflict of interest.
## 4 Discussion
### Performances of MAIAC VIs and NDVI\({}_{\text{prod}}\)
Therefore, some time lag between the estimates is not surprising considering that the same methods have been applied to two types of data based on fundamentally different observation techniques. The 20% thresholdwas selected according to [PERSON] et al. [60] and [PERSON] et al. [61], who found this to produce similar predictions to those of temperature data, however, relative thresholds used in the reviewed literature vary between 10% [62] and 50% [9]. Derivative methods to identify the points of maximum increase on the seasonal curve have also been applied in previous studies (e.g., [PERSON] et al. [54]), which is particularly beneficial when applied to regional and global scale studies and ecosystems characterized by multiple growth cycles [55]. EGS could not be successfully estimated using any of the MODIS time series. Previous studies have similarly found that EGS could not be determined with as high accuracy as SGS [11; 62]. The factors controlling the transition of vegetation into winter dormancy are not as well understood as for the onset of greenness during spring [64]. However, the decline in the VI signal taking place during senescence tend to occur at a slower rate and is less pronounced compared to the spring transition [62], which may explain why it was difficult to establish a specific EGS date from both the ground data and the remote sensing data. Furthermore, an important factor that causes GPP to decline in the autumn at high latitudes is the significant decrease in the amount of available PAR. The importance of LUE in vegetation productivity is thus decreased during this season as there is insufficient PAR to drive photosynthesis.
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### UC2: Urban Environment
#### 5.2.1 Urban Coverage  
Figure 11 shows that the use of lamp posts in an urban environments affects the coverage. Only scenarios that use lamp posts (LP and FL) can cover more than 97% of users, while the facade only (FA) can cover up to 50% of users. This is because nodes on walls need a greater beam angle, resulting in lower antenna gains due to the street canyon structure of urban cities.  
The lamp post cases have additional beam angle flexibility, providing the tool more freedom to establish a transmitter with the highest gain. Since the coverage is only lowered by 0.5%, vegetation has little effect in urban settings. This is mainly because there are only a few bushes and tiny trees in the downtown districts, which barely affect the performance of the network as a whole. Instead, torrential rainfall causes the coverage to decrease by 1% while boosting EN consumption by 5%. With a 3% difference between the worst case  
Figure 9: Distance distribution. (**a**) Between CPE and ENs. (**b**) Between ENs.  
(382 \(\,\mathrm{EN/km^{2}}\)) and the best case (370 \(\,\mathrm{EN/km^{2}}\)), the environmental factor generally has a negligibly small impact.  
Figure 11: User coverage and network utilisation in UC 2.  
Figure 10: Use Case 2 solution example. Snapshot of the urban Liege area.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 5 Experiments
### Experimental Results

Table 2: Comparison results of several algorithms for seven metrics such as TP. Figure 3: The detailed information of the evaluation metrics, HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, and LocA. The alpha is a threshold to compute the scores. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**MOT-Approach** & **MOTA\(\uparrow\)** & **MOTP\(\uparrow\)** & **MODA\(\uparrow\)** & **IDSW\(\downarrow\)** & **sMOTA\(\uparrow\)** & **\#Dets** & **\#Tracks** \\ \hline MVT & 84.08\% & 80.71\% & 84.34\% & 189 & 67.45\% & 30,306 & 968 \\ JRMOT [20] & 85.10\% & 85.28\% & 85.89\% & 271 & 72.11\% & 31,112 & 960 \\ MOTSFusion [21] & 84.24\% & 85.03\% & 85.45\% & 415 & 71.14\% & 30,813 & 929 \\ SRK\_ODSEA [22] & 87.79\% & 85.41\% & 88.90\% & 380 & 74.62\% & 31,551 & 1039 \\ Quasi-Dense [23] & 84.93\% & 84.85\% & 85.84\% & 313 & 71.69\% & 30,621 & 979 \\ FANTrack [24] & 75.84\% & 82.46\% & 78.00\% & 743 & 61.49\% & 29,435 & 1582 \\ extraCK [25] & 79.29\% & 82.06\% & 80.80\% & 520 & 64.44\% & 29,138 & 871 \\ Point3 DT [26] & 67.56\% & 76.83\% & 68.42\% & 294 & 48.73\% & 32,379 & 1086 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of several algorithms for seven metrics such as MOTA.
## 2 Methodology
### Point Cloud Preprocessing  
#### 2.2.1 Point Cloud Filtering  
The large number of point sets in the point cloud data leads to slow computation while processing the point cloud. Therefore, filtering with downsampling is used first to remove the redundant points during the computation process. Three-dimensional voxel grid filtering is used to significantly reduce the number of data points by dividing the point cloud into voxels and replacing multiple points with one representative point in each voxel. Also, 3D voxel grid filtering can effectively remove isolated noise points. Through voxelization, the point cloud is resampled to a consistent spatial resolution, which facilitates subsequent surface segmentation or surface reconstruction. The filtering effect is shown in Figure 8.  
To verify the effectiveness of the filtering process, a comparison of the number of points and point cloud loading time was conducted. As shown in Table 1, while preserving the essential geometric features, the point cloud becomes sparser after filtering, resulting  
Figure 8: Comparison of point clouds before and after filtering: (**a**) original point cloud of the desk; (**b**) filtered point cloud of the desk; (**c**) original point cloud of the workpiece; (**d**) filtered point cloud of the workpiece.  
Figure 7: Architecture of Dysample.  
in a significant reduction in the total number of points and a noticeable improvement in loading speed, which is beneficial for subsequent processing.  
To further evaluate the acceleration effect of voxel filtering in point cloud processing, we measured the total processing time for ground segmentation and ICP registration under two conditions: without filtering and with 3D voxel grid filtering. As shown in Table 2, although the filtering step introduces a slight computational overhead, it significantly reduces the time required for subsequent processing (segmentation + registration). This demonstrates that voxel filtering effectively improves the overall efficiency of the algorithm.