esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] et al. [22] investigated the effects of fermented mixed feed (FMF) on growth performance, carcass traits, meat quality, muscle amino acid, and fatty acid composition in finishing pigs. The dietary treatments included a basal diet, a basal diet enriched with 5% FMF, and a basal diet with 10% FMF. The authors reported that a diet supplemented with 10% FMF significantly increased the concentration of \(n\)-3 polyunsaturated fatty acids, \(n\)-6 PUFA, and total PUFA, and the PUFA to SFA ratio. They concluded that 10% FMF dietary supplementation improved the pigs' growth performance and meat quality, as well as altering the profiles of muscle fatty acids and amino acids in finishing pigs. The type of diet can also influence the growth intensity, feed consumption, and carcass characteristics. [PERSON] et al. [23] investigated the fattening and carcass qualities of Irish-origin pigs fed liquid and dry diets. They studied the quality of the carcass, including the slaughter characteristics; the weight and proportion of valuable parts of the carcass; and the total weight and proportion of meat, fat, and bone. They reported that the liquid fattening of pigs resulted in an increase in average weight at weaning, an increase in growth intensity and relative growth, and a reduction in age to 100 kg weight. Pigs fed liquid diets also had higher meat mass and higher fat percentage. The authors concluded that the study of fattening and slaughter indicators of pigs revealed a reliable advantage of a liquid feeding system compared to feeding system involved the use of dry feed. [PERSON] et al. [24] investigated the effect of wet feeding of finishing pigs on production performance, carcass composition and meat quality. They pointed out that wet-fed pigs had higher hot and cold carcass weights than dry-fed pigs. The authors found no differences in the protein and carbon content, pH, or water holding capacity of the meat between the two forms of feeding. They reported that the hardness, gumminess, chewiness, and toughness of the meat were higher in dry-fed pigs than wet-fed pigs. The shear strength, adhesiveness, elasticity, and cohesiveness were independent of the diet. They concluded that the wet-fed pigs had better productive performance, carcass composition and meat characteristics than the dry-fed pigs. To the best of our knowledge, no studies have been conducted on the influence of dry and liquid feed forms on the precise pork fat quality. Taking the above into consideration, the objective of this study was to analyze the impact of feeding type on the quality and nutritional characteristics of pork fat, including peroxide values, fatty acid profiles, and their distribution between the _sn_-2 and _sn_-1,3 positions in the triacylglycerglycerol molecules. Additionally, oxidative stability and nutritional indexes--such as the polyunsaturated fatty acid/saturated fatty acid ratio, atherogenicity index, thrombogenicity index, hypocholesterolemic/hypercholesterol index, and health-promoting index--were assessed.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 4 Slant Wet Delay Simulations
### Simulated Slant Topospheric Delay Evaluation  
Although we use the simulated measurements as inputs in the tomography system, and then use ERA5 to verify the accuracy of the tomographic results. In theory, the discrepancy between the simulated STD and GNSS-derived STD has no impacts on the tomographic results, but through this kind of comparisons, we can learn the accuracy of simulated SWD and STD based on ERA5, and provide a meaningful reference for other studies.  
The procedure for simulated STD evaluation is shown in Figure 3. GNSS observations at stations in Guangdong Province in southern China as shown in Figure 1 are processed by PANDA software [35]. The ERPs (earth rotation parameters) and the DCBs (differential code biases) provided by CODE were employed. The absolute antenna phase center, phase windup corrections and station displacement corrections suggested in the IERS 2003 conventions were applied. The elevation cut-off angle was 10 and an elevation-dependent weighting strategy was applied to measurements at low elevations. The Global mapping function was used to relate zenith tropospheric delays (ZTDs) to the measurements. From a selected CORS network, the satellite clocks are estimated firstly as white noise with the satellite orbits and the station coordinates fixed, and then the satellite orbit and the estimated clocks are applied to user station for kinematic PPP in post-mission mode. ZTDs are estimated as piece-wise constants with a constraint of 4 cm\({}^{2}\) per hour and ambiguities are estimated as float solutions.  
These stations will also be used for tomography experiments in the next section. The generated ZTDs are then mapped into the signal path direction and differences between the simulated and GNSS STD are calculated. Variations of STD difference with satellite elevation for two GPS satellites at one station, BLGT, on day 119, 2017 are taken as an example as shown in Figure 4, where we can find that the difference is obviously smaller at higher satellite elevation angle. The difference can reach 15-20 cm at 10\({}^{\circ}\) elevation while is only about 2.5-5 cm at 60\({}^{\circ}\) elevation.  
