esa-sceva/satcom-chunk-collection huggingface dataset:
# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 4 Experiments
### Ablation Study on Loss Function  
To thoroughly analyze the effectiveness of each component and loss function in the proposed method, we meticulously designed ablation experiments, complemented by a comprehensive analysis through quantitative experiments.  
Building upon the quantitative experiments, our ablation study primarily focused on two key metrics: the mean Intersection over Union (mIoU) and the Absolute Trajectory Error (ATE). As revealed by the data in the Table 4, we incrementally incorporated different loss functions into the model for comparison. When only the color loss (\(l_{rgb}\)) and depth loss (\(l_{depth}\)) were utilized, the model achieved an ATE of 20.59 and an mIoU of 60.51 on the Replica dataset, and an ATE of 22.54 with an mIoU of 63.24 on the ScanNet dataset. Upon introducing the semantic loss (\(l_{sem}\)) to form the combination of \(l_{rgb}+l_{sem}+l_{depth}\), a significant performance boost was observed. Specifically, on the Replica dataset, the ATE decreased to 17.52, and the mIoU increased to 67.48; on the ScanNet dataset, the ATE reached 14.35, and the mIoU climbed to 70.45. Notably, when the full set of loss functions \(l_{rgb}+l_{sem}+l_{feat}+l_{depth}\) (i.e., our proposed method) was employed, the performance was further substantially optimized. On the Replica dataset, the ATE plummed to 9.47, and the mIoU soared to 78.25; on the ScanNet dataset, the ATE dropped to 8.53, and the mIoU reached an impressive 80.56.  
In summary, the results of the ablation experiments strongly confirm the critical significance of the synergistic effect of the multi-scale information extractor, the full-attention  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Replica**} & \multicolumn{3}{c}{**ScanNet**} & \multirow{2}{*}{**Confidence**} \\ \cline{2-2} \cline{4-7}  & **Room 0** & **Room 1** & & & & **Avg** & **0000** & **0106** & **Avg** \\ \hline NIDS-SLAM & 9.5 & 12 & 10.8 & 12.2 & 7.70 & 9.55 & 89\% \\ \hline SNI-SLAM & 8.3 & 9.9 & 9.2 & 10.1 & 5.5 & 8.55 & 90\% \\ \hline Sem-SLAM & 8.0 & 9.0 & 8.1 & 8.6 & 7.8 & 8.0 & 92\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of the proposed method and existing NeRF-based SLAM methods in terms of the Absolute Trajectory Error (ATE) metric on the Replica and ScanNet datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Param** & **Memory** & **Slam FPS** \\ \hline NIDS-SLAM & 12.5 M & 1.5 GB & 1.8 \\ \hline SNI-SLAM & 6.3 M & 1.1 GB & 2.12 \\ \hline Sem-SLAM & 8.2 M & 1.3 GB & 2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Runtime and memory comparison on Replica.  
cross-modal feature fusion structure, and various loss functions (color loss, appearance loss, feature loss, depth loss) in our proposed method. This synergy plays a pivotal role in enhancing the model's positioning accuracy and semantic modeling performance in SLAM tasks. Each component is indispensable, and together they form an efficient algorithmic system.
## 4 Experimental Results and Discussion
### Experimental Setting  
The setting of hyper-parameters in this experiment is partly consistent with the parameters of the SoftLexicon(LSTM) model in [PERSON] et al.'s paper [13]. However, due to the characteristics of the dataset used in the experiment, we adjusted some of the hyper-parameters based on repeated experiments to ensure that the model achieves the best performance. For NLPCC2016, we use the Adam optimizer with the learning rate \(1\times 10^{-5}\) and the decay 0.01. The hidden size of the LSTM is 200, the dropout rate is 0.5, and the epoch number is 3. For the power grid dataset, the learning rate is \(3\times 10^{-5}\), the hidden size of the LSTM is 100, and the epoch number is 2. For the NLPCC2016 dataset, due to its relatively large amount of data, we use a simple cross-validation method. We used the training set and the test set divided by the NLPCC and additionally divided 10% of the data from the test set as the validation set. For the power grid dataset, we used the five-fold cross-validation method. We divided the 4523 pieces of data into five parts and used the training set and the test set alternately for experiments. The average value of five experiments was taken as the final evaluation metric.
## 2 Linear and Nonlinear Methods  
The statistical method conducts the prediction using models established based on historical data. Thus, this method is more straightforward and convenient to implement compared with the physical method. In this section, an overview of the commonly used linear and nonlinear models is presented.
# Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
## 5 Comparison with Related Works  
Very few works offer a complete design for IWSs powered by vibration energy. The complete design method includes the PT's performance and defines the specifications of the IWS powered by the harvested energy, more specifically from vibrations. Table 6 shows the comparison with some related designs. The application, as well as the strategy used, are also specified. This comparison was proposed in terms of throughput, transmission range, or else transmission frequency.  
As shown in Table 6, the commonly used method is the Harvest-Store-Use technique, which involves leaving the IWS on standby until enough energy is harvested for data transmission. The design in [64] was one of the first to propose a slaving of sensor performance to harvestable energy. Although a transmission frequency of 10 Hz was reached in this work, it is important to mention that the data compression technique (which can cause loss of information) was used. Besides, the sensor was powered by a hybrid energy harvesting system comprising the PT and a photovoltaic cell.  
In [65], a transmission frequency equal to that achieved in this work was obtained. However, these results are based on laboratory tests and not on an actual industrial application. The same observation can be made for the study in [38]. In [19] by considering most PT optimization solutions (in particular the SSHI technique), it is obtained that the vibrations took at a given moment in a mining locomotive make it possible to transmit data every 7 min at only 958 m, unlike every 1 min to 3 km as is the case in our study.  
Overall, unlike our design method, most of the related designs are based on fixed excitation characteristics (constant frequency and constant acceleration) of the transducer, which is difficult to achieve in a real situation.
## 3 Results and Discussion
### Voghurt Texture and Viscosity  
Changes in yoghurt texture, assessed in terms of firmness, cohesiveness, and stickiness, during storage are presented in Table 11. Significant differences (\(p<0.01\) and \(p<0.05\)) in yoghurt firmness were observed on storage days 7 and 14, respectively. Yoghurt produced from the milk of ewes that received unprocessed orange peel (UOP) was firmer compared to yoghurt from the milk of ewes subjected to the other two treatments. Regarding cohesiveness and stickiness, a treatment effect (\(p<0.05\)) was observed only on storage day 14, with yoghurt from ewes fed the non-hydrolysed orange peel being the most cohesive and stickiest. The textural properties of yoghurt are affected by various parameters, including the quality and composition of the milk (including protein, fat, and total solid contents), the heat treatment of the milk, the milk acidification, and the storage time, among other factors [90]. [PERSON] et al. [16] reported changes in the textural characteristics of yoghurt made from sheep milk produced by animals fed diets containing agro-industrial by-products such as tomato pomace, olive cake or leaves, lentil straw, and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Storage Day/Variable**} & \multicolumn{4}{c}{**Treatment**} \\ \cline{2-6}  & **Control** & **UOP** & **POP** & **SEM** & **Significance** \\  & **(n = 4)** & **(n = 4)** & **(n = 4)** & & \\ \hline \multicolumn{6}{c}{_Lightness (\(L^{*}\))_} \\ \hline  
\hline \hline \end{tabular}  
* UOP = unprocessed orange peel; POP = processed orange peel; \({}^{*}=p<0.05\); \({}^{**}=p<0.01\); NS = non-significant; superscripts a, b differ at \(p<0.05\).  
\end{table} 
Table 10: Instrumental colour changes during storage (days 7–21). Atriplex leaves. Similar to the present study, they found that the differences in texture were neither consistent nor significant enough to adversely affect the quality of the yoghurt. Finally, in relation to changes in texture during storage within the same group, no statistically significant differences (\(p>0.05\)) were observed in any of the examined parameters across all treatment groups. [PERSON] (2011) and [PERSON] et al. (2012) reported changes in yoghurt textural parameters during storage. Yoghurt viscosity is shown in Table 12 and, as shown, there were no significant differences (\(p>0.05\)) between treatments on storage days 7 and 14, whereas significant differences (\(p<0.05\)) were observed on storage day 21 between the groups. The rheological properties of the yoghurt significantly affect the product quality. The fat and the protein contents of the milk are the most important parameters. There were fluctuations in the viscosity of the yoghurt within the same treatment. However, according to [PERSON] et al. (2012), the yoghurt is not a significant difference in the yoghurt's performance. Figure 5: Total colour difference (\(\Delta\)E\({}_{\text{Lab}}\)) between the Control and samples from the UOP and POP treatments during storage.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 2 Financial Time-Series Deep Clustering Network: TCN-Deep Clustering  
To address problems where general clustering methods cannot guarantee that the extracted features fit the clustering structure and current deep clustering algorithms cannot significantly capture the financial time-series features, an end-to-end deep clustering algorithm for financial time series is proposed. The architecture of TCN-Deep Clustering consists of three main components: an encoder, decoder, and temporal clustering layer, as shown in Figure 1. In the first step, a feature extraction network consisting of an autoencoder based on a TCN network is pre-trained and in the second step, the temporal clustering layer is added to fine-tune the hidden feature. The algorithm's details are described as follows.
