esa-sceva/satcom-chunk-collection huggingface dataset:
# Intent to Adopt Location Sharing for Logging Safety Applications
## 3 Results  
### Completed Sample  
We distributed 331 surveys across the three LEAP update meetings (Lewison: 117, St. Maries: 83, Sandpoint: 131), and received 296 completed questionnaires (Lewiston: 96, St. Maries: 80, Sandpoint: 120) for an overall response rate of 89%. Sixteen surveys (Lewiston: 4, St. Maries: 6, Sandpoint: 6) were completed by non-loggers, including foresters and millworkers, as stated by participants in the job title question. We removed these 16 from analysis, so our final sample comprised of 280 surveys (85% of the total distributed). The response rate was high in comparison to similar studies administering surveys at logger workshops (e.g., 31% by [PERSON] et al. [41] and 78% by [PERSON] et al. [40]) and quite high for survey response generally. According to the Bureau of Labor Statistics' Quarterly Census of Employment and Wages, Idaho employed an average of 1,290 loggers (North American Industry Classification System code 113310) in 2016 [50], and in June 2018 the number of certified logging professionals in the state was 424 [51]. Thus, our completed sample represented approximately 66% of all Pro-Loggers and about 22% of all Idaho loggers.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 3 Results
### Feature Importance Analysis  
Feature importance analysis is a key step in machine learning, which can help us to assess the degree of contribution of each feature to the prediction results and to understand the decision-making way of the model. And it can also help us to identify the reasons for model performance degradation through the importance of each feature and to find the potential problems in the model and optimize and improve it accordingly. Therefore, feature importance analysis plays a very important role in model evaluation, understanding, and optimization.  
RF feature importance is derived by calculating the importance of each feature in all decision trees. XGBoost feature importance is derived from how often each feature is used as a split feature in all decision trees. Although both feature importance is judged by different criteria, they are integrated algorithms with stability and robustness. Therefore, RF and XGBoost are chosen for feature importance analysis in this paper.  
As can be seen from Figure 11, in RF feature importance, the feature that affects OMC the most is PL with an impact factor of 0.598, followed by LL with 0.294, and the lesser impacts are FC, Sand, and SG. In XGBoost feature importance, the feature that affects OMC the most is LL with an impact factor of 0.476, followed by PL with 0.297, and the lesser impacts are FC, Sand, and SG. In summary, the two features that affect OMC the most are PL and LL, which is consistent with geotechnical engineering experience.  
As can be seen from Figure 12, in RF feature importance, the two features that affect the MDD the most are LL and PL, whose influencing factors are 0.403 and 0.391, respectively, and the lesser influences are FC, SG, and Sand. In the feature importance analysis of XGBoost, the feature that affects the MDD the most is PL, with an influence factor of 0.466, followed by LL with 0.25, and the lesser influences of FC, Sand, and SG. Similarly, the two features that affect the MDD the most are PL and LL.  
Figure 10: Regression plots between actual and predicted values with XGBoost; (**a**) OMC; (**b**) MDD.  
The above findings coincide with those found in the literature [12; 28]. Feature importance analysis is meaningful in engineering to guide feature selection, interpret model predictions, identify data anomalies, guide decision making, etc., thus improving the efficiency, performance, and credibility of engineering.
_Appl. Sci._**2012**, \(2\), 166-174; doi:10.3390/app2010166  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Reduction and Immobilization of Potassium Permananate on Iron Oxide Catalyst by Fluidized-Bed Crystallization Technology**  
**[PERSON] 1, [PERSON] 2,3,*, [PERSON] 2, [PERSON] 2 and [PERSON] 1**  
Department of Environmental Engineering, Wuhan University, P.O. Box C319 Luoyu Road 129#, Wuhan 430079, China; E-Mails: [EMAIL_ADDRESS] (G.-X.L.);  
eeng(@whu.edu.cn (H.Z.)  
Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan;  
E-Mails: [EMAIL_ADDRESS] (T.-C.C.); [EMAIL_ADDRESS] (Y.-J.S.)  
Sustainable Environment Research Center, National Cheng Kung University, Tainan 701, Taiwan  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-6-275-7575x626-36; Fax: +886-6-234-4496.  
