esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the 1950s, Chinese coal mining enterprises first carried out experiments and application of gob-side roadway forming techniques in thin coal seams. Gob-side roadway forming stayed at a low level during that period due to backward coal mine support materials and techniques, as well as low degree of mechanization. From the 1960s to the 1970s, the gob-side roadway forming technique for small coal pillars developed rapidly [26]. Pillars were reduced from 20 m to 2-3 m and were applied in many mining areas across China [27; 28]. In the 1980s, with the development of backfill materials and backfill-related techniques and equipment, roadway-side-filled pillarless mining was more widely used, and the new technique of backfilling the goat-side entry retaining for next sublevel was put forward. Traditional roadway-side-filled gob-side roadway forming uses roadside backfills such as concrete walls and gangue bags to protect the gob-side mining roadway [29; 30]. But when the roof rock is soft, roof falling and net pocketing often occur in the roadway. Soft rock roof is an incurable \"cancer\" of roadway-side-filled pillarless mining. In recent years, with the gradual increase of mining intensity in China, the shallow coal resources have been gradually depleted. The state vigorously advocates green and efficient mining. Recently, the continuous development of supporting materials and supporting theories and techniques has pushed the theoretical research and field application of gob-side roadway forming into a new stage of development [31; 32; 33]. Academician [PERSON] proposed a roof cutting pressure relief method for pillarless mining. The core of this technique is to cut off the connection between the partial roof of the gob and the roof of the roadway by directional roof cutting so that the roof in a certain range above the roadway forms a short arm beam structure [34; 35; 36]. However, where the soft rock roof is unstable, easy to break, and falls off upon excavation, and without advance roof cutting to relieve the pressure, drill sticking and caving can often arise, which ultimately compromises the roadway forming quality and limits the development of pillarless mining. The problem of pillarless mining under soft rock roof conditions needs to be solved urgently. Figure 2: Bearing pressure distribution diagram of working face. Based on Academician [PERSON]'s \"Practical Mine Pressure Theory System Centered on the Movement of Overlying Rock\" [37; 38; 39], after field investigation, theoretical analysis and industrial tests using the mining roadway of Xinyi Coal Mine as the research object, we present a gob-side caving roadway forming (GSCRF) technique under soft rock roof conditions. Compared with the roof cutting technique, the process of this technique is simpler, and can well adapt to soft rock roof conditions. We also establish a mechanical model of GSCRF and the associated support scheme. Field experiments show that the new technique is very effective, inexpensive, quick and well able to maintain the stability of the mining roadway. It solves the problem of pillarless coal mining under soft rock roof conditions.
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 3 Methodology  
Based on the previous research, a simple prediction method for estimating the initial stiffness of the flat slab in wall-slab joints under earthquake loadings is needed for frame analysis of the TWTS structural system. In this work, an effective beam model based on the EBWM concept is developed for the initial stiffness considering the load equilibrium and deformation coordination characteristics at the wall-slab joints in TWTS structures and mainly includes:  
* Basic assumptions are made for the calculation diagram of wall-slab joints.
* Express the initial stiffness of the flat slab using a lateral input load representing the earthquake input excitations.
* Describe the initial stiffness by considering the proportion of the flat slab as an effective beam using its width.
* Obtain the effective beam width model by equalling these equivalent stiffness presented by two expressions.  
In the processed model, a critical coefficient, the lateral input load at the initial stage (1/2000 rad deformation angle or 0.05% drift ratio according to the current Japanese code), has to be acquired. To solve this, actual experimental data obtained from reinforced concrete wall-slab joint specimens from the literature is employed for providing an estimation of the requested lateral input load. The detailed information is as follows.  
* A criteria value of the lateral load over the bearing wall cross-section is defined based on actual experimental data from the literature.
* Establish the relationship between a target lateral input load and the criteria value.
* Estimate the target lateral input load using the criteria value.  
Based on these considerations, the effective beam width model can thus be established. Additionally, the effective beam width prediction method employed in the current Japanese codes [2; 3] is as well calculated and compared with the actual values, which can be used to demonstrate the better effectiveness and accuracy of the proposed EBWM model for the effective beam width for the initial stiffness.
