esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Validation of Spatial TDR by Outflow Logging  
The measurement from the spatial TDR sensor was firstly validated by the outflow data logged using an electrical bench scale. First, the tangent method was used to determine the travel time for each TDR trace sent from the column bottom. Second, the previously determined travel time was used to calculate the total mean capacitance using Equation (8). Then, the real part of soil permittivity can be calculated using Equation (7). Finally, the volumetric moisture content is calculated by [PERSON]'s model in Figure 8. Based on the outflow and volume of the specimen, the mean volumetric moisture content was eventually calculated and plotted in Figure 8. The moisture prediction from the spatial TDR sensor strongly agrees with the moisture content calculated from the outflow data. This demonstrates the success of electrical design with corresponding parameter calibration of the spatial TDR sensor and the applicability of [PERSON]'s model for this loamy sand.  
Figure 6: (**a**) The illustration of a single column drainage test without sample filled; (**b**) TDR waveforms indicating a drop in water table [62].  
Figure 8: The mean water content dynamics for the loamy sand column in comparison with the mean water content from outflow data.  
Figure 7: TDR traces for gravitational drainage test: (**a**) TDR traces measured from the top; (**b**) TDR traces measured from the bottom [62].
## 3 Results
### Influence of the Tilt Angle
#### 3.3.2 Influence of the Tilt Angle on the Pore Pressure and VWC  
To investigate the effects of the tilt angle, the pore pressure sensor (S-I) and VWC sensor (W-L) at the bottom were considered for comparison in different cases, as these are most crucial to the  
Figure 14: The VWC changes with elapsed time in case I, II, III, and IV (the VWC distribution after slope failure was verified by obtaining soil samples from various locations at the end of the test). (**a**) Case I. Multi-layer slope, 0 deg, 75 mm/h; (**b**) Case II. Multi-layer slope, 7 deg, 75 mm/h; (**c**) Case III. Multi-layer slope, 15 deg, 75 mm/h; (**d**) Case VII. Multi-layer slope, 21 deg, 75 mm/h.  
Figure 13: Effect of the tile angle on the capillary barrier diversion length in a multi-layer slope in case I, II, III, and IV (each case was performed three times for the same experimental conditions).  
knowledge on slope failure. Detailed in Figure 14 is the progressive build-up of VWC and pore water pressure throughout the experiments I, III, and IV, until failure occurred.  
For Figure 15a,b, it is clear that an increased tilt angle has a drastic effect on the build-up of volumetric water content and pore water at the bottom of the slope, progressively resulting in quicker failure times as the tilt angle increases.  
Once the wetting front arrived, sensor spikes in measurements occurred. It took around 1.18, 0.78, and 0.62 h for the pressure head and VWC to approach the maximum value in case I, III, and IV, respectively. The peak value of VWC and pore pressure values were similar for each case ranging between 0.348 and 0.351 and \(-\)0.021 and \(-\)0.016 kPa, respectively, at times of failure. Due to the rapid progression of the wetting front at an increased tilt angle, the pore water pressure and VWC increased at a faster rate, as is evident in results showing that the \(\alpha=21^{\circ}\) (case V) failed 47% sooner than \(\alpha=0^{\circ}\) (case I) and 34% sooner than \(\alpha=15^{\circ}\) (case III). These similar trends were exhibited in all experiments where the tilt angle was increased [27].
## 2 Methodology
### Adsorption Experiments  
An evaluation of the influence and prediction of optimal conditions for the adsorption parameters was conducted using a three-level Box-Behnken experimental design. The independent variables studied were pH, solid concentration, and contact time, while caffeine removal percentage and caffeine final concentration (C\({}_{\text{F}}\)) were selected as response functions. The independent factors were set at three levels, as indicated in Table 1, and their ranges were defined according to [PERSON]'s [45] study.  
The adsorption tests were conducted in batch experiments using a Wagner shaker with 360\({}^{\circ}\) stirring in Schott glass flasks (250 mL capacity). Each experiment involved 100 mL of a caffeine solution with an initial concentration of 20 mg L\({}^{-1}\). Following the designated contact time, the samples were filtered, and the concentration of the remaining caffeine solution was analyzed using a UV-Vis spectrophotometer (Pro-Analise model UV-1100, Sao Paulo, Brazil) at a wavelength of 273 nm. The caffeine removal percentage was calculated using Equation (1):  
\[Removal\ (\%)=\frac{C_{0}-C_{F}}{C_{0}}\cdot 100\% \tag{1}\]  
where \(C_{0}\) (mg L\({}^{-1}\)) is the initial CAF concentration and \(C_{F}\) (mg L\({}^{-1}\)) is the residual CAF at contact time t.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **pH** &
\begin{tabular}{c} **Solid Adsorbent** \\ **Concentration (mg L\({}^{-1}\))** \\ \end{tabular} & **Contac Time (min)** \\ \hline (+1) & 11.0 & 10.0 & 120.0 \\
0 & 7.0 & 5.5 & 65.0 \\ (\(-1\)) & 3.0 & 1.0 & 10.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Independent variable values at three different levels during CAF adsorption onto GAC.
