esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Vineyard Design and Thinning Treatments  
All experiments were performed with Riesling (_Vitis vinifera_ L.) grafted on SO4 rootstock in a vineyard site located at the Rheingau region (Germany: 49\({}^{\circ}\)59\({}^{\prime}\)30.8\({}^{\prime\prime}\) N, 7\({}^{\circ}\)56\({}^{\prime}\)54.1\({}^{\prime\prime}\) E) and managed by Hochschule Geisenheim University (HGU) between 2017 and 2019. The vineyard was planted in 1998 with an inter and intra row distance of 2 m \(\times\) 1.2 m (vine density: 4167 vines/ha) and a row azimuth of 160\({}^{\circ}\). Conversion to Semi-Minimal-Pruned Hedge training system was conducted in 2012. Three different thinning treatments were investigated considering two mechanical and one biotechnological thinning strategy. Mechanical shoot thinning was applied using the Darwin-Rotor (SMPH ST; Fruit Tec Maschinenbau, Markdorf, Germany) with horizontally rotating strings. Additionally, a second mechanical thinning strategy, which pursued bunch thinning, was performed with a harvest machine (SMPH BT; Grapeliner SF200, ERO, Niederkumbd, Germany) at berry pea size (E-L number 31; [25]). As biotechnological thinning strategy, the plant growth regulator Gibberellic acid (SMPH GA; Gibb 3, Plantan GmbH Buchholz i. d. Nordheide, Germany) was applied at a concentration of 50 ppm during flowering (E-L number 21). The treatments were established in three replicates, each represented by one row (field repetition). All three thinning treatments were compared to standard practice in VSP and a SMPH non-thinned control. Thinning treatments and annual adjustments are presented in Table 1.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
###### Abstract  
Aiming at the removal of radioactive cobalt ions from water by graphene oxide (GO), the adsorption mechanism of Co\({}^{2+}\) on graphene oxide was analyzed using the quantum chemical calculation software Gaussian 16 based on density functional theory. The influence of material structure factors such as carboxyl groups, hydroxyl groups, epoxy groups and graphene sheets as well as external environmental factors such as pH, temperature and interfering ions on the adsorption effect was determined, and the influence of external environment was verified through experiments. Through calculation and experiment, it was found that the existence of oxygen-containing functional groups on graphene oxide can improve the adsorption efficiency of the material appropriately, and increasing the pH under acidic conditions was also helpful to improve the adsorption effect. The material had certain selectivity for Co\({}^{2+}\), and the adsorption capacity and selectivity could be further improved when it was modified by increasing hydroxyl groups.  
GO; Gaussian; pH; temperature; interfering ions +
Footnote †: journal: Physics Letters B
###### Abstract  
Enhanced source location privacy and prolonged network lifetime are imperative for WSNs--the skin of IoT. To address these issues, a novel technique named source location privacy with enhanced privacy and network lifetime (SLP-E) is proposed. It employs a reverse random walk followed by a walk on annular rings, to create divergent routing paths in the network, and finally, min-hop routing together with the walk on dynamic rings to send the packets to the base station (BS). The existing random walk-based SLP approaches have either focused on enhancing only privacy at the cost of network lifetime (NLT) or have aimed at improving the amount of privacy without degrading the network lifetime performance. Unlike these schemes, the objectives of the proposed work are to simultaneously improve the safety period and network lifetime along with achieving uniform privacy. This combination of improvements has not been considered so far in a single SLP random walk-based scheme. Additionally, this study investigates for the first time the impact of the sensors' radio range on both privacy strength and network lifetime metrics in the context of SLP within WSNs. The performance measurements conducted using the proposed analytical models and the simulation results indicate an improvement in the safety period and network lifespan. The safety period in SLP-E increased by 26.5%, 97%, 123%, and 15.7% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. Similarly, the network lifetime of SLP-E increased by 17.36%, 0.2%, 83.41%, and 13.42% when compared with SLP-R, SRR, PRLPRW, and PSSLP techniques, respectively. No matter where a source node is located within a network, the SLP-E provides uniform and improved privacy and network lifetime. Further, the simulation results demonstrate that the sensors' radio range has an impact on the safety period, capture ratio, and the network lifetime.  
radio range; network lifetime; SLP; safety period; uniform privacy; WSN +
Footnote †: journal:  
00 2023 by the authors.  
