esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Shear Mechanical Properties of Pile-Soil Interface Under Freezing Conditions
## 4 Analysis of Test Results
### Variation in Strength Parameters of the Pile-Soil Interface  
In the process of pile pressing, the shear strains of the pile at different positions were measured by the strain gauges, and the interface shear stresses at the corresponding positions were further calculated. Simultaneously, the normal pressure on the side surface of the pile at different positions, under different loading levels, was obtained through the micro-earth pressure box embedded in the pile. Then, the peak interface shear stress at different positions along the pile was approximately regarded as the interface shear strength, and the parameters were analyzed in combination with the average normal pressures. The results demonstrate that the interface shear strength at different positions along the pile has a good linear relationship with the average normal pressure, which can be described well by using the Mohr-Coulomb criterion. The variation characteristics of interface strength parameters at different positions along the pile are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measured Locations** & **T = \(-2~{}^{\circ}\)C, \(\omega\) = 15\%** & **T = \(-5~{}^{\circ}\)C, \(\omega\) = 15\%** & **T = \(-2~{}^{\circ}\)C, \(\omega\) = 20\%** & **T = \(-5~{}^{\circ}\)C, \(\omega\) = 20\%** \\ \hline Depth\_2 cm & 65.17 & 145.88 & 121.62 & 178.75 \\ Depth\_6 cm & 51.55 & 102.93 & 59.30 & 121.71 \\ Depth\_10 cm & 27.90 & 84.23 & 55.47 & 84.96 \\ Depth\_14 cm & 26.15 & 55.00 & 23.18 & 37.04 \\ Depth\_18 cm & 7.65 & 16.62 & 4.99 & 19.69 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The interface shear strength under different test conditions (kPa).  
As shown in Figure 7, the interface strength parameters at different positions along the pile proved to have a decreasing tendency with the increase in the pile depth. The cohesion and internal friction angle of the interface at the upper part of the pile were greater than those at the lower part. Under the test condition of a \(-5\)\({}^{\circ}\) C temperature and 20% moisture content, the maximum interface strength parameters C and \(\varphi\) at the upper part were 64.8 kPa and 35.7\({}^{\circ}\), respectively, while these parameters at the lower part decreased to 11.5 kPa and 29.6\({}^{\circ}\). Under the same moisture content, the interface strength parameters increased with the decreasing temperature. Moreover, the interface strength parameters increased with the increasing moisture content while under the same temperature condition. Based on the comparison of the level of changes in the interface strength parameters with temperature and moisture content, it is concluded that the temperature has a more significant impact on interface strength parameters.
## 1 Introduction  
Tin(II) sulfide (SnS) is a binary main group metal chalcogenide semiconductor which favours a layered orthorhombic crystal structure [1], occurring naturally as the mineral _Herzenbergite_ (vide infra), which is structurally analogous to germanium sulfide (GeS) at room temperature [1, 2]. An orthorhombic, distorted NaCl-type (D\({}_{\text{2h}}\)[16]: Pnma) polymorph with lattice parameters of a = 4.32, b = 11.20 and c = 3.98 A is also known [3]. SnS layers are bound by weak van der Waals forces, which provide a chemically inert surface devoid of dangling bonds and surface density of states with no Fermi level pinning at the semiconductor surface. This provides SnS with a high level of environmental and chemical stability, even in acidic solutions [4].  