Figure 4: STD difference between simulation and measurement of G01 and G10 satellite at BLGT station.  
Figure 3: Flow chart of simulated STD evaluation.  
RMS of STD differences for individual GPS satellites at all stations as well as the average RMS at different satellite elevation angles for all observations on day 119, 2017 are presented in Figure 5. We can find that the average RMS for individual satellite is generally between 0.03 to 0.06 m, and significantly decreases with the satellite elevation angles. For satellite with elevation below 10 degrees, the average RMS can exceed 0.5 m, compared to only about 0.02 m for satellites with elevation larger than 80 degrees.
## 5 Simulation Results and Discussion
### Environmental Benefit Analysis  
The primary energy source for high-speed railway power grids is coal-fired power generation, which emits substantial carbon dioxide. Consequently, when drawing electricity from the grid, high-speed trains inherently incur substantial carbon footprints. In this study, the integration of PV systems and ESS not only optimizes economic costs but also significantly reduces carbon emission intensity. The carbon dioxide equivalent per kWh consumed by the high-speed railway grid reaches 0.5568 kg. The life-cycle carbon emissions of PV and ESS presented in the tables are calculated by amortizing total emissions over annual averages.  
As evidenced in Table 4, (a) both Case 2 and Case 3 exhibit lower carbon emissions than Case 1, with reductions of 691,912.5 kg and 693,452.5 kg, respectively. This confirms that incorporating renewable energy as a power source for high-speed railway systems effectively reduces overall carbon emissions. (b) Case 3, which integrates carbon trading and REC trading mechanisms, demonstrates a 1540 kg reduction in emissions compared to Case 2. Although the absolute emission reduction is modest, the synergistic implementation of these mechanisms further decreases the grid energy dependency ratio, thereby reducing grid-related carbon emissions from 24.70% in Case 2 to 24.72% in Case 3. Simultaneously, the life-cycle carbon emissions of ESS are reduced by 923.5 kg annually in Case 3 compared to Case 2, representing an 11.3% improvement. These results highlight the environmental benefits of optimizing ESS configurations through the integration of carbon-REC synergistic trading mechanisms.
## 8 Anonymity and Security Analysis
### Security against Passive Attacks  
SNs use disposable pseudonyms to identify each other instead of using real IDs. No real ID stored in the sensor and no pseudonym is used more than once. Data is encrypted all the way from the source to the BS using shared pair-wise keys. For _eavesdropping and content analysis_, ADV can intercept messages without being able to read them because data is encrypted all the way to the BS. The only information ADV can get from the captured messages is the pseudonyms: OHPID, BPID or FPID, which are all temporary IDs and have no use except to calculate a new set of pseudonyms. Fortunately, the ADV cannot get from the captured messages, important parameters \(\mathrm{a_{i\leftrightarrow j}b_{i}}\) or \(\mathrm{c_{i}}\) which are all required to calculated new pseudonyms. Source PIDs are all encrypted during transmission. For _hop-by-hop trace_, ADV can track a stream of messages from one node to another by overhearing the messages. The ADV will be challenged with many real and fake transmissions throughout the WSN. Furthermore, each node will retransmit the messages through different routes. For _size-correlation_, ADV will be able to understand relationship between incoming and outgoing messages by analyzing sizes of the messages. This attack does not work for our framework since all the messages have commensurate size. For _identity correlation_, ADV cannot relate overheard identities to their nodes. It is not possible since SNs use different pseudonyms every time a message is transmitted. For _rate monitoring_, ADV tries to collect some statistical information about transmission rates. For instance, WSN will have a higher transmission rate nearby the sink. This is handled by issuing fake messages to maintain a similar transmission rate. For _angle-of-arrival (AoA)_, ADV uses special hardware to determine the signal direction. The framework did not account for specific countermeasure; however, it becomes a more serious issue with mobile SNs. Furthermore, _AoA_ would not perform well in our framework because of the uniform message distribution by using real and fake messages. For _received-signal-strength (RSS)_, ADV uses special hardware to measure signal strength to calculate distance to the source. This is not an issue for our framework since every transmitter has fixed transmission power and SNs are immobile.
