esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PPS Algorithm
However, if multiple combinations within the same block meet the assembly tolerance, the algorithm refines its selection further by selecting the combination closest to the ideal dimension, thus minimizing deviation from the ideal dimension. This block-based selection strategy reduces the possibility of a uniform distribution of the assembled component clearances within the tolerance band. By prioritizing combinations that result in assembled component dimensions close to the optimal, PPS leads to a distribution that is more centered around the ideal clearance than that of other algorithms, thereby reducing variance. ```
1 A \(\leftarrow\) array of measurements of parts A
2 B \(\leftarrow\) array of measurements of parts B
3 strideA \(\leftarrow\) stride for array A
4 strideB \(\leftarrow\) stride for array B
5 A \(\leftarrow\) SortBySimilarity(A)
6 B \(\leftarrow\) SortBySimilarity(B)
7 n \(\leftarrow\) length(A)
8 m \(\leftarrow\) length(B)
9 initialize empty list P
10 fori \(\gets\) 1...n do
11 forj \(\leftarrow\) 1...m do
12 p \(\leftarrow\) quotient(i/strideA) + quotient(j/strideB)
13 ifp \(\geq\) length(P), then
14 add empty list to P
15 endif
16 add tuple (i, j) to P[p]
17 endfor
18 endfor
19 initialize empty list Q
20 forblock in P do
21 fori,j in block do
22 r \(\leftarrow\) dimension when A[i] and B[j] are assembled
23 if r satisfies tolerance, then
24 e \(\leftarrow\) absolute of error between r and ideal dimension
25 add tuple (e, i, j) to Q
26 endif
27 endfor
28 if Q is not empty then
29 break
30 endif
31 endfor
32 select A[i], B[j] by i, j, where minimum e is from Q
```  
**Algorithm 2**Predominance Preferential Selection  
The stride parameter is important in determining the granularity of priority rankings within each slot row. When stride is set to one for all slot rows, each component retains an individual priority ranking, and no blocks are formed. In this case, PPS operates similarly to DBP, where the combinations are evaluated and selected strictly according to their priority order. Conversely, if stride equals the total slot capacity of a row, all components in that row will be grouped into a single priority level, effectively bypassing the need for priority distinctions and focusing purely on minimizing the deviation from the target dimension across all combinations. This flexibility allows PPS to be adjusted to varying assembly conditions and requirements by accommodating both narrow and wide prioritization ranges.
# Infill Pattern and Density of 3D-Printed Insoles Alter Energy and Pressure Distribution in Gait
###### Abstract  
Diabetic foot ulcer patients are usually unaware of excessive pressure on their feet due to lost pressure sensitivity. Offloading insoles are used to alleviate foot pain. This study investigated the effect of infill pattern and infill density on plantar pressure reduction for 3D-printed insoles while walking. The study involves five infill patterns: grid, honeycomb, triangle, cubic, and gyroid, along with several infill densities ranging from 14% to 20%. The test 3D-printed thermoplastic polyurethane specimens were assessed for mechanical properties to identify suitable infill patterns for creating prototypes of insoles. The results indicated that the honeycomb infill pattern exhibited the highest maximum compression load at 50% compressive strain and has a significant area under the loading-unloading curve, signifying high energy absorption. Conversely, the gyroid infill pattern exhibited the lowest maximum compression load at 50% compressive strain and minimal energy absorption. Both infill patterns with 20% infill density were applied in 3D-printed insoles and tested on the foot plantar pressure of healthy male volunteers during walking. No statistically significant differences in plantar pressure were observed between the two infill patterns compared to walking without insoles. A reduction in plantar pressure at hindfoot was observed when using the insoles, although this change was not statistically significant.  