Tin(II) sulfide has emerged as an alternative solar absorber material to conventional thin film absorbers such as cadmium telluride (CdTe) and copper indium gallium selenide (CIGS), due to several properties including biological non-toxicity. Tin and sulfur are also both environmentally benign elements which are cheap and abundant in nature. SnS has an ideal bandgap for solar absorption (1.3 eV for bulk), a high absorbance coefficient >10\({}^{-4}\) cm\({}^{-1}\), Hall mobility up to 100 cm\({}^{2}\) Vs\({}^{-1}\) or higher, and tuneable carrier densities in the range of 10\({}^{15}\) to 10\({}^{18}\) cm\({}^{-3}\)[5, 6, 7]. Due to these exciting properties, it has been incorporated into thin film solar cell architecture alongside a range of electron and hole transporting materials. Theoretically, it has been predicted that the maximum power conversion efficiency (PCE) for SnS-based single junction thin film solar cells could reach a value of 32%; however, in practice, only 4.4% has been achieved so far [8]. Additionally, SnShas also been used as a substitute for platinum as a counter electrode (CE) in dye-sensitised solar cells (DSSCs) [9; 10]. Platinum (Pt) has been extensively used as a benchmark CE material due to its excellent catalytic activity, high electrical conductivity, and chemical stability [11]. However, due to the high cost, low natural abundance and the possibility of corrosion by iodide/triiodide (I\({}^{-}\)/I\({}_{3}\)\({}^{-}\)) redox electrolytes, large scale use of Pt is limited in this application. Several inorganic materials have been explored as replacements for Pt as CEs in dye sentised solar cells (DSSCs). Significant work has been focused on materials such as CoS [12], MoS\({}_{2}\)[13], WS\({}_{2}\)[14], NiS [15], and NiSe\({}_{2}\)[16]. Chalcogenides such as these have unique properties, such as material diversity, abundance, low cost, high catalytic activity and excellent electrical conductivity. However, the use of SnS is becoming more widespread and includes applications in photodetectors [17; 18], Li-ion and Na-ion batteries [19; 20; 21; 22], and sensors [23; 24], amongst others.
## 2 Background and Related Work
### WSN Deployment  
WSNs are very interesting because they can monitor and collect data in different environments and provide valuable real-time information for decision-making processes. WSNs are a fundamental part of the IIoT system because they are responsible for interconnecting the edge nodes (sensors, actuators, devices, and control systems) in what is known as a proximity network.  
One of the fundamental design problems of a WSN is the location of the sensors on the RoI, namely sensor or WSN deployment. The sensor's location may affect the system requirements and network performance metrics like coverage and connectivity. Careful placement of sensors along the tunnel can be a very effective means of optimization to achieve the desired design objectives [15]. The node sensor deployment can be classified into deterministic and random.
## 3 Results
### Validation of NEE  
The same analyses shown above for ET are presented for NEE with respect to the FLUXCOM reference data. NEE is defined as the total carbon exchange between the atmosphere and plants [9]. In the Noah-MP model, NEE is computed by summing plant respiration and soil respiration, and by then subtracting the Gross Primary Production. The depletion rate of oxygen and nitrates in the soil rate is influenced by soil water content and soil temperature.  
In Figure 5, GLASS LAI DA shows an overall improvement as per NIC of NCRMSE except for some parts of the western region, mostly characterized by shrublands (Figure 1). SMAP SM DA improves the estimation all over the study domain, except for some small regions in the southwest. The joint DA reflects the improvements and degradations from both the GLASS DA and SMAP DA.  
Both GLASS LAI DA and the joint DA improve the OL estimation of NEE, with larger anomaly correlation values (dark blue pixels) across CONUS (Figure 6). However, the SMAP DA has minimal impact on the NEE ACC, which is why the performance of the GLASS LAI and joint DAs results very similar.  
Figure 5: NIC of NCRMSE between DA and OL NEE with respect to FLUXCOM NEE from 2016 to 2018. Blue (red) color indicates improvement (degradation) after the application of DA.  
The NIC of NCRMSE for NEE shows a degradation in performance over shrublands for both GLASS LAI DA and the joint DA, with limited impact due to the SMAP DA. The NEE ACCs are smaller in shrubland regions compared to forests, woodlands, and croplands. Nonwoody semiarid ecosystems, e.g., shrubland and grassland, strongly respond to the availability of soil water [65]. However, the NEE estimated by the Noah-MP land surface model not only depends on soil water but also on the growth respiration of root, leaf, and wood. As LAI only provides an indicator of the greenness of the pixel, its assimilation in the model may not be enough to impact the estimation of NEE for small plants in semi-arid regions [66]. On the other hand, FLUXCOM uses greenness observation measures along with the measure of light that is emitted by pigments in the plants that are photosynthetically active (chlorophyll fluorescence), and then uses machine learning to merge the two datasets to estimate net ecosystem exchange [49; 65]. This is possibly a reason why the observations of NEE over shrublands are higher than the model output and why the DA performance is poor (i.e., NIC of NCRMSE is negative and ACC is very low) across these vegetation covers compared to forested areas and croplands.  