# Parameters Sensitivity Analysis of Position-Based Impedance Control for Bionic Legged Robots' [PERSON]
## 8 Conclusions  
In this paper, a HDU position-based impedance control method is proposed. The method of sensitivity analysis is selected in research. Dynamic sensitivity analysis and quantificational sensitivity analysis are conducted to study the four main control parameters. The results from the research are verified by experiments. Here are the conclusions summarized in this paper:  
1. Sensitivity analysis method is used to analyze the four parameters, which are the proportional gain and integral gain of the inner loop PID controller, and the desired stiffness and desired damping of the impedance outer loop. The first-order and second-order effects on position are analyzed by the trajectory sensitivity analysis, when the four parameters increase no more than 20%. In spite of its higher accuracy, the second-order sensitivity analysis requires a complex solving process with lots of hard work. Considering that the largest error between the first-order and second-order sensitivity analysis methods is below 20% of the amplitude, this paper proposed the easier first-order matrix sensitivity analysis method. Compared with first-order trajectory sensitivity, the first-order matrix sensitivity method is simpler and more accurate in computing.
2. The effects of parameter variation on output position varies periodically with sinusoidal disturbance force. Among the parameters, desired stiffness affects the output position much more than the other parameters. The influence of integral gain is the least. Desired damping and proportional gain have a similar influence on the output position.
3. The \(S_{1}\) of each parameter is a positive value, which shows that the increase of the four parameters will result in the reduction of the position amplitude attenuation. The \(S_{2}\) of each parameter has positive or negative values in different conditions. Their increase has different influences on the phase angle delay of output positions.  
The project is supported by National Natural Science Foundation of China (Grant No. 51605417), Key Project of Hebei Province Natural Science Foundation (Grant No. E2016203264).  
[PERSON] designed the experiments; [PERSON] wrote the paper; [PERSON] conceived the research idea; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data.  
The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 5 Discussion
Therefore, the principles of \"no coal caving in the upper part, less coal caving in the middle-upper part, normal coal caving in the middle-lower part, and maximum coal caving in the lower part\" should be followed. 3. The output of the working face should be maximized as much as possible while ensuring safety. Figure 20: Comparative analysis of the upper boundary of the TLEZ. The cutting speed must be maintained within an optimal range to ensure both operational safety and production efficiency. Excessive advancing speed exacerbates top-coal fragmentation, significantly compromising working face safety. Conversely, insufficient advancing speed negatively impacts mine productivity by restricting output capacity. Based on the boundary spatial morphology of the TLEZ, the reasonable coal caving principle not only guarantees the high output and high efficiency of the working face but also prevents the further deterioration of the stability of the \"support-surrounding rock\" system, significantly enhancing the safety of the working face. For mining engineers and underground workers, the delineation of the TLEZ boundary provides a methodology for predicting the TLEZ boundaries in similar working faces. This approach enables tailored guidance for the technological sequences and specific construction processes of fully mechanized top-coal caving faces based on varying boundary positions, thereby maximizing mine output while ensuring workplace safety. As all findings were derive from numerical simulations, two key limitations should be noted. Regarding geological variability, results may vary substantially between locations because each coal mine has distinct stratigraphic features. Concerning material assumptions, the model's homogenization of the coal and rock layers fails to capture real-world heterogeneities including faults, fractures, and lithological differences.
###### Abstract  
This research examined the effects of the supplementary or night-interrupting (NI) blue (B) light supplied at a low intensity on the flowering, gene expression, and morphogenesis of chrysanthemum, a qualitative short-day plant. White (W) light-emitting diodes (LEDs) were used to provide light with a photosynthetic photon flux density (PPFD) of 180 \(\upmu\)mol-m\({}^{-2}\)-s\({}^{-1}\) during the photoperiod to grow the plants in a plant factory. The control group was constructed with plants that were exposed to a 10-h short day (SD10) treatment without any blue light. The B light in this research was used for 4 h to either (1) extend the photoperiod for plants at the end of a 9-h short day (SD) treatment as the sole light source (SD9 + 4B), (2) provide night interruption (NI) to plants in the 13-h long-day (LD) treatment (LD13 + NI - 4B), (3) provide NI to plants in the 10-h SD treatment (SD10 + NI - 4B), or (4) supplement the W LEDs at the end of a 13-h LD treatment (LD13 + 4B). Blue LEDs were used to provide the supplementary/NI light at 10 \(\upmu\)mol-m\({}^{-2}\)-s\({}^{-1}\) PPFD. The LD13 + NI - 4B treatment resulted in the greatest plant height, followed by LD13 + 4B. Plants in all treatments flowered. It is noteworthy that despite the fact that chrysanthemum is a qualitative SD plant, chrysanthemum plants flowered when grown in the LD13 + 4B and LD13 + NI - 4B treatments. Plants grown in the LD13 + 4B had the greatest number of flowers. Plants grown in the LD13 + 4B treatment had the highest expression levels of the _cryptochrome 1_, _phytochrome A_, and _phytochrome B_ genes. The results of this study indicate that a 4-h supplementation of B light during the photoperiod increases flower bud formation and promotes flowering, and presents a possibility as an alternative method to using blackout curtains in LD seasons to practically induce flowering. The B light application methods to induce flowering in SD plants requires further research.  