It is essential to note that although finite element SIF solution methods are relatively economical, the associated computation time is often extensive. Consequently, conducting systematic research on cracks with diverse geometric shapes and expansion paths becomes impractical. To address this limitation, a simplified method for calculating SIFs for surface cracks with different positions, orientations, and sizes has been developed. This method is based on the weight function program developed by [PERSON] [44] and [PERSON] [45]. Utilizing this approach [46; 47], one only needs information about the geometry of the crack propagation path, the stress within the crack plane, the stress perpendicular to the crack plane, and the approximate weight function suitable for the crack geometry. In the operational phase of a silicon nitride ceramic bearing, the contact position between the bearing ball and ring undergoes constant variation. Consequently, the load contact position in relation to the surface crack of the ring channel is also in continual flux. Different load contact positions lead to distinct surface crack SIFs. Surprisingly, previous studies have not delved into the analysis of the crack propagation mechanism on the surface of silicon nitride ceramic bearing rings based on the relative contact position of cracks and loads. This paper initiates its exploration by conducting a rolling contact friction experiment, determining the friction coefficient essential for subsequent simulations. Subsequently, a three-dimensional finite element contact model is established between the silicon nitride ceramic ring and the ceramic ball. Following the insertion of the surface crack of the ring channel into the Franc3D-V8.4 software, a joint simulation of Franc3D-V8.4 and ABAQUS2020 is executed. The ensuing step involves calculating the crack front SIFs for various load contact positions during different propagation stages of cracks with diverse initial depths. The ensuing discussion delves into the changing patterns of crack front SIFs at distinct load contact positions as crack propagation unfolds. Notably, the research identifies the contact position where the maximum effective stress intensity factor load determines the crack front. Simultaneously, the paper explores the influence of the initial crack depth on the cycle life and direction of crack propagation.
## 8 Discussion and Analysis  
The results showed that the accuracy in predicting soil improvement by the pre-implementation of the DC technique using CPT data ranged from 78% to 100%. The accuracy of the prediction underlines the effectiveness of the developed framework in predicting soil improvement after the implementation of the DC technique. Moreover, the developed framework was able to predict the effectiveness of the DC technique in improving the soil bearing capacity in 99.08% of the layers, which indicates the effectiveness of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Soil Category (\(i\))** & **L Range** & **SIR** & **N(\(j\))** & **NPISIR(\(j\))** & **PA** & **OPA** & **PPI** \\ \hline
1 & \(\mathrm{I_{c}}\leq 1.31\) & [20, \(\sim\))0 MPa & & 172 & 1.00 & & \\
2 & \(1.31<\mathrm{I_{c}}\leq 2.05\) & [10–20] MPa & & & 1165 & 1.00 & & \\
3 & \(2.05<\mathrm{I_{c}}\leq 2.6\) & [5–10] MPa & 2275 & 799 & 0.99 & 0.99 & 1.06 \\
4 & \(2.6<\mathrm{I_{c}}\leq 2.95\) & [1–5] MPa & & 66 & 0.79 & & \\
5 & \(\mathrm{I_{c}}>2.95\) & [0–1] MPa & & 73 & 0.99 & & \\ \hline \end{tabular}
\end{table}
Table 6: Summary of Soil Improvement Range for Soil categories.  
Figure 2: pre and post improvement qc vs. \(\mathrm{I_{c}}\).  
developed framework in evaluating the effectiveness of the DC technique in improving soil bearing capacity using initial CPT data, as shown in Table 3.  