Figure 6: Difference in ACC between DA and OL NEE with respect to FLUXCOM NEE from 2016 to 2018. Blue (red) color indicates improvement (degradation) after the application of DA.
## 5 Noise-Corrected Clustered-Sparse Channel Estimation  
In this section, the parametric form of noise-resistant Bayesian channel estimation is given first. Then, the data detection and noise measurement are derived. Then, the noise-corrected clustered-sparse channel estimation algorithm is presented. At last, the complexity and performance analysis of this algorithm is given.
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry Analysis (GC-MS)  
Volatile emissions were identified by gas chromatography/mass spectrometry (GC/MS) coupled to a 5975B quadrupole mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with a DB-5 column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m). After sampling, the SPME fiber was introduced onto the GC injector in the splitless mode under the following conditions. The oven temperature program was set at 60 \({}^{\circ}\)C for 1.3 min and increased to 246 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min; the total time was 63.3 min. The carrier gas helium was set at 1.3 mL/min. The injector temperature was set at 220 \({}^{\circ}\)C and the detector temperature was 230 \({}^{\circ}\)C. Electron ionization mode (EI-MS; 70 eV) was used to acquire mass spectra in the 35-550 \(m/z\) at 2.8 scans/s. MassHunter software (B.07.06, Agilent Technologies, Santa Clara, CA, USA) was utilized to obtain the mass spectra and ion chromatogram. Volatile emissions of lychee plants were identified by a combination of 3 methods: (1) The individual peaks were identified by comparing their mass spectra with Wiley 12/National Institute of Standards and Technology (NIST) [56], MassFinder [57], Adams Library [58], Flavors and Fragrances of Natural and Synthetic Compounds 3 [59]; (2) in-house library \"SHRS Essential Oil Constituents-DB-5\" which was built up from authentic standards purchased from different sources, and components of known essential oils analyzed under the same conditions [60]; (3) to confirm the identification, retention index (RI) values calculated according to the RI van Den Dool and Kratz equation \(\mathrm{RI_{x}}=100_{\mathrm{n}}+100\) (\(\mathrm{t_{x}}-\mathrm{t_{n}}\))/(\(\mathrm{t_{n+1}}-\mathrm{t_{n}}\));t\({}_{n+1}\) and t\({}_{n}\) retention times of the reference \(n\)-alkane hydrocarbons eluting immediately before and after compound \"X\"; t\({}_{x}\) retention time of compound \"X\" [61] to a series of \(n\)-alkanes (C\({}_{g}\)-C\({}_{21}\), Sigma-Aldrich, St. Louis, MO, USA), and compared with their RIs available in the literature [58; 62; 63; 64]. The relative percentages were obtained by total ion current (TIC) peak areas.  
Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (ACH) for GC-MS data were performed using XLSTAT 2021 (Addinsoft, New York, NY, USA). Only constituents in a concentration higher than 0.5% were used as variables for the PCA and ACH analysis. Data used for the PCA and ACH analyses were a 21 \(\times\) 5 matrix (105 data) for samples A1 to A5, and 81 \(\times\) 4 matrix (112 data) for samples B1 to B4. [PERSON]'s correlation model was used for PCA. Euclidean distance for measure and [PERSON]'s method were used for ACH analysis.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 5 Comparative Analysis of Optical Encryption Methods
The security-enhancing capabilities of metasurfaces have been assessed [40], yet these capabilities create manufacturing hurdles and thermal control problems that would pose substantial obstacles for widespread implementation. Reprogrammable meta-holograms offer nearly unlimited capabilities in creating dynamic encryption channels. Tests conducted showed that the sensitivity of metasurfaces depends strongly on perfect calibration during their iterative alignment trials. The theoretical bandwidth capacity is high yet the actual implementation of 400 Gbps or higher data transmission rates exists only in initial experimental stages [40]. The research summary presented in Table 2 showcases the technical principles and strengths and weaknesses alongside the confirmed bandwidth capacity of the examined methods. The table underwent modification to follow CIA model principles by explaining how each strategy deals with confidentiality, integrity, and availability risks [31]. The research utilizes multiple reputable data sources that combine peer-reviewed research and vendor documentation and experimental network lab measures [33]. Several findings emerged from iterative scenario modeling [34]. First, methods that lack adaptability, such as traditional DRPE, are vulnerable to deep-learning-based analysis if the same phase mask is reused extensively. Second, purely classical approaches can excel at high data rates, but they risk eventual compromise if a sufficiently powerful quantum adversary emerges [14]. The combination of advanced approaches using MHCD or meta-holograms presents potential solutions to unite classical-quantum security standards, although the necessary infrastructure must resolve precision and operational problems [40].