## Materials and Methods
### Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR) Analysis  
Cells (\(1\times 10^{6}\) cell/well) were grown to \(>\)90% confluence prior to UVA irradiation. Following UVA irradiation cells were treated with or without (2'S)-columbianetin for 24 h. Total RNA was isolated from nonirradiated and irradiated (UVA, 10 J/cm\({}^{2}\)) HDFs using AccuPrep(r) Universal RNA Extraction Kit (Bioneer, Daejeon, Korea) according to manufacturer's instructions. Total RNA was treated with RNase-free DNase I (Thermo Fisher Scientific, Rockford, IL, USA). The cDNA synthesis from total RNA was performed using CellScript All-in-One cDNA synthesis Master Mix (CellSafe, Yongin, Korea) following the producer's directions. cDNA amplification was carried out with quantitative PCR in a Thermal Cycler Dice(r) Real Time System TP800 (Takara Bio Inc., Ohtsu, Japan) following the manufacturer's protocol. Briefly 1.0 L of cDNA sample and 10.5 L of Luna Universal qPCR Mix (New England Biolabs, Ipswich, MA, USA) were mixed with forward and reverse primers in nuclease-free water. The target cDNA was amplified using following forward and reverse primers; MMP-1, forward 5'-TCT GAC GTT GAT CCC AGA GAG CAG-3', reverse 5'-CAG GGT GAC ACC AGT GAC TGC AC-3'; type I procollagen, forward 5'-CTC GAG GTG GAC ACC CTC-3', reverse 5'-CAG CTG GAT GGC CAC ATC GG-3'; \(\beta\)-actin, forward 5'-AGA TCA AGA TCA TTG CTC CTC CTC CTG-3', reverse 5'-CAA GAA AGG GTG TAA CGC AAC TAA G-3'. The PCR amplification was carried out with an initial denaturation at 95 C for 1 min, followed by 40 PCR cycles, each cycle consisting of 95 C for 15 s and 60 C for 30 s. Relative quantification was calculated using the 2-(\(\Delta\)ACT) method. \(\beta\)-Actin was used as an internal control.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Molybdenum Sulfide Films  
The molybdenum sulfide (MoS2) is known as transition metal dichalcogenide. This material has very unique properties, such as a tunable band gap value and flexible mechanical behaviors. MoS2 thin films were deposited on various substrates (a glass slide, FTO, and silicon (111) wafer) by using sodium sulfide [87] and ammonium molybate ((NH4)2 MoO4). Research findings show that these films have appropriate band gaps (1.3 eV) and excellent absorption coefficient values (2.8 \(\times\) 10\({}^{6}\) cm\({}^{-1}\)).
## 3 Results and Discussion
### Focused Optical Vortex Generation
The FWHM of horizontal cuts of vortex rings and focusing efficiencies are calculated under a different NA in Figure 5. The radius of all the designed metasurfaces is 6 \(\upmu\)m, and the topological charge \(l\) is kept at 1. It can be seen that, as NA increases, both FWHM and focusing efficiency are decreased, which can also be attributed to the decrease in the vortex ring's diameter. All the metasurfaces were diffraction limited (FWHM \(<\lambda\)/(2 NA)). The maximum value of NA reaches 0.788, and the maximum of focusing efficiency is measured to be as high as 79.2%. As far as we are concerned, the focusing efficiency seems to be promising compared to prior work [37; 38; 39; 40]. Figure 3: (**a**) Sketch of a transmission-type focused optical vortex (FOV) generator under the RCP incidence. The LCP transmissive wave with the desired topological charge can be observed with a donut-shaped ring in the middle of the focal plane. (**b**) Demonstration of numerical aperture (NA) calculation for the FOV generator. Figure 4: Numerically simulated results for a focused optical vortex (FOV) generator. Electric field intensity profile in \(xz\)-plane for (**a**) \(l=1\), (**b**) \(l=2\), and (**c**) \(l=3\). The white dashed line denotes the focal plane. The vortex rings are imaged at the focal plane, \(\mathrm{z=10~{}\upmu m}\), along with their phase pattern in the corresponding bottom-left inset figure for (**d**) \(l=1\), (**e**) \(l=2\), (**f**) \(l=3\) in \(xy\)-plane, (**a**,**d**), (**b**,**e**), and (**c**,**f**) share the same color bars, respectively. The upper-right inset maps show the amplification of the vortex ring. (**g**–**i**) The corresponding horizontal cuts of vortex rings at the focal spot having a full width half maximum (FWHM) of 311 nm, 335 nm, and 355 nm, respectively. Figure 5: The FWHM of horizontal cuts of vortex rings and focusing efficiencies versus NA of the FOV generators under an RCP wave incidence of 355 nm. The radius of the metasurface is kept at 6 \(\upmu\)m and the topological charge \(l\) of the OV is kept at 1.