diabetic foot; 3D-printed insole; infill pattern; infill density; plantar pressure Article
## 4 Conclusions
Some differences between the shapes of simulation and measurement attenuation curves (e.g., location of the fades) can be attributed to an imprecise representation of room geometry and material parameters in computer models. Indoor BAN simulation studies for a simulation domain corresponding to an average-sized office room are feasible using XFDTD software package with only a moderate PC computer configuration containing two nVIDIA Tesla C2075 GPU cards. With this computer configuration the simulations took from 45 min to 70 min depending on the FDTD algorithm convergence for a given simulated case. The authors plan to carry out an in-depth study to formulate a practical indoor BAN radio channel path loss model taking into consideration various on-body antenna configurations as well as room dimensions and human body location as parameters. The model should be of help to engineers designing BAN destined for operation in particular indoor environments.
## 1 Introduction  
The Nanjing region is the most important tungsten-tin-rare metal metallogenic province in China, and most of the tungsten-tin deposits were formed in the Mesozoic and are closely related to granite [1; 2; 3]. The mineralization is explosive, mainly divided into two stages at the Late Jurassic stage (165-150 Ma [2]; or 160-150 Ma [3]) and the Cretaceous stage (130-90 Ma, peaking at 100-90 Ma [2; 3]). In terms of spatial distribution, there are obvious differences in the metallogenic element assemblages of deposits in the eastern and western segments of the Nanling region. There are many large-scale tungsten-dominated deposits and a few tin-dominated deposits in the eastern segment, such as Shizhuyuan [4; 5; 6; 7], Xihuashan [8; 9; 10; 11], Piaotang [12; 13], Taoxikeng [14], Dajishan [15; 16; 17], and Yaogangxian [18; 19]. In addition, there is a series of large-scale tin-dominated deposits in the western segment such as Xianghualing [20; 21; 22], Furong [23; 24], and Xitian [25; 26]. According to previous studies, the magmatism related to tungsten-tin mineralization in these orefields or deposits all occurred in the Late Jurassic stage (160-150 Ma).  
The Xianghualing tin-polymetallic orefield (XHLOF), located in the middle of the Nanling region (Figure 1, modified from [27]), is representative of large-scale tin-polymetallic deposits and unique independent large-size rare metal deposits [20]. The mineralization in XHLOF is complex with a variety of mineralization types and metallogenic elements. Previous chronological data show that the ages of the metallogenic granites in the XHLOF are concentrated in 160-150 Ma ([22], and the metallogenic ages obtained by molybdenite and mica dating are 161-154 Ma which is basically consistent with the ages of the metallogenic granites [20,21].  
During our field investigation of the Laohuya lead-zinc-tin deposit in the north of XHLOF, apart from the stratiform-like cassiterite-sulfide orebody which is the representative mineralization type formed in 160-150 Ma, steep hydrothermal vein-type orebodies containing wolframite, galena and sphalerite were noted. Moreover, the steep hydrothermal vein-type orebody cut through the stratiform-like cassiterite-sulfide orebody, which implies there may have been a new stage of mineralization. Meanwhile, the concealed granite related to mineralization was found by drilling, which provided an opportunity to judge whether there was a new stage of magmatism later than the Late Jurassic stage in XHLOF.  
To verify the existence of Late Cretaceous magmatism in XHLOF and constrain its petrogenesis and tectonic setting, we report the data of zircon U-Pb geochronology, zircon trace elements, whole-rock petrochemistry and zircon Lu-Hf isotopes of the Laohuya granite (LG), which also helps to clarify the magma-metallogenic stage and may contribute to the improvement of existing deposit models of the XHLOF in future studies.