## 4 Results
### Void Ratio (\(e\))  
As presented in Section 2.5, the impact of the void ratio on the damping properties of the cohesive soil is not straightforward. Therefore, in Figure 11a,b, a different approach for the presentation of that influence is presented. In picture 11a, the minimal damping ratio versus the void ratio is shown. In this figure, some global dependency can be seen; namely, from the void ratio 0.3 to about 0.5, the \(D_{MIN}\) decreases. When the void ratio rises, the minimal damping ratio increases. That phenomenon is marked in Figure 11a by the black arrows. Moreover, the same observations can be found in [20,22]. However, it is well-known that the void ratio depends on the effective stress (\(p\)'), so it is hard to determine the impact of the void ratio on the minimal damping ratio for individual samples. Thus, the authors of this article propose dividing minimal damping ratio by the square root of effective stress (\(p\)'), in order to emphasize the influence of the void ratio on the damping ratio. This procedure is presented in Figure 11b. The relationship between these parameters is best described by a power function with the minimal coefficient of determination in sample _K7_ 0.86. Therefore, in the case of cohesive soil from Warsaw, the void ratio has a noticeable impact on the minimal damping ratio (\(D_{MIN}\)). However, the authors did not find any influence of \(e\) on the shape of the damping ratio curve of the studied soils.
## 2 Materials and Methods
### Improvement of Acquisition Rate  
The signal acquisition rate of a data acquisition system is mainly determined by the single cycle time of the system, i.e., the time to obtain a set of measured values. The main factors affecting the single cycle time of the system include the signal frequency released by the FPGA and the transmission rate of the communication module.  
(1) _Analog-to-digital conversion optimization_  
Phase-sensitive demodulation is the most crucial step in obtaining measured values in FPGA. This step requires that the sampling frequency of the A/D module and the D/A module should be consistent. The number of single-cycle semaphores output by analog-to-digital conversion is determined by the sampling frequency of the analog-to-digital conversion chip and the frequency of the analog signal.  
\[n=f_{s}/f \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Material** & **Resistivity (\(\Omega\)-m)** & **Yield Strength (MPa)** & **Electrochemical Characteristics** & **Price (RMB/ton)** \\ \hline Copper & \(1.72\times 10^{-8}\) & 343 & Strong stability and corrosion resistance & 62,980 \\ Titanium & \(4.2\times 10^{-7}\) & 300 & Good stability and corrosion resistance & 62,210 \\ Carbon & \(3.5\times 10^{-6}\) & 9.3 & Non-metallic, corrosion-resistant & 2000 \\ Tungsten & \(5.48\times 10^{-8}\) & - & Strong stability and corrosion resistance & 116,000 \\ Stainless steel & \(7.3\times 10^{-7}\) & 207 & Moderate stability, not resistant to corrosion & 15,625 \\ \hline \end{tabular}
\end{table}
Table 2: Characteristics of electrode materials at room temperature.  
Figure 5: Composition of shielding wire.  
where \(f\) is the frequency of the analog signal, \(f_{s}\) is the sampling frequency, and \(n\) is the number of single-cycle semaphores. The number of different single-cycle semaphores directly determines the final effect of digital-to-analog conversion. The more single-cycle semaphores, the higher the similarity between the output digital signal and the analog signal.  
The system has a high demand for signal frequency. The sampling frequency of the A/D and D/A modules must be increased to obtain a higher signal frequency and ensure the current number of single-cycle semaphores. The current sampling frequency of the system is 10 MHz, which is directly provided by FPGA. The highest sampling frequency of the D/A chip AD9764 is 125 MHz, and the highest sampling frequency of the A/D chip AD9240 is 10 MHz.  
The current sampling frequency is the highest sampling frequency of AD9240, so the AD9240 chip directly limits the signal frequency of the data acquisition system. Based on the above analysis, it is necessary to replace the A/D conversion chip to improve the signal frequency of the data acquisition system. The common parameters of low-voltage analog-to-digital conversion chips are shown in Table 3.  
Considering sampling frequency, cost, accuracy, and other conditions, a 14-bit high-speed analog-to-digital converter AD9244 was ultimately adopted. Its peripheral circuit design is shown in Figure 6.