## 4 Establishment of Double-Coupled Duffing Weak Signal Detection System  
### Variance Discriminant Method for Response State of Double-Coupled Duffing System  
It can be seen from the response of the above double-coupled Duffing system under the action of different amplitude built-in forces that its phase diagram and time domain diagram are very complex, and it is difficult to determine the judgment criteria for different states and critical points. According to the distribution characteristics of phase diagram trajectories in different states of the double-coupled Duffing system, this paper proposes to use the variance of the output phase trajectories of the double-coupled Duffing system as the basis for system state determination. The variance definition and calculation equation of the output phase trajectory value of the double-coupled Duffing system is as follows:  
\[D_{output}=\frac{\sum_{i=1}^{M}\left(\left(x_{1,i}-\overline{x_{1}}\right)^{ 2}+\left(x_{2,i}-\overline{x_{2}}\right)^{2}\right)}{M} \tag{3}\]  
Figure 2: Phase plane diagram and domain diagram of Duffing system. (**a**) \(F=0.2\), homoclinic orbit; (**b**) \(F=0.5\), periodic bifurcation; (**c**) \(F=0.8\), chaotic state; (**d**) \(F=1\), large-scale periodic state.  
where \(i\) is the number of sampling points for the output phase trajectory, \(x_{1,i}\) and \(x_{2,i}\) are the displacement and velocity output values of the first Duffing system at the ith sampling time, and \(\overline{x_{1}}\), \(\overline{x_{2}}\) are the mean values of displacement and velocity output values of the first Duffing system, respectively.  
Figure 3 shows the variation in the variance output value with the amplitude of the built-in force. The red dashed line in the figure represents the critical point between different states. When a double-coupled Duffing system enters a large periodic state from a chaotic state, the phase trajectory variance usually jumps significantly. According to this change feature of variance, this paper defines the following variance jump rate to determine whether the system state jumps:  
\[R=\frac{s_{t}^{2}-s_{2}^{2}}{s_{1}^{2}-s_{2}^{2}} \tag{4}\]  
where \(R\) is the variance jump rate, \(s_{1}^{2}\) is the variance of the output phase trajectory after the system enters the large periodic state, \(s_{2}^{2}\) is the variance of the system at the chaotic threshold (the critical point of the chaotic state and the large-scale periodic state), and \(s_{t}^{2}\) is the variance of the output phase trajectory of the system excited by the signal to be measured.  
When the variance jump rate is greater than a certain value, it is considered that a state jump has occurred at this time. This value is called the variance jump rate threshold. The determination of the size of the variance jump rate threshold is the key to this decision method. If the threshold is set too high, the detection performance of the Duffing system will be reduced, and if the threshold is set too low, the robustness of the Duffing system detection will be reduced. After a lot of simulation tests, the variance jump rate threshold is set to 0.8.
## 3 Results and Analysis
### Temperature Rise and Thermal Deformation Patterns of DPRS
#### 3.1.2 Influence of Load at Different Ambient Temperatures  
Figure 11 illustrates the temperature variation of Temperature node #9 on the nut after the DPRS is subjected to axial loads of 10, 20, and 30 kN at ambient temperatures of 20 and 120 \({}^{\circ}\)C when the screw speed is 3000 rpm. The temperature at Temperature node #9 on the nut increases gradually with increasing load at a given ambient temperature. This is because as the axial load becomes greater, the long and short semi-axes of the contact ellipse corresponding to the DPRS contact surface increase, the contact thermal resistance increases. Therefore, the node temperature increases with the increase in axial load. Comparison of Figure 11a,b shows that, as the ambient temperature increases, the amount of variation in the steady-state temperature of the nodes under different loads decreases. The difference in the steady state temperature between Node #9 at 30 and 20 kN load decreases by 0.9 \({}^{\circ}\)C as the ambient temperature increases from 20 to 120 \({}^{\circ}\)C.  