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 5 Secure Multi-Party Computation Protocol Based on NFHE Scheme
### Security
#### 5.3.3 In Malicious Model  
Due to the SMC protocol under the CRS model, if the protocol is proven to be safe under the semi-malicious model, the protocol can be converted into a protocol under the malicious model by non-interactive zero-knowledge proofs (NIZKs) [21]. Therefore, the SMC protocol designed in this paper is also safe under the malicious model.
# Measurement of Lower Extremity Alignment Using a Smartphone Application
## 1 Introduction
### Lower Extremity Alignment Measurement  
#### 2.3.1 Measurement of the Lower Extremity Alignment on Radiographs  
For full-length radiography of the lower extremities with a weighted load, the patient's legs were spread out as wide as their shoulders. While standing, the patella was positioned in a forward direction and the feet were rotated, depending on the patient, to achieve proper positioning. After the right posture was achieved, a digital X-ray (XGEO GC80, Samsung) device was used for imaging. The radiographs were acquired from the center of the femoral head to the center of the knee to the center of the ankle using an angle measurement tool on an INFINITT PACS (G3 PACS version 3.011.3, BN104, Infinitt, Phillipsburg, NJ, USA) (Figure 2) [17; 18; 22].  
Figure 2: Hip–knee–ankle angle measurement. Hip–knee–ankle angle measurement, a method for measuring the lower extremity alignment on full-leg radiographic images. (1) Center of the femoral head; (2) center of the knee (midpoint between the tips of the tibial spine); (3) center of the ankle (superior facet of the talus). Lower extremity alignment is defined as the angle of the line connecting 1, 2, and 3.
## 3 Results
### Unprotected Loss of Flow (ULOF) Transient Accident Simulation  
The ULOF accident scenario can occur in an LFMSR when there is a sudden plant outage and the back-up generators fail. This scenario is modeled as a null transient since GeN-Foam does not currently support transient input. To simulate the following transient in GeN-Foam, the pump momentum source is immediately set to zero after the reactor system reaches steady-state conditions as described in the previous section. GeN-Foam iteratively couples the neutronics and thermal-hydraulics calculations. Unlike the steady-state simulations, the transient models do not have a constant applied power. The power distribution is calculated by the neutronics solver and is passed on to the thermal-hydraulics solver which uses the power distribution to calculate the temperature distribution.  
The two parameters of interest during the ULOF transient are velocity and temperature. It is crucial that the maximum temperature does not approach the boiling temperature (1673 K) of the fuel salt and that the system attains a non-zero natural circulation velocity. Upon initiating the transient, salt velocity decreases rapidly in the first 25 s, after which it stabilizes due to the establishment of buoyancy-driven flow.  
Figure 7: Precursor spatial distribution in the primary loop: Group 1 (**a**), Group 8 (**b**).  
A significant increase in temperature occurs in this transient which decreases the density of the fuel. Fuel-salt temperature is stabilized after the ULOF transient by negative temperature feedback effects. Parameterized cross-sections for 700 K and 1100 K were prepared for these simulations according to the GeN-Foam requirements. The spatial distribution of the velocity and the temperature at the end of the simulated transient (after 300 s) are shown in Figure 8a,b, respectively. The overall maximum velocity decreases as expected, and its contour changes considerably from the steady-state solution in Figure 5. During the ULOF transient, the momentum source slowly changes from the pump region to the core region. The predicted velocity magnitudes shown in Figure 8 seem large for natural circulation and may be due to the large core region that is contributing energy and therefore fluid momentum.  
Figure 8: Thermal hydraulic parameters: (**a**) temperature and (**b**) velocity after 300 s during a ULOF transient.
