esa-sceva/satcom-chunk-collection huggingface dataset:
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Watermark Embedding Module
#### 4.1.2 Compression Conversion  
In this paper, _L-Bits to N-Packets_ coding scheme is introduced to solve the problem of channel capacity reduction. The selection of \(L\) and \(N\) size is related to the data transmission rate in this scheme. According to [PERSON] et al. [44], _8-Bits to 3-Packets_ coding scheme is chosen to simplify the encoding and decoding algorithms and improve their efficiency.  
In the _8-Bits to 3-Packets_ coding scheme, every _8-Bits_ binary code is mapped to a non-negative integer vector \((n_{1},n_{2},n_{3})\), where \(n_{1}+n_{2}+n_{3}\leq 10\). Then, vector \((n_{1},n_{2},n_{3})\) is mapped to interval arrival delay sequence \((T_{1},T_{2},T_{3})\) according to Equation (7).  
\[T_{i}=T_{\min}+n_{i}\Delta,i=1,2,3 \tag{7}\]  
For example, the mapping vector \((n_{1},n_{2},n_{3})\) of the \(F\) is \((1,1,5)\). If the minimum interval arrival delay of the current network is \(T_{\min}=50 ms\) and the interval sequence step size is \(\Delta=10\) ms, then it can be mapped into interval arrival delay sequence \((T_{1},T_{2},T_{3})=(50+10,50+10,50+50)=(60,60,100)\). To embed them into the packet stream, four packets need to be sent. Their sending time is \((t_{0},t_{1},t_{2},t_{3})=(0,60,120,220)\), and the total time of embedding \(8-bit\) binary information is \(220\) ms.  
A mapping table with the same size and content is maintained in the controller. The conversion between binary sequence and vector is carried out combined with Equation (7). By the same encoding table based on \(T_{\min}\) and \(\Delta\), the SDN controller keeps the mapping relationship between the binary sequence and the delay sequence in the sender and receiver.
# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 3 Dataset Description
The Shimmer3 GSR+ Units are non-invasive and completely painless sensors that could be easily worn by the participants, as shown in Figure 1.  
CLAWDAS data acquired during cognitive and listening tasks were considered here. The experimental protocol was composed of the following steps:  
* 3 min of questionnaires to collect the personal details and the current emotional state of each participant using STAI Questionnaires;
* 1 min of Baseline (BL) in the resting state condition;
* 6 min of Reading (R) and Comprehension (C) tasks composed by two repetitions (trials) of 2 min of R followed by 1 min of self-assessment and C questions;
* 1 min of Baseline (BL) in the resting state condition;
* A 15 min sequence composed of six repetitions of the following two tasks: 1. 2 min of Audio Listening (AL); in this task, relaxation was induced by natural sounds (Figure 2, right);
* 30 s of cognitive load, induced by mental Math Calculations (MCs) that involve sums, subtractions, and multiplications (Figure 2, left). Each repetition has a different audio track and Math Calculation. * 1 min of Baseline (BL) in the resting state condition  
The experimental protocol was reviewed and approved by the Research Ethics Committee at The University of Tokyo, Japan (Nos. 19-283 and 19-376). The CLAWDAS dataset was partitioned into two distinct subsets, according to the age of the participants: CLAWDAS Young, which included all the signals acquired from young adults, and CLAWDAS Elderly, which grouped all the signals collected from the elderly. In the following analysis, the two groups were considered separately. Table 1 reports the number of instances for each task in the CLAS and CLAWDAS datasets, keeping distinct the two subsets of CLAWDAS related to subjects' age (Young and Elderly). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Num Subj.** & **BL** & **MP** & **ST** & **LP** & **NS** & **BL** & **MC** & **AL** & **R** & **C** \\ \hline CLAS & 60 & 60 & 60 & 60 & 60 & 360 & - & - & - & - \\ \hline CLAWDAS Young & 16 & - & - & - & - & - & 46 & 96 & 96 & 32 & 32 \\ \hline CLAWDAS Elderly & 20 & - & - & - & - & - & 60 & 120 & 120 & 40 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of instances for each task in the CLAS dataset (first 5 columns) and in the CLAWDAS dataset (last 5 columns), distinguished into CLAWDAS Young and CLAWDAS Elderly. BL = Baseline, MP = Math Problem, ST = Stroop Test, LP = Logic Problem, NS = Neutral State, MC = Math Calculation, R = Reading, C = Comprehension. Figure 1: Sensors used to collect physiological data in the CLAWDAS dataset.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 3 Evolution, Architecture, and Implementation of the IFC Schema