Liconse MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## Appendix A Solutions of the Governing Equation
### Rigid Joint Model  
For the rigid joint model (\(m=1\)), the solutions for the upper sub-beam (\(s=1\)) are:  
\[\begin{split}&\mathcal{N}^{(11)}=-\frac{1}{a_{2}}\bigg{(}a_{1} \mathcal{M}^{(0)}+a_{0}\mathcal{N}^{(0)}+E\bigg{)}\,,\\ &\mathcal{M}^{(11)}=\frac{1}{\xi}\bigg{[}\eta\mathcal{N}^{(11)}- \bigg{(}a^{(2)}+\frac{h^{(2)}}{2}b^{(2)}\bigg{)}\mathcal{N}^{(0)}-\bigg{(}b^ {(2)}+\frac{h^{(2)}}{2}d^{(2)}\bigg{)}\mathcal{M}^{(0)}-E_{1}\bigg{]}\,,\\ &\mathcal{Q}^{(11)}=-\bigg{[}\bigg{(}\frac{h^{(1)}}{2}+\frac{ \eta}{\xi}\bigg{)}\frac{a_{1}}{a_{2}}+\frac{1}{\xi}\bigg{(}b^{(2)}+\frac{h^{( 2)}}{2}d^{(2)}\bigg{)}\bigg{]}\mathcal{Q}^{(0)}\end{split} \tag{12}\]  
Here, \(\eta\), \(\xi\), \(a_{0}\), \(a_{1}\), \(a_{2}\), \(E_{1}\), \(E_{2}\), and \(E\) are parameters that are estimated by:\[\eta =a^{(1)}+a^{(2)}-\frac{h^{(1)}}{2}b^{(1)}+\bigg{(}h^{(2)}+\frac{h^{(1 )}}{2}\bigg{)}b^{(2)}+\frac{(h^{(1)}+h^{(2)})}{4}h^{(2)}d^{(2)}, \tag{10}\] \[\xi =-b^{(1)}-b^{(2)}+\frac{h^{(1)}}{2}d^{(1)}-\frac{h^{(2)}}{2}d^{(2)},\] (11) \[\mathfrak{a}_{2} =-\bigg{(}d^{(1)}+d^{(2)}\bigg{)}\eta-\xi\bigg{(}b^{(1)}+b^{(2)}+ \frac{h^{(1)}+h^{(2)}}{2}d^{(2)}\bigg{)},\] (12) \[\mathfrak{a}_{1} =\bigg{(}d^{(1)}+d^{(2)}\bigg{)}\bigg{(}b^{(2)}+\frac{h^{(2)}}{2} d^{(2)}\bigg{)}+\xi d^{(2)},\] (13) \[\mathfrak{a}_{0} =\bigg{(}d^{(1)}+d^{(2)}\bigg{)}\bigg{(}a^{(2)}+\frac{h^{(2)}}{2} b^{(2)}\bigg{)}+\xi b^{(2)},\] (14) \[\mathfrak{E}_{1} =\mathfrak{e}_{1}^{(2)}-\mathfrak{e}_{1}^{(1)}+\frac{h^{(1)}}{2} \mathfrak{e}_{2}^{(1)}+\frac{h^{(2)}}{2}\mathfrak{e}_{2}^{(2)},\] (15) \[\mathfrak{E}_{2} =\mathfrak{e}_{2}^{(1)}-\mathfrak{e}_{2}^{(2)},\] (16) \[\mathfrak{E} =\bigg{(}d^{(1)}+d^{(2)}\bigg{)}\mathfrak{E}_{1}-\xi\mathfrak{E}_{2} \tag{17}\]  
At the crack tip \(N\), \(M\), and \(Q\) appear for the rigid joint model so that the equilibrium equations (Figure 3b) are satisfied. \(N\), \(M\), and \(Q\) are:  
\[N=\mathcal{N}^{(1^{\prime})}|_{x^{\prime}=\alpha^{-}}-\mathcal{N}^{(11)}|_{x=0 ^{+}},\ \ Q=\mathcal{Q}^{(1^{\prime})}|_{x^{\prime}=\alpha^{-}}-\mathcal{Q}^{(11)}|_{x= 0^{+}},\ \ M^{*}=M-\frac{h^{(1)}}{2}\mathcal{N} \tag{18}\]  
with \(M=\mathcal{M}^{(1^{\prime})}|_{x^{\prime}=\alpha^{-}}-\mathcal{M}^{(11)}|_{x= 0^{+}}\)  
Moreover, it should be mentioned that Equation (10) is used to estimate the displacements and rotation at the crack tip (\(x=0^{+}\)) as:  
\[u^{(11)}|_{x=0}=u^{(12)}|_{x=0},\ \ \phi^{(11)}|_{x=0}=\phi^{(12)}|_{x=0},\ \ w^{(11)}|_{x=0}=w^{(12)}|_{x=0} \tag{19}\]
## 4 Experiment Validation
### Case 2: Bearing Fault Diagnosis Based on XJTU Bearing Datasets
#### 4.2.2 Diagnostic Results and Analysis  
First, the data were weighted by SAM, and then the weighted data were input into DWSC for DWC and PWC, as well as the subsequent convolution, BN and pooling, Sigmoid in turn, and classification.  