_Received: 9 February 2012; in revised form: 21 February 2012 / Accepted: 23 February 2012 / Published: 1 March 2012_  
**Abstract:** A manganese immobilization technology in a fluidized-bed reactor (FBR) was developed by using a waste iron oxide (_i.e._, BT-3) as catalyst which is a by-product from the fluidized-bed Fenton reaction (FBR-Fenton). It was found that BT-3 could easily reduce potassium permanganate (KMnO\({}_{4}\)) to MnO\({}_{2}\). Furthermore, MnO\({}_{2}\) could accumulate on the surface of BT-3 catalyst to form a new Fe-Mn oxide. Laboratory experiments were carried out to investigate the KMnO\({}_{4}\)-reduction mechanism, including the effect of KMnO\({}_{4}\) concentration, BT-3 dosage, and operational solution pH. The results showed that the pH solution was a signifcant factor in the reduction of KMnO\({}_{4}\). At the optimum level, pH\({}_{\rm f}\) 6, KMnO\({}_{4}\) was virtually reduced in 10 min. A pseudo-first order reaction was employed to describe the reduction rate of KMnO\({}_{4}\).  
**Keywords:** Fluidized-Bed Reactor (FBR); iron oxide; potassium permanganate
## 3 Results  
### In Vitro Studies  
#### 3.1.1 Preliminary Study  
The presence of flavonoids and phenolic compounds was confirmed by the respective assays as described above. The appearance, smell, and the percentage yield of garlic methanol extract were found to be brownish, pungent, and 6.32%, respectively. Further, total phenolic compounds in the garlic extract was determined to be \(18.45\pm 0.08\) mg gallic acid equivalent/g dry weight of extract that is nearly same as detailed by previous researchers [27]. It is indeed important to keep in mind that phenolic contents show the means of protection against infections or environmental stress, and therefore, are not related with growth activity and plant tissue development [12]. Moreover, any presence of sulfur compounds probably decreases the total phenolic and flavonoid content.  
The chemical composition of garlic (_Allium sativum_) extract has been investigated previously. Phenolic acids, including vanillic acid, caffe acid, \(p\)-coumaric acid and ferulic acid, anthocyanins like cyanidin-3-(6\({}^{\prime}\)-malonyl)-glucoside) and organosulfur compounds such as L-allilin and methih were shown to be the major components of garlic extract [28]. HPLC GC/MS analysis identified luteolin-7-glucoside, luteolin, apifenin-7-glucoside, hyperoside, rutin, caffeic acid, chlorogenic acid and rosmarinic acid in as the major phenolic compounds methanolic garlic extract [29]. However, another study revealed that pyrogallol (139.35 to 418.80 ug/g), rutin (51.35 to 90.45 ug/g) protocatechuic acid (31.75 to 76.25 ug/g), \(\beta\)-resorcyclic acid (313.5 to 452.ug/mL), gallic acid (67.40 to 196.80 ug/g) and quercetin (25.70 to 56.65 ug/g) were the principal phenolic constituents in garlic extract [30].  
To calculate the total flavonoid content of the extract, the colorimetric \(\mathrm{AlCl_{3}}\) test employing quercetin as reference was used. The total flavonoid content was found to be \(13.58\pm 0.01\) mg quercetin equivalents (QE)/g dry weight of the extract.  
1.2 \(\alpha\),\(\alpha\)-Diphenyl-\(\beta\)-Picrylhydrazyl (DPPH) Free Radical Scavenging Method  
The potential of giving electron, and reducing DPPH free radical is exploited in this assay to evaluate the antioxidant activity of garlic extract. It was seen that the addition of garlic extract to DPPH solution reduced the DPPH free radical to the corresponding hydrazine. DPPH reduction was investigated by recording the drop in absorbance at 517 nm. Our results suggest that garlic has an excellent DPPH free radical scavenging activity and this ability at 600 ug/mL was shown to be the most notable. Our data suggest that garlic extract might be a rich source of anti-oxidant compounds and its DPPH scavenging activity increases dose-dependently. The DPPH radical scavenging test results are shown in Figure 1.