### Core Architecture
_IfcProductExtension_ also defines entities to define the spatial hierarchical breakdown of project structures using the entities _IfcSite_, _IfcBuilding_, _IfcBuildingStorey_, and _IfcSpace_. Resource layer:  
This is the lowest layer and includes all individual schemas containing resource definitions, i.e., basic entities to define geometry, material, quantity, measurement, data, time, etc. Entities and types defined in this layer can be referenced by all entities in the layersabove. As resource definitions do not have a concept of identity (such as a GUID), multiple objects referencing the same instance of a resource entity do not imply a relationship. For example, _IfcGeometricConstraintResource_ defines the resources used to determine the placement of the shape representation of a product within the geometric representation context of a project using entities such as _IfcLocalPlacement_ and _IfcGridPlacement_.
# A Power-Gated 8-Transistor Physically Unclonable Function Accelerates Evaluation Speeds
## 2 Power-Gated PUF Architecture and Design Methods
### 8-Transistor PUF Design  
A distinctive feature of the 8T PUF is preparing a clean status for the next power-on cycle with a high reset speed. This clean status means attackers cannot read out any retention data. Unlike some 8T SRAM designs [49; 50] concentrating on improving the write margin and dealing with the opposite requirements for read stability and write ability, this 8T PUF abandons the write function and keeps only the SRAM read ability. The physical parameter mismatch, which affects SRAM stability, is actually a vital feature of PUFs. Since mismatch amongst transistors increases with the scaling down the physical size, the 8T PUF uses the smallest transistor to maximise the mismatch. An 8T PUF can be fabricated by the same process as MCUs, so a special process for high-density SRAM is not needed.  
The 8T PUF cell shown in Figure 1a stems from conventional 6T-SRAM [51]. Four transistors, namely _PM0_, _PM1_, _NM0_ and _NM1_, make up two cross-coupled inverters for storing data. The unique PUF readings predominantly come from the threshold voltage mismatch among these transistors. After powering up, the mutual input and output nodes, i.e., _Data_ and _DataN_, both reach a metastable state [52], at which time the outputs of two cross-coupled inverters are lingering between logical '0' and '1', then cross it and settle in one of these stable states. _NM4_ and _NM3_ are access transistors. They connect the internal nodes _Data_ and _DataN_ to bit lines _BL_ and _BLN_. In addition, two NMOS transistors _NM2_ and _NM5_ are designed to rapidly discharge the _Data_ and _DataN_ nodes during the reset stage with both _SUPn1_ and _SUPn2_ signals asserting. As a result, the reset time can be shortened by 5 orders of magnitude, i.e., from 120 microseconds to 1 nanosecond, hence facilitating high-rate PUF evaluation. Furthermore, no data remanence can be exploited by attackers [53; 54]. The PUF layouts were implemented symmetrically. During the evaluations, it is shown that a tiny bias of layout influences the proportion of the ones and  
Figure 4: Part of the power-gated PUF test circuit layout.  
zeros of the PUF readings. With two additional transistors, the area cost of the 8T PUF is 13.2% more than its 6T-SRAM PUF counterpart, which was implemented for comparison as shown in Figure 4.