The training and testing results of the proposed method on the XJTU datasets are shown in Figure 13. Figure 13a,b show the accuracy and loss changes of the training and testing datasets with epoch, respectively, and it can be observed that when the epoch is about 12, the model basically reaches convergence, and the changes of accuracy and loss tend to be basically stable, and the highest fault diagnosis accuracy rate is 100%. Figure 13c,d are the visualization results by t-SNE, Figure 13c is the visualization graph of the training result, and Figure 13d is the visualization graph of the test result. It can be clearly seen in Figure 13c,d that diverse fault types have an almost perfect separation degree. Figure 13e,f are the confusion matrix of the training and test results. It can be observed that the model proposed in the paper can identify and classify the five fault types perfectly and the recognition accuracy of each type of fault is 100%.  
Figure 11: Comparisons between the different fault diagnosis methods.  
Figure 12: The experimental platform of XJTU.
# Leveraging Internet News-Based Data for Rockfall Hazard Susceptibility Assessment on Highways
## 1 Introduction
A study that has more data points is the mapping of landslide susceptibility along the Karakoram Highway in Pakistan [10]. This study addresses not only rockfalls, but also rockslides and debris flows. Since the Karakoram Highway's completion in 1979, frequent disruptions due to rockfalls, rockslides, and debris flows have been common, often triggered by heavy rainfall--conditions similar to those in Taiwan. The study employs machine learning models and a landslide inventory comprising 303 points to evaluate the relationship between landslide events and their causative factors. The literature review reveals that few comprehensive studies have been conducted on rockfall hazard susceptibility, with many existing studies on rockfalls often including broader landslide phenomena, rather than focusing exclusively on rockfalls. Additionally, the data points in these studies are typically limited, ranging from 34 to 303, including rockslides and debris flows. Various methods have been employed in hazard susceptibility analysis, such as rockfall rating systems, AHP, physically-based methods, and ANN, among others. In contrast, the current study leverages the power of the internet and Google Alerts to automatically collect daily news from Taiwan from April 2019 to February 2024, thereby creating a comprehensive database exclusively focusing on rockfall events. The locations of these events are pinpointed using Google Earth for an extensive rockfall hazard susceptibility analysis. Specifically, this research focuses on rockfall events near the highway system caused by rainfall, excluding events on other types of roads such as farm roads, urban roads, forest roads, or hiking trails or those caused by earthquakes. Furthermore, this study employs advanced machine learning algorithms, particularly the Random Forest algorithm, known for its superior performance in various applications. The primary objective is to develop a detailed rockfall hazard susceptibility assessment for Taiwan's roadway system.
###### Abstract  
It is known that heat treatment can lead to physical and chemical changes that can decrease or alter the properties of food. This work evaluated the influence on the antioxidant activity of boiling, pressure, and microwave cooking processes on three selected sweet potato varieties (purple, orange, and yellow). The samples were analyzed for total phenols and anthocyanin content and antioxidant capacity. The cooking water of the boiling and pressure processing were also analyzed. The results demonstrated that the purple sweet potato had better phenolic compounds, anthocyanins, and antioxidant activity profiles than the other varieties studied. On the other hand, the yellow sweet potato was the variety that showed the lowest antioxidant activity after applying the different culinary processes. Microwave processing, particularly when applied to purple sweet potato samples, seemed to be the most suitable cooking process to extract the bioactive compounds with antioxidant activity. Related to the cooking water, there were discrepancies between the behavior of different sweet potato varieties, since not all the samples followed similar profiles. In conclusion, it is necessary to study sweet potatoes processed through various cooking methods for antioxidant properties and other characteristics, such as texture, flavor, and nutritional value.  