Figure 2 shows the soil bearing capacity before and after the implementation of the DC technique; the bearing capacity of the soil was significantly enhanced, as illustrated in the Figure 2. The results are in good agreement and consistent with [6; 10; 18], who suggested that the soil layers with I\({}_{\text{c}}\) values higher than 2.95% may not receive a significant improvement in soil bearing capacity, which eventually leads to the lower efficiency of DC. However, soil types where the predicted negligible q (0 to 1 MPa) means that they require improved soil bearing capacity do not justify the use of the DC technique due to the impracticability in terms of cost and time required to implement it. In addition, these studies established that soil with an I\({}_{\text{c}}\) value of less than 1.31 can experience a 20 MPa improvement in load-bearing capacity. On the other hand, both studies confirmed that soil layers with an I\({}_{\text{c}}\) value between 1.31 and 2.95 experience an improvement in soil bearing capacity ranging from 1 to 20 MPa. Table 3 illustrates the SIR with the percent error for each category ranging from 0 to 21%, reflecting a good accuracy status for the proposed case study. The reliability and accuracy of the proposed framework is around 99.08%, reflecting that the framework is a reliable basis for geo-technicians, designers, and decision makers to rely on as a guide for the DC engineering process. Finally, the study results support the soil improvement results of [PERSON] and [PERSON] (2018).
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 5 Implementation and Evaluation of the Solution  
A real testbed was deployed at the Computer Science Faculty (University of Murcia). As can be seen in Figure 3, real plans of the building floors are used to show the main architectural components and one of the validation tests. Prior to the tests, 26 WiFi APs were registered with their positions (latitude and longitude), as well as their measured RSSI at 1 meter of distance using the smartphone app. In addition, five BLE tags were installed at strategic points. For the sake of clarity, these tags were depicted with the technology symbol, while WiFi APs were included as points surrounded by waves. Only some of the doors of the first floor of the building were provided with contact-less card readers; that supposes approximately 34 of them. However we only used one in the trials in order not to compromise workmates privacy. It is shown in purple in the map on the right of the first floor, as can better be seen in the zoomed part of the map. The door opening system of the building was prepared to send positions to the positioning system when the user was identified by the ID card readers.  
The main part of the positioning system was implemented as a Java library used in an smartphone app, and a small server with a REST interface developed in PHP, which worked as a proxy between the door opening system of the building and our positioning system. The app could continuously  
Figure 3: Deployment and operation test of the positioning solution.  
collect radio measurements and generate position estimates. Since the scan of access points and BLE devices took approximately five seconds, a new position could not be outputted at a higher frequency, so the system provided the same estimated location several times if a higher frequency was requested. The system always responded immediately with the last estimated position available. Every estimation was accompanied with a time-stamp, so the user could estimate the age of every received approximation by the positioning system.
## 3 Results
### Failure Mode Determination  
Table 2 and Figure 3 illustrate the frequency of the identified failure categories within each group. Adhesive failure was the most prevalent failure mode, occurring in 100% of specimens in groups 2 and 3 and in over 80% of specimens in the remaining groups. In contrast, mixed failure was observed in all groups, except groups 2 and 3. None of the groups exhibited cohesive failure. Figure 4a-g shows the percentage of adhesive failures observed in groups 1 to 7, respectively, while Figure 4h-l shows the percentage of mixed failures in groups 1, 4, 5, 6, and 7, respectively.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Mean SBS \(\pm\) SD**} & \multicolumn{4}{c}{**Failure Mode Percentages (\%)**} \\ \cline{3-5}  & & **Adhesive** & **Mixed** & **Cohesive** \\ \hline
1. No saliva contamination, & & & & \\ no additional cleaning (control) & 22.24 \(\pm\) 2.37 \({}^{\text{A}}\) & 22 (88\%) & 3 (12\%) & 0 (0\%) \\ \hline
2. Saliva without rinsing & 0.90 \(\pm\) 0.20 \({}^{\text{B}}\) & 25 (100\%) & 0 (0\%) & 0 (0\%) \\ \hline
3. Saliva, rubbed with distilled water, & 1.35 \(\pm\) 0.43 \({}^{\text{B}}\) & 25 (100\%) & 0 (0\%) & 0 (0\%) \\ \hline
4. Saliva, rubbed with Ivoclean, & 18.51 \(\pm\) 3.01 \({}^{\text{C}}\) & 22 (88\%) & 3 (12\%) & 0 (0\%) \\ rinsed with distilled water & & 20.92 \(\pm\) 2.63 \({}^{\text{AC}}\) & 21 (84\%) & 4 (16\%) & 0 (0\%) \\ \hline
6. Saliva, rubbed with Tooth Primer, & 21.43 \(\pm\) 2.81 \({}^{\text{AC}}\) & 22 (88\%) & 3 (12\%) & 0 (0\%) \\ rinsed with distilled water & & 20.87 \(\pm\) 2.35 \({}^{\text{AC}}\) & 23 (92\%) & 2 (8\%) & 0 (0\%) \\ \hline \hline \end{tabular} The different uppercase letters represent significant differences at \(p\) < 0.05.  