## 3 Methodology
### Camera Placement Optimization Based on Reinforcement Learning
#### 3.3.2 Reward Function  
The reward function for the camera placement strategy is the total benefit value of all areas covered by the cameras. The cameras in this study have a fixed 90-degree field of view, with visible ranges set according to standard camera parameters. To quickly calculate the covered area and eliminate overlapping regions, each monitoring area is precomputed with visibility grid codes for each viewing angle. The visible area for each camera setup can then be quickly found by performing a union operation on each camera's visible area, removing overlaps, and calculating the total benefit based on the pixel values in each grid. As shown in Formula (5), the action value function Q in state s with action a can be defined as the reward value of the current state (r(s,a)) and the sum of possibility of state s\({}^{\prime}\) after action a\({}^{\prime}\). \(\gamma\) is the learning rate.  
\[Q^{*}(s,a)=r(s,a)+\gamma\sum_{s^{\prime}\in S}P\big{(}s^{\prime}|s,a\big{)} \underset{a^{\prime}}{\max}Q^{*}\big{(}s^{\prime},a^{\prime}\big{)} \tag{5}\]  
Formula (6) show the loss function of the Q net with the mean-square error.  
\[\omega^{*}=\underset{\omega}{arg\min}\frac{1}{2N}\sum_{i=1}^{N}\left[Q_{ \omega}(s_{i},a_{i})-\left(r_{i}+\gamma\underset{a^{\prime}}{\max}Q_{\omega} \big{(}s^{\prime}_{i},a^{\prime}\big{)}\right)\right]^{2} \tag{6}\]  
The specific calculation method is illustrated in Figure 5. For the reward value Qij that moves a camera from location Ci to Cj, Vi is the visible pixels of the spatial syntax map by removing Ci from camera set C and Ri is the corresponding sum of their importance value. Similarly, Vj is the visible pixel after moving the camera to location Cj, and Rj is the responding sum of importance value. Qij can be calculated as Rj - Ri.
## 2 Materials and Methods
### Error Budget Approach
#### 2.3.1 Random Errors  
In each focal plane image, the source relative positions can be assessed with precision [31] depending on the optical resolution, hence the photon distribution on the focal plane, and on the source brightness, through the signal to noise ratio (S/N). A simple expression for the one-dimensional location uncertainty \(\sigma\)[32] is  
\[\sigma=\alpha\frac{\lambda}{D}\cdot\frac{1}{\mathrm{S/N}}\,, \tag{2}\]  
where \(\lambda/D\) is a geometric factor related to diffraction and the ideal telescope resolving power through the observing effective wavelength \(\lambda\) and instrument diameter \(D\). The \(\alpha\) parameter is a factor taking into account all other numerical and geometric terms, in particular the pixel size; the above simplified formula is valid over a limited range of the relevant experimental conditions, e.g. away from detector saturation.  
The separation between sources is affected by the uncertainty on either end; hence, its precision is mostly limited by the faintest object in the pair.  
The location uncertainty is the photon-limited astrometric noise, which must be combined with other random and systematic contributions to achieve a realistic error budget. Some considerations on this issue are addressed below. For practical reasons, namely detector saturation on bright sources, the overall six-hour observation (solid black line) is split into shorter elementary exposures with a nominal duration of one minute (dotted black line). The brightest stars (\(G\leq 12\,\mathrm{mag}\)) will require further reduction in the elementary exposure time.