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
###### Abstract
Various SAR images, including European remote sensing satellite (ERS), Radarsat, Envisat, Advanced Land Observing Satellite (ALOS), has been used in the mapping of wet snow in seasonal snow cover by using \(\sigma^{0}\), incidence angle, and polarization information [25; 26; 27; 28]. Various methods, such as visual interpretation, supervised classification, and machine learning algorithms, have been used in the classification of RGZs for the icefields at high latitudes and altitudes [22; 23; 24; 29; 30]. In the Antarctic and Greenland ice sheet, snowmelt occurs at low latitudes and altitudes frequently. The seasonal monitoring of glacier zones is important in order to understand the impact of meteorological parameters of the stability, energy, and mass balance of the ice sheets. Clustering algorithm and region classification based on backscatter and texture properties have been used in the mapping of RGZs on the ice sheets [16; 31]. In the AP, the pattern of surface snowmelt and melt ponds highlighted a potential contributing process to the collapses of ice shelves [32]. The boundaries between glacier zones were found to be climate-related in the AP. As the uppermost limit of summer snowmelt, the elevation of the DSL always indicates significant climate changes in the AP [8]. An upward significant shift of the DSL is believed to be related with the change in the snow cover properties under extreme melting conditions, while a downward shift of the DSL is a consequence of the continuous accumulation of dry and fine-grained snow and the absence of melt event for several years. During the last decade of 20 th century, a significant upward shift of the DSL in the AP was interpreted as a response to the increase of high temperature events [33]. DSL elevation in the AP was found to show good correlation with the summer air temperature [34]. Sentinel-1A and Sentinel-1B were launched in 2014 and 2016, respectively, and provide continuous imagery over the polar regions. With the two-satellite constellation, six-day exact repeat pass observations can be used for the monitoring of detailed patterns of snowmelt in the AP. We developed a practical method based on the simulations of a microwave scattering model to classify summer RGZs by using Sentinel-1 images in the AP (Section 3). We compare the summer snowmelt detected by the special sensor microwave/imager (SSM/I) and Sentinel-1. The SSM/I-derived melt area is used to validate the DSL mapped by winter Sentinel-1 mosaic. The mapping of summer RGZs and winter DSL in the AP are shown in Section 4, followed by the discussion and conclusions of this study in Sections 5 and 6.
## 2 Materials and Methods
### 3D Fall Detection
#### 2.4.2 Features-Based Fall State Classification
* Recovery state: This is in complete contrast to the fall state and represents the state when the upper body of a person rises from the ground and thus, the difference between the top or bottom from the current frame \(c_{k}\) to the frame \(nm_{k}\) in no-motion phase or \(f_{k}\) in fall state is the most significant feature. If the previous state is the emergency phase, then, the state converts to a recovery state if Equation (21) is satisfied. If the previous state is the fall state and the interval between the current frame and the fall state start frame is greater than or equal to t, then the state changes from the fall state to the recovery state under Equation (21). \[\left\{ \begin{array}{l}|p_{t}(c_{k})-p_{t}(nm_{k})|>\delta_{5}\\ |p_{b}(c_{k})-p_{b}(nm_{k})|>\delta_{5}\end{array} \right.\] (21) \[c_{k}-f_{k}>\tau\] (22) Here, \(\delta_{5}\) is our predefined threshold. With either condition satisfied, the body enters the recovery state from the previous state. All the states explained in the preceding sections are illustrated in Figure 7.  
Figure 6: Adaptive features for accurate state classification. Figure 7: State classification during the fall process.
## 3 Experiments and Analysis
### Rotational Speed Measurement Experiment  
The rotational speed of bottomhole power drilling tools in actual drilling operations does not exceed 1000 rpm. Therefore, the sensor's measurement range was defined as 0 to 1000 rpm, and the sensor's rotational speed measurement characteristics were tested within this range. The experimental results are shown in Figure 4. Figure 4a shows that the amplitude of the sensor's output voltage slightly decreases with increasing rotational speed. The triboelectric output voltage is theoretically only related to the friction contact area and the magnitude of the frictional force; therefore, the voltage should remain constant with increasing rotational speed. However, the arch ring bonded with Kapton is flexible. At higher rotational speeds, the recovery of the flexible material takes time, which may lead to insufficient friction contact, resulting in a slight reduction in the friction contact area and a slight decrease in output voltage. Figure 4b shows that the amplitude of the sensor's output current gradually increases with increasing rotational speed. This is because an increased rotational speed leads to a greater number of friction contacts per unit time, resulting in an increased accumulation of charge per unit time. Since current is directly proportional to the amount of charge, the current also increases.  