## 2 Materials and Methods
### Ability of SIF to Monitor Drought-Induced Changes in NPP Dynamics
During a typical drought event (Figure 9b), the SIF/NPP ratio fluctuated significantly from May to mid-June. The ratio rapidly increased from approximately \(-3\) to \(11\) before dropping rapidly to approximately \(-7\). During this period, the relationship between SIF and NPP changed rapidly. From mid-late June, the SIF/NPP ratio became positive and gradually stabilized, indicating a synchronous trend in the dynamic changes between SIF and NPP, with a relatively stable relationship. From September to early October, the SIF/NPP ratio again showed substantial fluctuation, rapidly increasing from approximately \(-4\) to \(14\). NPP gradually stabilized. During a severe drought event (Figure 9c), the SIF/NPP ratio became positive around mid-May, indicating that the dynamic changes between SIF and NPP were gradually synchronized. However, the relationship between the two was not yet constant. In approximately early July, the SIF/NPP ratio gradually became negative, indicating gradual desynchronization in the dynamic changes between SIF and NPP. By mid-July, the SIF/NPP ratio gradually increased above zero and remained stable. Around mid-September, the SIF/NPP ratio began to become negative and remained unchanged for approximately half a month before turning positive again. This trend indicates a brief asynchrony in the dynamic changes between SIF and NPP. During an extremely severe drought event (Figure 9d), the trend in SIF/NPP change was similar to that of a severe drought event from June to December, and the relationship between SIF and NPP showed similar characteristics. Four drought severity levels for the two drought events were compared. During the drought event from October 2013 to September 2014, the overall trend in the SIF/NPP ratio change was similar. The relationship between SIF and NPP was more stable in early spring, summer, autumn, and winter, whereas it was unstable during the spring-summer and summer-autumn transition periods, which was consistent with our overall drought event research results. In addition, the ability of SIF to monitor the dynamic changes in NPP induced by severe drought events was weaker than that of other drought severity levels. During the drought event from May to December 2019, significant differences were  
Figure 9: Dynamic changes in SIF/NPP with an 8-day time interval in (**a**) mildly drought-affected regions, (**b**) moderately drought-affected regions, (**c**) severely drought-affected regions, and (**d**) extremely drought-affected regions from May to December 2019.  
present in the SIF/NPP ratio among different drought severity levels from May to June, which was the spring-summer transition period, and the relationship between SIF and NPP was unstable. This result is consistent with that of our previous analysis. From July to the end of the drought, the trend in the SIF/NPP ratio changes was the same for different drought severity levels.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 1 Introduction
In other words, it works in a peer-to-peer fashion with implicit help from the common neighbors of the claimant and verifier. For example, sensors collect the sensed data coming from neighbors for a certain amount of time and use relevant data to verify the location of the claimant after the appropriate calculation. The advantage of the proposed scheme is that it can effectively verify the claimed location of a sensor without incurring significant communication and computation overhead. This is because our scheme does not request the participation of all of the sensors in the network and does make use of simple calculations for the location verification. In addition, there are no requirements of special  
Figure 1: Localization and location verification. hardware, dedicated verifiers and a trusted third-party for the verification. The contributions of this work can be described as follows:  
* No prearranged setup or sensor deployment knowledge is required. The proposed scheme works based on the MSR token, which will be explained in Section 4. The MSR token can be acquired independently by the claimant and verifier without explicitly exchanging of information. * The proposed scheme works in a localized way, not involving the entire networks whenever location verification is needed. Unlike the other location verification schemes, the proposed scheme can perform the verification process with sensors around the area where the location verification is required. Many of the other location verification schemes involve the entire network for the verification process even when only one sensor has a problem with its location. * Little communication and computation overhead will be caused by the location verification scheme. The scheme utilizes the data packets, such as temperature sensing data from sensors, to deliver the information for location verification. With this implicit data exchange, two verification packets are needed to verify a single location claim. Moreover, simple calculation in the verification algorithm does not incur much computation overhead for the sensors. The remainder of this paper is organized as follows. Section 2 discusses the related works. Section 3 provides the system and attack model for the proposed scheme. Section 4 describes the proposed scheme, which is named mutually-shared region-based location verification (MSRLV). Afterward, the security and performance analysis are provided in Section 5, and the verification performance of the proposed scheme is evaluated in Section 6. Section 7 will discuss further considerations on the performance improvement. Finally, Section 8 concludes the paper.