I 20231372  
hypoglycemic properties, and are responsible for these properties in food [9; 10]. Due to their high nutritional and energy values, sweet potatoes can be used in specific contexts as a functional food in the prevention of food shortages and situations of malnutrition [4; 6]. Still, different sweet potato varieties have differences in composition. Purple sweet potato has a higher anthocyanin content, while phenolic compounds are abundant in yellow and orange sweet potatoes [8]. On the other hand, the culinary processes to which foods are subjected can lead to physical and chemical changes that can decrease or alter their properties [2; 11; 12; 13].  
This research studied the influence of different culinary processes (boiling, pressure, and microwave) on the antioxidant activity of three sweet potato varieties (purple, orange, and yellow). For this, the contents of phenolic compounds and anthocyanins were determined. The antioxidant capacity was also evaluated using the DPPH method in the different raw and processed samples, with and without peel, so that it would also be possible to compare the composition and bioactivity with these different characterization methods. Furthermore, the cooking water of the boiling and pressure processes were also analyzed.
## 5 Discussion  
The key to detecting volcano unrest and understanding the underlying mechanisms is to be able to recognize when a volcano is deviating from its background level of activity. Once the eruption starts on the other hand, the key to decipher the eruptive dynamics and to mitigate the related hazards, is to integrate multiparametric dataset streaming from both space- and ground-based sensors, in order to provide the most comprehensive view of the eruptive phenomena. Both require \"monitoring\", i.e., observing the volcanic activity over long periods of time, during both quiescent and eruptive phases. As such, the aim of monitoring platforms such as MOUNTS is twofold: (1) a scientific one, aiming at deepening our understanding of volcanic processes and patterns at stake at active volcanoes, by processing in a systematic way large amounts of data in an effort to construct global databases, and (2) a societal one, aiming at producing more successful eruption forecasts, and providing additional information to the operational community (e.g., local volcano observatories and civil protection) in order to mitigate the risks related to volcanic hazards.  
The results presented in this paper intend to demonstrate how the monitoring platform MOUNTS can contribute to both scientific and operational aims. We here discuss the benefits, limitations, and future developments of the system.
## 7 Limitations  
As with any user study, there are limitations to this study. First, because the focus groups were conducted via video conferencing, our Older Adult (OA) participants demonstrated a certain level of technological aptitude.  
Second, the stimuli medium is chosen to showcase how each of the 3 robot embodiments would function and interact with the OA. We chose a hand-sketch-style animation to depict the broad strokes of how each robot embodiment assisted an OA with sitting up from laying down, sit-to-stand from the bed and also the couch, providing stability while exercising, and finally stepping up to place an offering on an altar. However, participants did have several detailed questions regarding how different the robots would act: How would Scoot give a boost to a person to stand up? [PU-25,40, PJ12]; Should PILLAR raise up and lift the person while they held on? [PU07]; Is there continued walking support from all robot embodiments? [PU-01,15,33,PJ-04,17,26,27,30,31,35]. Relatedly, although we used the color orange to denotesoft areas on each robot embodiment, participants had to speculate as to what \"softness\" meant; the wide variety of material comparisons in Figure 10 and Appendix C.1.1 along with comments about the robot's density highlight the need for further exploration into this multi-faceted design. Robot design requirements and specifications are premature given this phase of the user-centered design process; however, it was necessary to keep in mind what is presently and in the near future feasible (vs. science fiction). The inspiration for the most preferred robot, TRIbot, was based on a real robot, custom-designed and fabricated by Toyota Research Institute, as noted in Section 3.1.3. Technical details about the custom TTT robot are available at [39], which also details its performance metrics collected during six week-long field tests, spread across 18 months.  
Finally, it should be noted that Care Receivers (CR) participants had differing opinions about whether they needed the robot themselves: Only 19 CRs (14 JP, 5 US) agreed or somewhat agreed, While 13 CRs (3 JP, 10 US) disagreed or somewhat disagreed; the remaining 11 CRs (6 JP, 5 US) were undecided. Despite our inclusion criteria for CRs having a mix of care levels, they either require some help or feel like some help is needed in daily life for some household chores due to physical difficulty/pain. Caregiver (CG) participants speculated about each robot embodiment's acceptance and use of the robot by their CR, usually an aged parent. Our subsequent research focuses on OA CRs themselves and their current and future needs.