Both the voltage pulse and current pulse signals output by the sensor can be used for rotational speed measurement. However, as shown in Figure 4a,b, the amplitude of the voltage pulse signal is in volts (V), while the amplitude of the current pulse signal is in microamperes (\(\upmu\)A). The magnitude of the voltage signal is significantly larger than that of the current signal. Given the presence of noise interference in practical environments, and the significantly larger magnitude of the voltage signal, the voltage signal exhibits far superior noise immunity compared to the current signal. Therefore, the voltage pulse signal was selected as the sensor's rotational speed measurement signal. Subsequently, the output pulse frequency of the sensor at different rotational speeds was measured, and the results are shown in Figure 4c. This figure demonstrates a good linear relationship between the frequency of the sensor's output voltage signal and the rotational speed, with a linearity of 3.1%, indicating that the maximum deviation of the fitted curve from the ideal straight line accounts for 3.1% of the full scale. Further testing of the measurement error within the measurement range was conducted using 10,000 sets of data. The dataset consists of 10,000 sets of voltage measurement results at different rotational speeds. Each set contains 11 data points corresponding to different rotational speeds. The resulting measurement error curve is shown in Figure 4d, indicating that the sensor's measurement error is less than 3.2%, satisfying the requirements for downhole applications.  
Figure 3: Experimental setup. (**a**) Schematic diagram of the experimental setup and (**b**) photograph of the experimental setup.
## 2 Materials and Methods
### Material Characterization
#### 2.5.5 Barrier Measurements  
Water vapor permeability (WVP) of the films was determined according to ASTM E96/E96M (ASTM, 2005) gravimetric methodology following the modifications proposed by [PERSON] [39]. Film samples (\(\mathcal{O}\) = 3.5 cm) were placed and sealed in Payne permeability cups filled with 5 mL of distilled water (100% RH). Then, the cups were placed into desiccators containing Mg(NO\({}_{3}\))\({}_{2}\) over-saturated solution (53% RH) and weighed periodically (ME36S, Sartorius, \(\pm\) 0.00001 g, Fisher Scientific, Hampton, NH, USA) every 1.5 h for 25 h. For each treatment, the WVP was calculated considering the water vapor transmission rate (WVTR), which was determined from the slope of the weight loss vs. time. The measurements were performed in triplicate.  
Oxygen permeability (OP) of the films was determined using an Ox-Tran equipment (Model 1/50, Mocon, Minneapolis, MN, USA) at 25 \({}^{\circ}\)C and 53% of RH, according to ASTM D3985-05 (ASTM, 2010). The used area of films was 50 cm\({}^{2}\) and the oxygen transmission rate (OTR) was obtained every 15 min until equilibrium was reached. The measurements were carried out in triplicate.
## 2 Materials and Methods
### Methodology: Isolation and Phenotypic Characterization of Escherichia coli
#### 2.5.4 Phenotypic Assessment Detection of Lipase, Caseinase, Lecithinase, and Amylase  
The isolates were screened to assess the secretion of various virulence markers, including lipase, caseinase, lecithinase, and amylase enzymes, using a culture-based agar plate assay. These virulence factors, when secreted, are known to play roles in host tissue invasion through direct damage, complement system inactivation, cytotoxicity, and other mechanisms. Overnight cultures of each isolate were activated and streaked onto agar media containing specific substrates suitable for each enzyme activity.  