## 3 Wake-Up Radio Communication Protocols
### Passive Wake-Up Signal Packet
#### 3.2.3 Wake-Up Signal Frame Trailer  
The frame-check-sequence (FCS) is performed on the WuS frame to check the integrity of the data using cyclic redundancy check (CRC) or FEC.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 5 Experimental Evaluation
### Plm-Icd  
Table 4 shows a summary of the results for PLM-ICD, obtained for test with threshold of 0.5, 0.4 and 0.2, and \(\frac{1}{6}\) of the test dataset, the same as used for the Cosine method.  
As the table shows, PLM-ICD performs better than the Cosine method using the approaches described before. However, there is still room for improvement, especially considering the difficult trade-off between precision and recall. As the threshold decreases, PLM-ICD finds more codes, but many of them are false positives. So recall increases but precision is dramatically affected.  
[PERSON], et al. (2023) [13] reported a better F1-score on the test set (58.5% compared to 46.9% in the present work), using PLM-ICD on MIMIC-IV with ICD-10 labels. They used a bigger test set consisting of 24,461 records, following the partition method proposed in [22]. In the present work, because of the lack of computational resources, the volume of data was smaller and that may justify the different results.
## 3 Results and Discussion
### Color Parameters and Sensory Analysis  
Color is one of the most important psychological properties of food products that influence consumers' perceptions. The BGP exhibited color parameters of 1.52 \(\pm\) 0.21 L, 0.05 \(\pm\) 0.03 a\({}^{\star}\), and 0.26 \(\pm\) 0.06 b\({}^{\star}\), which were similar to the results reported by [PERSON] et al. (2016). The color of the bread crumbs changed to dark brown as a function of the % BGP added (Table 6). The degree of lightness progressively decreased, the redness (a\({}^{\star}\) value) increased, and the yellownes (b\({}^{\star}\) value) decreased from the wheat bread to the bread with 3% BGP. These results are according to a sensory evaluation (Table 6), in which the color intensity due to the gradual addition of BGP, particularly 3 g/100 g, was judged as too dark.  
The effects of the addition of BGP on the sensory characteristics of the bread are reported in Table 7. Preferences for color, texture, taste, and smell and overall acceptability were evaluated using a hedonic scale from 1 to 5. The addition of BGP progressively increased the color intensity and changed the taste and smell of the bread, reducing the overall acceptability. The addition of 3 g/100 g BGP significantly reduced the overall acceptability due to a stronger smell, a darker color, and drier crumbs than the control. The addition of 0.5 g /100 g of BGP did not significantly change the sensory characteristics of the bread and its overall acceptability. Finally, the addition of 1-2 g/100 g modified the sensorial attributes, but the product remained acceptable with a characteristic flavor.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Samples** & **L** & **a\({}^{\star}\)** & **b\({}^{\star}\)** \\ \hline Wheat bread & 62.55 \(\pm\) 1.03 a  & \(-\)4.08 \(\pm\) 0.05 a  & 16.89 \(\pm\) 1.08 a  \\
0.5 g/100 g & 46.36 \(\pm\) 1.00 b  & \(-\)1.77 \(\pm\) 0.22 b  & 15.40 \(\pm\) 1.01 ab  \\
1 g/100 g & 33.34 \(\pm\) 3.07 c  & \(-\)1.47 \(\pm\) 0.09 b  & 13.51 \(\pm\) 1.51 b  \\
2 g/100 g & 37.33 \(\pm\) 1.37 d  & \(-\)1.07 \(\pm\) 0.13 c  & 13.33 \(\pm\) 0.66 b  \\
3 g/100 g & 24.72 \(\pm\) 1.40 d  & 0.24 \(\pm\) 0.13 d  & 12.83 \(\pm\) 1.20 b  \\ BGP & 1.52 \(\pm\) 0.21 & 0.05 \(\pm\) 0.03 & 0.26 \(\pm\) 0.06  \\ \hline \hline \end{tabular} Columns with different letters indicate significant differences (\(p\) < 0.05), and 0.5 g/100 g, 1 g/100 g, 2 g/100 g, and 3 g/100 g correspond to wheat bread with 5, 10, 20, and 30 g/kg of black garlic powder added, respectively.  