\end{table}
Table 2: Shear bond strength (mean \(\pm\) standard deviation (MPa)) and failure mode percentages (%).
## Materials and Methods
### Total RNA Extraction and Quantitative Reverse Transcription (RT-q) PCR  
Total RNA was isolated from cultured cells using TRIzol (Life Technologies, Rockville, MD, USA) following the manufacturer's protocol. cDNA was synthesized by reverse transcription using a High Capacity RNA-to-cDNA Kit and was used for RT-qPCR using the Universal Probe Library method in a Light Cycler 96 system (Hoffmann La Roche, Basel, Switzerland). The PCR thermal cycling was as follows: initial denaturing at 95 \({}^{\circ}\)C for 30 s, 40 cycles of 92 \({}^{\circ}\)C for 5 s, and 60 \({}^{\circ}\)C for 20 s. Relative mRNA levels were calculated using the comparative 2\({}^{-\Delta\Delta\text{Cq}}\) method and normalized to the amount of \(\beta\)-actin [31]. The specific primers were as follows: forward 5\({}^{\prime}\)-AAGGAGGCCTGCTATTTGAA-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-AAAAGCATTGAAACTTGAAA-3\({}^{\prime}\) for occludin; forward 5\({}^{\prime}\)-AAAATTTAAACTAAGTCA GACTGGAGA-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-TCAGCTTTGGTGAGAGAAGG-3\({}^{\prime}\) for ZO-1; forward 5\({}^{\prime}\)-CAGCCGTGTTTTGAGCA-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-CAAACCACCGCTTACAGATG-3\({}^{\prime}\) for Claudin-1; forward 5\({}^{\prime}\)-AACCTGGCATGGACTTGAAA-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-GGTCAAGTTGTGGGCGCAC-3\({}^{\prime}\) for Claudin-3; forward 5\({}^{\prime}\)-AACCTGTCCCGAGAGAGA-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-GCAAGTGGTGAGCAGACCAG T-3\({}^{\prime}\) for Claudin-4; forward 5\({}^{\prime}\)-CAGCTCTGTGGAAGG-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-TGCTTTCAAGAGGTC ACC-3\({}^{\prime}\) for JAM-1; and forward 5\({}^{\prime}\)-AAGTCCTTGCCATCATAAA-3\({}^{\prime}\) and reverse 5\({}^{\prime}\)-ATGCTA TCACCTCCCTG-3\({}^{\prime}\) for the housekeeping gene \(\beta\)-Actin.
## 3 Relational Extraction Model PTKRE
### Input Layer  
First, we introduce prompt information in the form of \"$ [ent1] $ and # [ent2] # are related in the sentence through <mask> <mask> sentence\": at the beginning of the sentence. Subsequently, we replace \"[ent1]\" and \"[ent2]\" with the head entity and the tail entity in  
Figure 1: Overall structure of the model. In the figure, “$” and “#” are special tokens, where \(S_{i}\) to \(S_{j}\) are head entities and \(S_{k}\) to \(S_{m}\) are tail entities.  
the text, respectively. Specific examples are provided in Table 1. Next, we need to apply a replacement operation to all sentences in the dataset. The special token \"<e1>\" and \"</e1>\" denoting the start and end positions of the head entity in the sentences are replaced with the token \"$\", while the token \"<e2>\" and \"</e2>\" indicating the start and end positions of the tail entity are replaced with the token \"#\". Finally, \"
\" is added at the beginning of each sentence, and \"
\" is added at the end. For example, a sentence in the dataset \"A <e1> girl </e1> plays her <e2> violin </e2> on a pogo stick\". after processing would become: \"
 $ girl $ and # violin # are related in the sentence through <mask> <mask> sentence: A $ girl $ plays her # violin # on a pogo stick 
\". Next, we use the previously modified sentence as the input sequence \(S_{input}\) = {\(x_{0}\), \(x_{1}\), \(x_{2}\),..., \(x_{t}\)} for RoBERTa, and we finally obtain the corresponding vector representation H = {\(h_{0}\), \(h_{1}\), \(h_{2}\),..., \(h_{t}\)} from RoBERTa, with \(h_{0}\) containing the feature information of the entire sentence.