The results show that the axial load on the DPRS is positively correlated with the steady-state temperature of the part, and that the influence of the load variation on the steady-state temperature of the part is somewhat diminished as the ambient temperature increases.  
Figure 10: Thermal deformation of the screw at different temperatures.  
Figure 9: Temperature rise at each DPRS temperature node in different temperature fields: (**a**) 20 \({}^{\circ}\)C; (**b**) 130 \({}^{\circ}\)C.
## 2 Materials and Methods
### Study Site and Sampling
#### 2.1.2 BVOCs Sampling  
Branches were found to be actively photosynthesizing before starting BVOC measurements. A combination of the static confinement technique [17] with a constant air flow exposed to an adsorbent fiber extraction technique [18] were used. The selected branch was introduced gently into the chamber to avoid, within reasonable limits, plant stress; photosynthetically active solar radiation (PAR), temperature and relative humidity sensors were installed inside the chamber (Figure 1c). Then, a pump with an airflow of 100 mL/min was made to pass through a solid phase microextraction fiber. The extraction medium is a solid phase microextraction fiber with 65 \(\upmu\)m polydimethylsiloxane/divinylbenzene (PDMS/DVB) as coating [19]. A fiber holder was used to avoid interferences during the procedure. After air sampling, both the branch and the connection were removed and the fiber was stored in an insulating bag at 5 \({}^{\circ}\)C until chemically analyzed. The same procedure was repeated four times a day: at 11:00 a.m., 1:00 p.m., 3:00 p.m. and 5:00 p.m. over 5 days. After the fifth day, the branch was cut and dried in an oven at 80 \({}^{\circ}\)C for 72 h and then the dry mass recorded.  
Figure 1: Setup for sampling gases emitted by _Platanus \(\times\) acerifolia_ and _Schinus mole._ (**a**) CO\({}_{2}\) fluxes using an Infrared Gas Analyzer (IRGA) followed by 5 min gas accumulation inside the chamber to extract a gas aliquote to determine methane concentration; (**b**) chamber covered with a black cloth to record autotrophic respiration; (**c**) Semi-closed-chamber for BVOCs sampling with sensors simultaneously recording solar radiation, temperature and relative humidity.
## 5 Conclusions  
This article is an extension of work on a novel technique presented for PAPR reduction of OFDM based signals at the 2019 Wireless-Days Conference in Manchester [24]. A more than 4 dB PAPR reduction at CCDF of \(10^{-4}\) or \(0.0001\), while the EVM performance degraded by less than 1%, was demonstrated. In order to explore the practicality of the PSI technique, more evaluations and tests have been performed. The CCDF analysis was tested using LTE signals with IFFT lengths of 4096 and 8192 (in the case of an aggregated signal), with bandwidths of 40 MHz and beyond. Furthermore, the PSI technique was tested with a Doherty PA and a FPGA evaluation board. It was seen that the DPD is able to operate almost in same way as it does without PSI; however, with PSI applied, the PA has more chance to operate in higher power region without experiencing non-linearity. It was observed from experimental tests that the back-off is improved by about 1 dB. Studies showed similar performance for \(16 QAM\) and \(64 QAM\) modulation schemes. For future work, generating co-sim block and compiling the algorithm directly on FPGA is recommended; this will indicate the exact amount of hardware resources required for implementing the proposed technique in hardware. It is expected that by optimising all the parameters of PSI algorithm, the output power value achievable can be increased further in future work. Moreover, bit error rate (BER) and symbol error rate (SER) can be investigated in future works.  
**Author Contributions:** Conceptualization, S.M., R.F., D.M., and J.D.; Data curation, S.M., D.M., and J.D.; Funding acquisition, R.F.; Investigation, R.F.; Methodology, S.M.; Resources, R.F., and J.D.; Supervision, R.F., D.M., and J.D.; Validation, S.M., and J.D.; Writing--original draft, S.M.; Review, and editing, S.M., D.M., and J.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and was co-funded under the European Regional Development Fund under grant number \(13/RC/2077\).  