\end{table}
Table 6: Color parameters in bread crumb samples (mean \(\pm\) SD) and black garlic powder (BGP).
# Computationally Efficient Soft Detection Schemes for Coded Massive MIMO Systems +
###### Abstract  
This paper presents a computationally efficient soft detection scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme adopts joint iterative detection and decoding (JIDD) methods for their capacity limiting performances. In addition, the minimum mean square error parallel interference cancellation (MMSE-PIC)-based detection scheme is used for soft information exchange. We propose a number of techniques to reduce the computational complexity, while keeping almost the same performance as the conventional ones. First, a technique is proposed to approximate the Gram matrix to a constant valued diagonal matrix. This proposal can lead to elimination of complex matrix inversion process and multiple layer dependent estimations, resulting in huge complexity reduction. Second, compact equations to estimate soft-symbol values for \(M\)-ary (quadrature amplitude modulation) QAM are derived. From the investigation example of \(2^{8}\)-QAM in this paper, this proposal showed more than two orders of less computations compared to the conventional scheme. The simulation results demonstrate that the proposed method can achieve approximating performance to the conventional method with a largely reduced computational complexity.  
massive MIMO; soft detection; JIDD; coded MIMO; MMSE-PIC Article
# New Insight on the Formation of 2-Aminoacetophenone in White Wines
## 4 Discussion
On the other hand, Mn was proven to be a strong promoter of sulfite oxidation in the presence of traces of Fe and Cu [22]. In our conditions, the presence of a higher level of Mn was actually rather associated with a higher content of 2-AAP. However, this trend was not observed when the wine had a higher initial Fe(II/III) concentration. This shows the complexity of chemical reactions yielding 2-AAP in a wine medium and can explain why the main limiting factors of 2-AAP formation in wine are not well identified yet. Further research will have to investigate more in detail the interactions between transitional metal ions and wine matrix. This study opened new perspectives in the understanding of 2-AAP synthesis in white wines. Both varietal differences in 2-AAP formation and interactions between antioxidants and transition metals require further investigations. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13148472/s1](https://www.mdpi.com/article/10.3390/app13148472/s1)]([https://www.mdpi.com/article/10.3390/app13148472/s1](https://www.mdpi.com/article/10.3390/app13148472/s1)), Table S1: Wine list for survey 1 and 2 with corresponding concentration in 2-AAP; Table S2: Supplementary data 2: Chemical parameter of wines used for the experiments. Conceptualization, G.A. ; methodology, G.A., T.R.V., C.P., M.S., V.S.S. and A.V. ; software, T.R.V., P.E., A.V., K.S. and M.S. ; validation, T.R.V., P.E., C.P., M.S., V.S.S., A.V. and K.S. ; formal analysis, T.R.V., P.E., M.S., A.V. and K.S. ; investigation, G.A., T.R.V., C.P., M.S., V.S., A.V. and K.L. ; resources, G.A., C.P., K.L., M.S.L. and R.E. ; data curation, G.A., T.R.V., P.E., C.P., M.S., A.V. and K.S. ; writing--original draft preparation, T.R.V., G.A., C.P., M.S. and A.V. ; writing--review and editing, C.P., M.S., V.S.S., A.V., K.L. and M.S.L. ; project administration, M.S.L., G.A., R.E., A.V. and V.S.S. ; funding acquisition, G.A. All authors have read and agreed to the published version of the manuscript. This work was cofunded by the Slovenian Research Agency (ARRS) research project L4-1842 titled \"Influence of heavy metal ions on shelf life of white wines\" and research program P1-0034 titled \"Analysis and chemical characterization of materials and processes\" and the Biolaffort company. Not applicable. The data presented in this study are available on request from the corresponding author. We would like to thank [PERSON] and [PERSON] from Biolaffort, and [PERSON] and [PERSON] from Laffort, for the scientific and technical discussions during this project. We would like also to acknowledge all the producers who provided wine samples for this work. The authors declare no conflict of interest.