## 4 Verification on the Mesoscale Equivalent Model  
To further verify the developed model, numerical simulations on both uniaxial compression tests and penetration tests presented in Section 1 are conducted in LS-DYNA employing the mesoscale equivalent model. The numerical results are compared with corresponding experimental results, which show that the developed mesoscale equivalent model could well reproduce the behavior of UHPC under static and dynamic loadings.
## 3 Methodology
### Reinforcement Learning Setup
#### 3.2.4 DRL Network Architecture  
The DDPG network is an actor-critic, model-free algorithm that can be applied to continuous action spaces. The algorithm uses the actor network to output the continuous spatial action in the random process of the \(\pi\) strategy and uses the critic network to calculate the corresponding Q value for the action. Policy replay is performed with an experience replay buffer, from which data is drawn each training session. Both networks have a target network to reduce the impact of network parameter updates on learning stability. The soft update rules of the target networks are:  
\[\theta^{Q^{\prime}} \leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\,, \tag{8}\] \[\theta^{\mu^{\prime}} \leftarrow\tau\theta^{u}+(1-\tau)\theta^{\mu^{\prime}}\,,\]  
where \(\theta\) is the weight of the network, \(Q\) stands for the critic network, \(Q^{\prime}\) stands for the target critic network, \(\mu\) stands for the actor network, and \(\mu^{\prime}\) stands for the target actor network. \(\tau\) is the discount factor, which we set at 0.01 in training.  
We elaborate the network as shown in Figure 4. We use the multilayer feedforward neural network [40] to complete the mapping from the input to the output. Actor network maps observation data to actions. We design a 5-hidden-layer neural network as a non-linear function approximator to the policy \(\pi\). The first three layers are fully connected networks with 500 rectifier units and ReLU activation functions. Then the full connection outputs the rotational velocity through the Tanh activation function with range \((-1,1)\), and the normalized value \((0,1)\) of the translational velocity through the Sigmoid activation function. Critic network maps observation data and actions to Q-values. After the observation data passes through a fully connected layer, it is merged with the current action. After two fully connected layers, the Q value is output through linear activation.
## 2 VLSI ASIC Design Flow  
The ASIC design flow comprises several steps, from conceptualization to specification and tape-outs, as shown in Figure 1. The end product usually is small in the order of nanometers. The ASIC design flow starts with the specification definition of the system [30].  
The current emphasis of low-power VLSI design is on designing and implementing power-efficient algorithms of arithmetic units such as adders, subtractors, multipliers, and dividers. Due to the rapid growth of wireless devices in the electronics market, total power consumption, a core design constraint, must be addressed. The efficient design of an Integrated Circuit (IC), considering low power, high speed, and small area, is quite challenging [31]. Since maximizing the run time of these portable devices is desirable, reducing power consumption becomes a critical factor that requires careful consideration [32].  
The most demanding constraint when it comes to the design of an efficient, high-performance system in nanometer technologies is power dissipation. ASICs, having optimized performance, offer better power efficiency than other implementation schemes. ASIC offers lower power solutions and higher speeds for the same processor technology due to its interconnect architecture compared to FPGA. Due to the limitations imposed by general-purpose processors regarding computational power and throughput, implementing image processing algorithms in real time is complex as it requires high computational power and throughput rate. ASICs are semiconductors that can be specially designed for a particular function. They can reduce silicon area with high efficiency in speed and power, making them suitable candidates for image processing applications.  
The efficient implementation of the different arithmetic circuits of the computational unit for executing distinct algorithms is realizable with ASIC. As the complexity of arithmetic circuits increases, so does the power consumption and transistor density, giving rise to the low-power design techniques of VLSI with state-of-the-art concepts. With the ever-growing popularity of battery-powered electronic devices, area, power, and delay from the perspective of a circuit play a significant part in VLSI. As the area increases, the number of logic gates also increases, slowing the circuit and increasing power consumption. A trade-off often exists among power, delay, and area in VLSI.  
The PDP is the design metric that gauges the effectiveness and performance of low-power devices. As the complexity of the devices increases, the power dissipation keeps rising, impacting the performance and reliability. In such a scenario, power consumption becomes an integral part of the design that requires the design constraints to be further optimized. The latest technological advancements have led researchers to work to design power-efficient, high-speed, and smaller devices with a focus on Power Delay Area (PDA) constraints.  
Figure 1: VLSI ASIC Design Flow.