## 3 Results and Discussion
### Determination of Relevant Visual Information
#### 3.2.2 Preliminary Observations from the Practice Clip  
A screen shot from the practice video clip (clip P) is shown in Figure 6. Participants described the practice video clip as the left boom of a sprayer applying chemicals on the field (based on mist present in the video). Specific features that were seen included the spray pattern, properly functioning nozzles, stable but relatively low boom height, no visible obstacles in the field, and the presence of green crop (short in height). Participants also noticed that the field was level, had previously been sprayed (given the wheel tracks), and had several regions where no crop was growing. Most participants identified boom features (left boom height, nozzle, and spray pattern) and crop (or field) conditions as the information that was gained from watching the practice clip. A majority (86%) of the participants indicated that the information provided by the practice clip was something they typically viewed while spraying. Among the 14% of participants who did not indicate that such information was typically viewed during spraying, 50% of them felt that it would useful to see the areas provided by the practice clip. When asked how they would use the information gained from the practice clip, participants indicated that the information would help them to identify if nozzles were plugged, to set their guidance line to match the previous passes or tracks (since the field had previously been sprayed), to determine the type of spraying method that would be economical (i.e., adopting full application or spot spraying), and to make navigation decisions (e.g., deciding whether to go into low areas of the field, considering the risk of getting stuck). Two participants also noted that the information would give an operator the assurance that they were doing a good job. In response to the fifth question, 86% of the participants indicated that the information provided by the practice clip was \"very important\" or \"extremely important\". Almost two-thirds of the participants (62%) indicated that it would be difficult to gain the same information if the live video was replaced with an encoded display. Overall,  
Figure 5: Grouping of participants by age and with respect to their years of experience in spraying.  
analysis of the data from the practice clip gave us confidence that participants accurately understood the purpose of the research and the questions in the questionnaire.
# DRAM Retention Behavior with Accelerated Aging in Commercial Chips
## 4 Experimental Results and Analysis
### Retention Error in Fresh DRAM Modules
#### 4.2.3 Design-Induced Variations  
There exists variation in access latency among different regions in a DRAM chip due to the relative distances of different components from the peripheral structures. Cells located closer to the sense amplifiers can be accessed faster as they experience smaller RC delays [27]. Design-induced variation is static and depends on the physical organization, i.e., it is always determined by the cell's location. DRAMs with the same internal design demonstrate a similar pattern in failure distribution [27].  
In Figure 5, we observe the design-induced variation for different DRAM vendors. The x-axis represents the bins of row numbers where every 512 consecutive rows are grouped together. The y-axis represents the aggregated number of bit flips in a group of 512 consecutive rows for patterns 0 xFF and 0x00. We observe that the number of failures in the rows gradually increases with increased distance from the peripherals, and this pattern periodically repeats. In addition, the design-induced variation pattern repeats every 4096 pages (eight groups of 512 pages). The repetition is caused by the introduction of a new global sense amplifier and completely dependent on a specific array architecture. We observe two different regions in the Micron design, which is explained in detail in Section 4.2.4.  
Figure 4: Distribution of retention errors across rows and columns for pattern 0 xFF and 0x00 in a Micron module.  
Figure 5: Retention error in a module increases periodically across the row due to design variations. (**a**) Retention error (\(t_{ret}\) = 20 s) in a Micron 1 GB DRAM module. (**b**) Retention error (\(t_{ret}\) = 20 s) in a Hynix 2 GB DRAM module. (**c**) Retention error (\(t_{ret}\) = 20 s) in a Samsung 2 GB DRAM module.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Interpretation of Model Outputs
#### 3.2.1 Biological Oxygen Demand (BOD)  
Biological Oxygen Demand is a key indicator of the organic pollution level in a water body. It is a measure of the quantity of oxygen used by microorganisms in the oxidation of organic matter. When BOD levels are high, there is less dissolved oxygen available for higher organisms, which can lead to a decline in biodiversity and overall ecosystem health [25; 26; 52].  
The high importance of BOD in our models suggests that organic pollutants, likely originating from residential sewage and waste, are a primary concern for an urban lake's water quality. This aligns with the known issue of untreated or inadequately treated sewage discharges in urban areas.