## 2 Materials and Methods
### Data Analysis  
The daytime and nighttime periods were separated according to the global radiation threshold of 20 W m\({}^{-2}\)[37]. The half-hour CO\({}_{2}\) fluxes were integrated to their respective daily, monthly and annual periods using Microsoft Excel 2007 Software and MATLAB software. For the meteorological factors, the monthly mean values of \(T_{\mathrm{a}}\), \(RH\), \(E_{p}\) and GWD, and the mean annual \(T_{\mathrm{a}}\) and total annual \(P\), were calculated in the period 2013-2016 and as historical averages (1957-2016). The meteorological data were recorded by the Ejin weather station and provided by China Meteorological Data Sharing Services. Daily mean soil temperature (\(T_{\mathrm{s}}\)), VPD, PAR and GWD were also used to determine the major controlling factors of the carbon fluxes, i.e., NEE, GPP and Re, using appropriate correlation analysis. The Spearman correlations were calculated by SPSS Statistics software package V.16 (SPSS Inc., Chicago, IL, USA).
# To Cache or Not to Cache
## 3 Design  
Non-datapath algorithms such as LARC can be counter-productive to the hit rate as they do not cache an item until it is reused, incurring a compulsory additional cache  
Figure 1: Performance comparison using mARC and ARC over time for MSR workload Proj3. Presented are various moments where mARC is not performing or deciding as well as it could.  
miss per item. Additionally, when working sets change frequently, the requirement of proof of reuse can significantly impair hit rates. In the case of mARC, its high level of complexity comes not from using three states, but rather its seven, built-in, state transition conditions. However, even with these conditions, mARC still excluded the direct state transitions between the Unique Access and Stable states, thereby imposing unnecessary writes and potentially removing \"soon-to-be-hit\" items from the cache in exchange for \"one-hit wonders\".  
The following section explains the design of _Fear of Missing Out_ (**FOMO**) that takes a new approach to storage caching. **FOMO** learns from our findings with both mARC and LARC, but is itself based on a novel approach involving a simple, two-state design: _Insert_ and _Filter_. **FOMO** is designed to be a generic non-datapath _admission policy_ that could augment any datapath cache replacement algorithm and still provide the write reductions and performance that is expected from non-datapath caches.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 2 LoRaWAN Outline
### Transmission Power Control  
LoRaWAN includes an adaptative data rate (ADR) algorithm as a TPC scheme, which is used to improve energy consumption dynamically. The idea behind this scheme is to change the transmission parameters (transmission power and SF) according to the channel state, particularly the behavior of the SNR. As previously discussed, when the SF increases, the effect is that the SNR\({}_{limit}\) can be worse [36], as shown in Table 2. The SNR\({}_{limit}\) is the worst SNR the receiver can tolerate to demodulate the received signal adequately. In the case of LoRaWAN, the SNR\({}_{limit}\) can be negative; that is, the received signal power can be less than the noise floor power. The steps to implement the ADR algorithm are discussed as follows [37].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spreading Factor** & **SNR Limit (dB)** \\ \hline
7 & –7.5 \\
8 & –10 \\
9 & –12.5 \\
10 & –15 \\
11 & –17.5 \\
12 & –20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SNR limit versus spreading factor dependency [36].  
1. The EN enables the ADR scheme and informs the GW. Hence, the NS can change the transmission power and the SF. In addition, the EN sends information with an SF = 12 to guarantee that the data can reach the gateway.
2. The base station collects 20 values of SNR (SNR\({}_{measured}\)) from the node and sends them to the network server.
3. The network server takes the maximum value of the 20 SNR\({}_{measured}\) samples, for example, 5 dB. It also takes the current SF.
4. The network server calculates the margin \(M_{e}\) as shown in Equation (2), \[M_{e}=SNR_{measured}-SNR_{limit}-LM,\] (2) where \(LM\) is the link margin, used as a security term to achieve reliable communications. For example, \(LM=10\,\)dB, SNR\({}_{limit}=-20\,\)dB (SF = 12, from Table 2), so \(M_{e}=5\,\)dB \(-\) (\(-20\,\)dB) \(-10\,\)dB = 15 dB. It means an excess of 15 dB in the link budget, which causes a waste of energy.
5. The network server calculates the \(M_{e}\) again with a lower spreading factor (it always must be greater than zero to guarantee a stable link). For example, for SF = 7, \(SNR_{limit}=-7.5\,\)dB, so \(M_{e}=5\,\)dB \(-\) (\(-7.5\,\)dB) \(-\) 10 dB = 2.5 dB.
6. Because there is an excess of 2.5 dB, the network server also lowers the transmission power.