Figure 18: (**a**) Spectrum of PA output signal with and without DPD, and PSI technique (FPGA test). (**b**) PA AMAM characteristic with and without DPD, and PSI technique (FPGA test).  
**Acknowledgments:** The help and support of [PERSON] and [PERSON], are acknowledged and appreciated.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Edge and Cloud Computing in Smart Cities
## 3 Enabling Technologies
### Edge Virtualization and Resource Management  
Virtualization technologies are essential for managing computational resources efficiently in edge-cloud environments. By enabling multiple applications to share processing infrastructure dynamically, virtualization enhances scalability, flexibility, and cost-effectiveness.  
One of the key benefits of virtualization is containerization, which allows applications to run in isolated environments with minimal overhead. Containers provide a lightweight alternative to virtual machines (VMs), reducing the complexity of deploying and managing workloads at the edge. This approach is widely used in microservice-based architectures, where applications are broken down into smaller, modular components [99; 100; 101].  
Another important aspect of resource management is dynamic workload scaling. Edge-cloud systems must adjust computational resources based on real-time demand to maintain optimal performance. When the workload increases, additional virtual instances can be deployed to handle the demand. Conversely, during low-traffic periods, resources can be deallocated to save energy [102].  
To further enhance efficiency, energy-aware resource management strategies are implemented. These include techniques such as dynamic voltage and frequency scaling (DVFS), which adjusts processing power based on workload intensity to reduce energy consumption. By optimizing power usage, edge-cloud systems can achieve sustainability without compromising performance [103].  
Effective virtualization and intelligent resource management enable seamless workload distribution, energy-efficient computing, and adaptive service provisioning, making them fundamental for large-scale edge-cloud infrastructures in smart cities [104; 105].
## 4 Spatial Knowledge
### Atomic Gesture Identifier
#### 4.4.3 Correlation Coefficient  
The Pearson product-moment correlation coefficient is computed to evaluate the representativity of the direction inferred on the data. It is used in combination with the linear regressions explained in Section 4. In our algorithm, the correlation coefficient is crucial for the segmentation of the atomic gestures. It is actually necessary to compare the found gestures when trying to combine the results of recursive calls. It is also used to plot the current dataset to the appropriate qualitative direction. The coefficient, denoted by \(\varphi\), is evaluated using Equation (1).  
\[\frac{\left(n(\sum_{i=0}^{n}x_{i}y_{i})-(\sum_{i=0}^{n}x_{i})(\sum_{i=0}^{n}y_{ i})\right)}{\sqrt{n\big{(}\sum_{i=0}^{n}x_{i}^{2}\big{)}-\big{(}\sum_{i=0}^{n}x_{i} \big{)}^{2}}\times\sqrt{n\big{(}\sum_{i=0}^{n}y_{i}^{2}\big{)}-\big{(}\sum_{i=0 }^{n}y_{i}\big{)}^{2}}} \tag{1}\]  
The value of \(\varphi\) is comprised between \(-1\) and \(+1\) based on the degree of correlation. A value far from zero expresses a better correlation between the direction and the data. However, a threshold still needs to be set to make a decision. In our experiments, the threshold was set empirically by generating small sets of data points with representative noise with a stationary object. The idle objects usually returned a value of \(\varphi<0.5\).
## 1 Introduction
In case of navigating pedestrians, especially blind ones, the target accuracy should ideally not exceed the pavement width, _i.e._, _ca._ 2 m.  
This work presents a navigation scheme using GPS readouts, digital maps, inertial sensors and stereovision images with an aim of navigating a blind pedestrian. An accelerometer is used to detect pedestrian's strides and estimate their length. A gyroscope serves for estimating the heading direction. Those data sources help eliminate gross positioning errors and outliers. The digital maps are used twofold. Firstly, the so-called probability map is built to eliminate improbable user transitions, like traversing water ponds, crossing walls and buildings, _etc_. Secondly, a 3D model of the environment is built. The model is compared to stereoscopic images recorded by a mobile stereo camera. Interestingly, this comparison of 3D geometry of the environment provides good positioning accuracy in the surrounding of buildings, where GPS readouts are compromised. The proposed scheme employs the particle filter, also known as a sequential Monte Carlo method.