## 3 Results
### Train-Test Split Models Results
#### 3.1.3 Combined Dataset  
The combined Emotional and Neutral Stimulus Dataset, which integrated 209 samples, each characterized by approximately 20 characteristics, was divided into a training group consisting of 167 samples and a validation group of 42 samples. In this specific case, models such as Random Forest, Logistic Regression, and AdaBoost were found to have commendable results, with metric values within the training group exceeding or equal to 80%. In the context of the validation group shown in Table 4, the precision and F1 score indices manifested the following results for these three models: 0.76 for both parameters in the case of the Random Forest, 0.64 for both in the case of logistic regression, and 0.6 and 0.59, respectively, for AdaBoost. The Random Forest model emerged as the algorithm that achieved the most favorable performance within this combined dataset. It can be said that this last Random Forest result even exceeded that of 81% of the Neutral Stimulus dataset due to the increased data flow and generalizability.
###### Abstract
To achieve decimeter-to-meter accuracy during 30 s of signal loss with four visible satellites, newer, more affordable dual-frequency GNSS receivers are integrated with MEMS-IMUs. It is estimated that dual-frequency processing provides a tenfold improvement over single-frequency processing [28]. Due to the availability of GNSS chipsets for smartphones, native sensor fusion methods that utilize smartphones' onboard inertial sensors have been investigated. Using PPP, RTK, and other accurate techniques, smartphone positioning with sub-meter-to-decimeter accuracy is achievable with static positioning in open-sky environments [29; 30; 31; 32; 33]. However, obtaining precise and continuous positioning with smartphone raw observations in complex urban environments remains challenging. This is due to the frequent pseudorange gross errors, carrier-phase discontinuities, and the limited availability of GNSS ambiguity-fixed results in actual kinematics. Recent advancements in GNSSs have significantly improved location-based services across various applications. However, the accuracy of GNSSs can be compromised in urban canyons, areas with dense foliage, or environments characterized by a high building density. In these scenarios, INSs provide crucial navigation data independently of GNSS signals, ensuring continued accuracy and reliability. Recognizing the complementary strengths of GNSSs and INSs, this study investigates the different methods of integrating INS observations with GNSS data. By comparing these methods, we aim to identify the most effective approach to enhance the navigation accuracy in challenging environments--a step forward in applications ranging from autonomous driving to urban planning. Consequently, this paper evaluates various data processing methods based on INS observations, assessing their performance in typical GNSS-compromised conditions and illustrating their broader implications for precise navigation solutions. The objective of this study is (1) to increase the accuracy of positioning moving vehicles in complex urban environments using Android smartphones; (2) to process the collected data from two Android smartphones using SPP and RTK positioning methods, and fusion methods utilizing GNSS raw observations of varying frequencies and constellations and INS observations; (3) a comparison of the results of the processed smartphone data with the U-Blox GNSS receiver.
## 3 Ecotoxicity
### Aquatic Biomodels
#### 3.1.3 Algae  
There are not many studies of DESs in algae. [PERSON] et al. [84] studied the ecotoxicity of pure DES in _Raphidocelis subcapiata_ (_R. subcapiata_) (algae), which is a unicellular green alga commonly used in this kind of study due to its easy cultivation, good sensitivity, and involvement in biomagnification processes [99; 100]. This information is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline
**DES (Molar Ratio)** & **EC\({}_{\text{50}}\) (mg/L)** & **Reference** \\ \hline ChCl:glycerol (1:2) & 2528 \(\pm\) 151 & [84] \\ ChCl:glycerol:water (1:2:1) & 1762 \(\pm\) 83 & [84] \\ ChCl:urea (1:2) & 1099 \(\pm\)25 & [84] \\ ChCl:urea:water (1:2:1.7) & 1533 \(\pm\) 37 & [84] \\ ChCl:EG (1:2) & 1868 \(\pm\) 175 & [84] \\ ChCl:EG:water (1:2:1) & 2353 \(\pm\) 46 & [84] \\ \hline \end{tabular}
\end{table}
Table 4: Ecotoxicological data of some DESs in _D. magna_.  