blue LED; flower bud formation; number of flowers; photoperiod +
Footnote †: journal: plants
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 3 Results
#### 3.1.2 Group with Abdomen and Pelvis Imaging  
In the 'Abdomen + Pelvis' group, which included 146 patient observations, the analysis had a stronger association. The correlation between BMI and total DLP was notably robust (Spearman r = 0.70, \(p\)-value < 0.00001), alongside a moderate positive correlation with DLP during the liver phase (Spearman r = 0.44, \(p\)-value < 0.00001). This indicates a significant impact of BMI on radiation dose in more comprehensive imaging that includes both the abdomen and pelvis (Figure 5).  
Is There a Statistically Significant Difference in DLP and DLP in the Liver Phase Values Depending on the Maximum Dimensions of the Patient?
###### Abstract  
The insertion of micro-textures plays a role in reducing friction and increasing wear resistance of the cutters, which also has a certain impact on the stress field of the cutter during milling. Therefore, in order to study the mechanisms of friction reduction and wear resistance of micro-textured cutters in high speed cutting of titanium alloys, the dynamic characteristics of the instantaneous stress field during the machining of titanium alloys with micro-textured cutters were studied by changing the distribution density of the micro-textures on the cutter. First, the micro-texture insertion area of the ball-end milling cutter was theoretically analyzed. Then, variable density micro-textured ball-end milling cutters and non-texture cutters were used to cut titanium alloy, and the mathematical model of milling force and cutter-chip contact area was established. Then, the stress density functions of different micro-texture density cutters and non-texture cutters were established to simulate the stress fields of variable density micro-textured ball-end milling cutters and non-texture cutters. Finally, the genetic algorithm was used to optimize the variable density distribution of micro-textured cutters in which the instantaneous stress field of the cutters was taken as the optimization objective. The optimal solution for the variable density distribution of the micro-textured cutter in the cutter-chip tight contact area was obtained as follows: the texture distribution densities in the first, second, and third areas are second, and third areas are 0.0905, 0.0712, and 0.0493, respectively.  
**Keywords:** variable density; micro-textured ball-end milling cutter; high speed cutting; stress field; genetic algorithm +
Footnote †: journal: Applied Sciences
# Assessment and Evaluation of the Intake of Sugars in European Countries
## 4 Discussion
Other uncertainties regarding the sugar content are processing, recipes, food conversion factors, brands, and market share. The industry in many European countries is reformulating their products in that the sugar-containing products, especially sugar-sweetened beverages, have now a lower sugar content than in the food composition tables used in the studies. By definition, food composition data lag in the reality since an update takes time and can only be completed when the sugar content has actually changed. Therefore, the intake data presented are somewhat outdated. All dietary methods are vulnerable to over- and under-reporting. For sugars, with guidelines to reduce the dietary intake, underreporting is more likely than overreporting. Food items with a negative health image (e.g., cakes, sweets, confectionery) are more likely to be under-reported. According to [PERSON] et al. [54] there are several possible reasons for underreporting: lower consumption during the data-collection period, consuming or reporting smaller portions than normal, or not all consumed food items are reported. Women are more likely to under-report than men, and under-reporting is more common among overweight and obese individuals. Other associated characteristics, for which there is less consistent evidence, include age, smoking habits, level of education, social class, physical activity, and dietary restraint [57]. Most data on the intake of sugars are based on 2-4 days (7 days at the most), which is not a good reflection of the chronic intake of an individual. Chronic diseases do not develop overnight but during many years, and for a link between diet and a chronic disease the information on the habitual dietary intake is needed. [PERSON] et al. [58] studied four methods to estimate the usual intake distribution from two 24-hour dietary recalls and found that the bias in the mean and the variation in the bias increases with a smaller sample size, higher variance ratios, and with more pronounced departures (skewness) from a normal distribution. Often, a large standard deviation (sometimes higher than the mean) is reported, which indicates a skewed (probably to the right) distribution whereby the mean value is in those cases (much) higher than the median value.