# A Voxel Generator Based on Autoencoder
## 4 Performance Evaluation
### Comparison with Related Approaches
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Encoder** & **Decoder** & \begin{tabular}{c} **Flatten** \\ **Type** \\ \end{tabular} & **Car** & **Chair** & **Lamp** & **Table** \\ \hline \multirow{4}{*}{MVGG16} & MSConv & \begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.861 & 0.630 & 0.571 & 0.697 \\  & MMConv & \begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.858 & 0.651 & 0.587 & 0.711 \\ \cline{2-6}  & \multirow{2}{*}{MMConv} & \begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.852 & 0.587 & 0.539 & 0.671 \\  & & FF & 0.857 & 0.653 & 0.586 & 0.712 \\ \hline \multirow{4}{*}{MResNet18} & MSConv & \begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.858 & 0.650 & 0.564 & 0.711 \\  & MMConv & \
\begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.859 & 0.649 & 0.591 & 0.711 \\ \cline{2-6}  & \multirow{2}{*}{MMConv} &
\begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.849 & 0.656 & 0.567 & 0.715 \\  & FF & 0.859 & 0.669 & 0.597 & 0.716 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: IOU values of different encoder–decoder combinations with FF or GAP. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Approach** & **Encoder** & **Decoder** &
\begin{tabular}{c} **Flatten** \\ **Type** \\ \end{tabular} & **Car** & **Chair** & **Lamp** & **Table** \\ \hline
3D-R2N2 [15] & & & 0.661 & 0.439 & 0.281 & 0.42 \\ \hline V3 DOR-AE [16] & & & 0.713 & 0.511 & 0.465 & 0.508 \\ \hline Pix2 Vox [17] & & & 0.854 & 0.567 & 0.443 & 0.601 \\ \hline TMVNet [24] & & & 0.870 & 0.721 & 0.534 & 0.660 \\ \hline \multirow{4}{*}{VoxGen} & MSConv & \begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.861 & 0.630 & 0.571 & 0.697 \\  & MMConv & \begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.858 & 0.651 & 0.587 & 0.711 \\  & & FF & 0.857 & 0.653 & 0.586 & 0.712 \\ \cline{2-6}  & \multirow{2}{*}{MResNet18} & MSConv & \
\begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.858 & 0.650 & 0.564 & 0.711 \\  & & FF & 0.859 & 0.649 & 0.591 & 0.711 \\ \cline{2-6}  & \multirow{2}{*}{MMConv} &
\begin{tabular}{c} GAP \\ FF \\ \end{tabular} & 0.849 & 0.656 & 0.567 & 0.715 \\  & & FF & 0.859 & 0.669 & 0.597 & 0.716 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between VoxGen and related approaches. combinations of encoder, decoder, and flattened layer. Although the feature-extraction network of Pix2 Vox is the same as that of VGG16, it has no neural network between the encoder and the decoder. Consequently, it does not have non-linear learning ability, in contrast to VoxGen. Even though Pix2 Vox merges multiple generated voxels in order to refine the final output, this does not guarantee that the quality of the output voxel will be improved. As previously discussed, VoxGen is more effective than the three other approaches in terms of the quality of generated voxels, due to its superior architecture.
## 5 Discussion
### Limitations  
Several limitations of this study should be acknowledged:  
The participants with RCRSP in our sample presented with mild to moderate disability and pain levels. Further research is needed to evaluate the applicability of these tests in individuals with more severe presentations.  
The relatively young age of our study population (40 years) represents a generalization limitation. The performance characteristics and feasibility of these tests might differ in older populations due to age-related changes in muscle strength, power generation, and overall shoulder function. Future validation studies are needed in older populations to establish age-specific normative values and assess test feasibility in this demographic.  
This study has potential heterogeneity within the RCRSP group. While all participants met the clinical diagnostic criteria for RCRSP, this diagnosis is a wide umbrella term. We did not further stratify participants based on specific imaging findings (e.g., tendinopathy, partial-thickness tears, or bursitis) through imaging or arthroscopic diagnosis. Future studies should consider subgroup analysis based on potential pathoanatomical observations to determine if these tests perform differently across various RCRSP presentations. However, this limitation reflects the clinical reality where RCRSP often presents as a complex, multifaceted condition rather than a homogeneous pathology.  
The order of physical tests was uniform to avoid fatigue effects from throwing. While this approach ensured consistency, it may have introduced order effects. Future studies with randomized test sequences and larger sample sizes could address this limitation.  
We used isometric grip strength as a comparator measure, which has been previously validated against isokinetic measures [17]. However, direct comparison with isokinetic measurements of shoulder strength in this population would further support construct validity.  
While we assessed the feasibility of the tests in terms of pain provocation, we did not evaluate their responsiveness to change over time or with intervention. Longitudinal studies are needed to establish the tests' sensitivity to clinical change.  
The sample size, while sufficient for our primary analyses, may have limited our ability to detect smaller effect sizes, particularly for the SMBT. Larger studies may provide more definitive conclusions about the discriminative validity of this test. Another limitation is the absence of receiver operating characteristic (ROC) analysis to determine optimal cut-off values for discriminating between individuals with and without RCRSP. Such analysis would enhance the clinical utility of these tests by providing clinicians with specific thresholds for identifying functional deficits and monitoring progress. Future research should focus on establishing these cut-off values across different age groups and activity levels, which would facilitate more precise clinical decision-making and outcome assessment.