The lipolytic activity (lipase production) of _E. coli_ isolates was assessed using Tween 80-agar media (composed of 15 mL/L Tween 80, 5 g/L tryptone, 2.5 g/L yeast extract, 5 g/L NaCl, and 20 g/L agar; pH 7.2 \(\pm\) 0.2). The plates were spot inoculated with the isolates and incubated aerobically for 24-48 h at 37 \({}^{\circ}\)C. The presence of clear zones (halos) around the colonies, following staining with methyl red solution (0.2 g/L methyl red in 95% ethanol), indicated the presence of lipolytic activity (lipase production) [46].  
The production of caseinase from the isolates was detected using Skimmed Milk Agar (SMA, HiMedia Laboratories GmbH, Modautal, Germany). The isolates were streaked onto SMA plates and then incubated aerobically for 24 h at 37 \({}^{\circ}\)C. The presence of transparent zones around the colonies indicated caseinase production [42].  
To assess lecithinase activity, isolates were cultured on an Egg Yolk Agar base (HiMedia Laboratories GmbH, Modautal, Germany) supplemented with 2.5% yolk. Incubation was conducted for 18-48 h initially, with further incubation for up to 7 days to account for potential delayed lipase activity. The plates were then examined for the presence of opaque zones around the colonies to confirm lecithinase production [42].  
For amylolytic enzymes, the _E. coli_ isolates were streaked onto Starch Agar media plates containing 10 g/L soluble starch, 5 g/L tryptone, 3 g/L yeast extract, and 20 g/L agar at pH 7.2 \(\pm\) 0.2. The plates were then incubated aerobically for 24-48 h at 37 \({}^{\circ}\)C. Following incubation, the plates were flooded with potassium iodide solution and allowed to react for 10 min. The presence of transparent zones surrounding the colonies indicated amylase production [47; 48].
# Experimental Study on the Soil-Structure Responses Induced by Tunnelling in Limited Space
## 2 Project Description and Geological Condition
### Geological Condition  
Shanghai is located on the east side of China and the bedrock in Shanghai lies under thick Quaternary sediments. Hence, the main strata in Shanghai are soft silty clay and sandy clay [33]. Prior to the excavation of the tunnel, comprehensive in situ tests and laboratory experiments (e.g., cone penetration tests, boreholes, triaxial tests and oedometer tests) were conducted to investigate the surrounding and subsurface soil properties across the site. The typical soil profile along with the measured soil properties are illustrated in Figure 2. As shown in Figures 1 and 2, the uppermost layer is the artificial fill (2.6 m). Next, a firm silty clay (3.4 m) lies beneath this. Under the firm silty clay, a very soft muddy clay extends to a depth of 21.8 m BGS. The fourth layer is clay and silty clay (8.5 m). The third and fourth strata contain organics, humus and sludge which could cause significant reduction of soil strength. Through comprehensive laboratory tests, these two strata were shown to feature low strength, high compressibility and large water content. As the aforementioned two strata are susceptible to disturbance, these weak soils could pose a great threat to the safety of the project. Below the clay and silty layer, a dense silty sand layer with a thickness of about 34 m was encountered. Directly beneath the dense silty sand is very dense silty sand which extends to the termination depth of in situ tests at 80 m BGS. The level of the long-term phreatic water is 0.5 to 1.7 m BGS which fluctuates slightly with seasonal changes. The confined aquifer with the piezometric head of 3.8 m BGS is located at 33-80 m BGS. Generally, the sensitive thick soft soils above the sand strata in Shanghai were characterized as poor (e.g., large void ratio, low undrained shear strength). According to the geotechnical parameters shown in Figure 2, the Shanghai soft clay will deform drastically and fail quickly once it is subjected to disturbances. In addition, the soil properties gradually become better as the depth increases. More detailed geological and hydrological conditions in Shanghai can be found in [PERSON] et al. (2019) [34] and [PERSON] et al. (2015) [35]. From Figures 1 and 2, it can be seen that the new tunnel mainly passed through the very soft muddy clay which featured weak properties. Additionally, both the underpass and the existing diaphragm wall were located in the upper 30 m of thick soft soil. Considering the challenging field geological conditions and the congested space for tunnelling, the challenge and uncertainty of the construction was magnified significantly.  
Figure 1: Typical cross-section view of the project.