## 6 Pitfalls and Potentials
### Virtualization Feasibility of Network Functions
#### 6.2.3 Virtualization of RAN Functions  
In a 5G RAN architecture, the BBU functionality is split into the CU and the DU [136]. The BBU can be virtualized using the OpenAir-Interface (OAI) software platform. However, virtualizing the BBU can be challenging due to the high processing and timing requirements on certain functions [137].  
In a RAN, multiple functions can be virtualized [124]. The CU oversees multiple base stations and manages network resources. Virtualizing the CU is easier than the DU because it has lower processing requirements [138]. The control plane and user plane within the CU can both be virtualized using off-the-shelf hardware [139]. Conversely, the DU handles real-time Layer 1 and Layer 2 scheduling tasks [136]. Virtualizing the DU poses challenges due to strict latency requirements and the necessity for real-time operations. Nonetheless, technological advancements have facilitated the connection of the DU to the radio via eCPRI, which can indeed be virtualized [139].  
As per our knowledge, while documenting this article we did not find a specific list of network functions that could not be virtualized using NFV. It is important to note that the decision to virtualize a network function depends on various factors, such as the nature of the function, performance requirements, and cost considerations. The feasibility of virtualizing a network function is a subject of ongoing research and development in the field of 5G and beyond cellular networks [140; 141; 142].
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
###### Abstract  
This paper presents a novel reconfigurable SRAM CRP PUF that can achieve high reliability and randomness. In conventional reconfigurable SRAM CRP PUFs, imprecise timing control can produce a biased response output, which is typically attributed to mismatches in the connection of input control signals to the two inverter arrays in the layout floorplan. We propose a timing control scheme along with the addition of an adjunct NMOS transistor to address this issue. This eliminates the connection mismatches for the challenge and word-line inputs to the two inverter arrays. Furthermore, we employ symmetric layout techniques to achieve the randomness of response output. The symmetric arrangement of the two inverter arrays maximizes the inherent random output characteristics derived from process variation. The pre-charge input signal is symmetrically connected to each array to prevent delay mismatches. A 16 \(\times\) 9-bit reconfigurable PUF array is fabricated by using a 180 nm CMOS process, with a PUF cell area of 1.2 \(\times\) 10\({}^{4}\) F\({}^{2}\)/bit. The experimental results demonstrate an inter Hamming distance of 0.4949 across 40 chips and an intra Hamming distance of 0.0167 for a single chip in 5000 trials. The measured worst bit error rate (BER) is 4.86% at the nominal point (1.8 V, 25 \({}^{\circ}\)C). The proposed prototype exhibits good reliability and randomness, as well as a small silicon area when compared to the conventional SRAM CRP PUFs.  
PUF; SRAM CRP PUF; randomness; stability; authentication; internet of things 2024, 1309. [[https://doi.org/10.3390/electronics13020309](https://doi.org/10.3390/electronics13020309)]([https://doi.org/10.3390/electronics13020309](https://doi.org/10.3390/electronics13020309))
###### Abstract  
An integrated CO\({}_{2}\)/O\({}_{2}\) co-gasification system of municipal solid waste (MSW) and bituminous coal (BC) with CO\({}_{2}\) capture was developed and simulated by the Aspen plus, which mainly consisted of three processes: air separation unit, co-gasification system, and CO\({}_{2}\) absorption unit. In addition, raw syngas composition, cold gas efficiency (CGE), and overall energy efficiency (OEE) of the entail system were evaluated in detail with respect to the main operating parameters (gasification temperature, \(T\); oxygen equivalence ratio, \(R_{\rm o}\); mole of CO\({}_{2}\) to carbon ratio, \(R_{\rm c}\); and the MSW blending ratio, \(R_{\rm M}\)). The results indicated that the addition of BC improved the gasification of MSW. Higher gasification temperature increased CGE and OEE. Increasing the \(R_{\rm c}\) ratio led to the decrease of H\({}_{2}\) mole fraction due to the enhanced reverse water-gas shift reaction. In addition, the CGE and OEE of the system decreased with increasing \(R_{\rm M}\). From the analyses of the parameters, the most optimal operating conditions were set as \(T\) = 900 \({}^{\circ}\)C, \(R_{\rm o}\) = 0.2, \(R_{\rm c}\) = 0.5, and \(R_{\rm M}\) = 0.6, and the corresponding OEE of the system reached 0.57. The system can achieve a large processing capacity of MSW at the cost of the efficiency loss of this condition.  
municipal solid waste (MSW); bituminous coal; co-gasification technology; CO\({}_{2}\)/O\({}_{2}\) atmosphere; thermodynamic equilibrium model +
Footnote †: journal: Applied Physics Letters