The range concentrations tested in algae were 1000-40,000 mg/L for Rel, 100-50,000 mg/L for RelW, 500-60,000 mg/L for Gly, 1000-45,000 mg/L for GlyW, 200-45,000 mg/L for Etha, and 100-45,000 mg/L for EthaW. Results for the algae biomodel followed a decreasing pattern, as follows: RelW > EthaW > GlyW > GLy > Rel > Etha.
## Monitoring Model Results
### Comparative Results of Monitoring Model Accuracy  
In order to further verify the accuracy and reliability of the monitoring model established by the PSO-BP neural network algorithm, the genetic algorithm (GA)-optimized BP neural network algorithm was selected for reliability comparative analysis.  
GA is an optimization algorithm developed based on [PERSON]'s theory of evolution and Mendelian genetics; it is used for neural network parameter optimization and genetic mechanism biological evolution processes [21; 36]. Figure 7 shows the fitness curve of the GA of the monitoring model. When the evolutionary algebra was 30 times, the genetic algorithm could not reach the state of convergence. When the evolutionary algebra was 200 times, the GA still could not reach the state of convergence.  
Figure 8 shows the prediction accuracy of the monitoring model established using the GA-BP neural network algorithm. It can be seen from the figure that the predicted output of all test samples is basically the same as the actual expected output. Combined with the GA-BP neural network algorithm prediction error analysis in Table 7, it can be concluded that the maximum error rate of the test sample is 10.05%, the minimum error rate is 1.45%, and the average error rate is 6.23%.  
Figure 7: Fitness curve of the GA of the monitoring model.  
Table 8 shows the average error rate of 10 runs of the GA-BP neural network algorithm; the maximum average error rate is 22.10%, the minimum average error rate is 6.10%, the average error rate (10 times) is 10.81%, and the GA-BP neural network algorithm training time is 91.53 s. The GA-BP neural network can effectively monitor the woodworking tool wear conditions, but the long training time of the model restricts its application. The proposed PSO-BP neural network algorithm is superior to the GA-BP neural network algorithm in terms of error rate and training time. Overall, the proposed PSO-BP neural network algorithm performs better than the GA-BP neural network algorithm.
## 3 Results
### Correlation of CUE Extremes with GPP and Ra Extremes  
We used the partial correlation method to explore the CUE correlation with GPP and Ra at different seasons (Figure 5). The results indicated there were significant positive correlations between CUE and GPP, with partial correlation coefficients larger than 0.6 in most regions of global. In contrast, the correlation of CUE with Ra was negative in most global areas in different seasons, with the R being smaller than \(-0.6\). Thus, positive CUE extremes may result from positive GPP extremes and negative Ra extremes, and negative CUE extremes may result from negative GPP extremes and positive Ra extremes. Therefore, in this study, we investigated the probabilities of concurrence of positive CUE extremes with positive GPP extremes and negative Ra extremes and negative CUE extremes with negative GPP extremes and positive Ra extremes (Figure 6).  
The probabilities of GPP extremes resulted in CUE extremes being larger than Ra (Figure 6). The possibilities of positive GPP extremes resulting in positive CUE extremes were larger than 25% in most areas of the globe (Figure 6a), but the areas of negative Ra extremes that resulted in positive CUE extremes were less and mainly concentrated in some regions of Europe (Figure 6b). Similarly, the probabilities of negative GPP extremes resulting in negative CUE extremes were larger than 25% in most areas of the globe (Figure 6c), but the areas of positive Ra extremes resulted in negative CUE extremes being small (Figure 6d). These results indicated that CUE extremes were mainly controlled by GPP rather than Ra extremes.  
Figure 5: Spatial pattern of the partial correlation coefficients between CUE with GPP and Ra in (**a,b**) MAM, (**c,d**) JJA, (**e,f**) SON, and (**g.h**) DJF. “MAM” contains the month of March, April, and May; “JJA” contains the month of June, July, and August; “SON” contains the month of September, October, and November; “DJF” contains the month of December, January, and February.