esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Efficiency of Air Circulation in a Refrigerated Room  
The efficient air circulation in a refrigerated room saves the unnecessary waste of energy, thereby reducing carbon emissions and environmental damages [42]. To assess the efficiency of air circulation, the energy utilization coefficient (\(\eta\)) was selected as a studied parameter [43], where a higher \(\eta\) means a better efficiency. The calculation formula is:  
\[\eta=\frac{T_{h}-T_{0}}{T_{n}-T_{0}} \tag{12}\]  
As depicted in Figure 11, the four \(\eta\) curves are increasing first, and then decreasing time. The peak residence times of these \(\eta\) curves are 2 h, when all \(\eta\) go down and become gradually constant in time. The maximums of \(\eta\)at UPF-PSMs, UPT-PSMs and their corresponding NSMs are 1.60, 2.33, 1.87 and 2.00, respectively. During the long frozen storage (6 or more months) [1; 7; 8], it is important to achieve an \(\eta\), which is constant in time. After freezing completion, these four stacking modes are stabilised around different constant values of \(\eta\) (namely 0.86, 0.79, 0.91 and 0.89). For NSMs, these constant \(\eta\) values are larger than those for PSMs. Therefore, the application of NSMs provides a higher efficiency of the air circulation in the studied refrigerated room during frozen storage.  
The above-mentioned studies demonstrate that the freezing completion effect and the product temperature uniformity are superior when applying UPF-PSMs. Simultaneously, for long-term frozen storage, superior uniformity of flow distribution and efficiency of air circulation in the room can be achieved by means of NSMs. Thus, a comprehensive stacking mode can be proposed, which is applies UPF-PSMs in the freezing completion process, followed by NSMs during long-term frozen storage.
# FPGA Implementation of ECT Digital System for Imaging Conductive Materials
###### Abstract  
This paper presents the hardware implementation of a stand-alone Electrical Capacitance Tomography (ECT) system employing a Field Programmable Gate Array (FPGA). The image reconstruction algorithms of the ECT system demand intensive computation and fast processing of large number of measurements. The inner product of large vectors is the core of the majority of these algorithms. Therefore, a reconfigurable segmented parallel inner product architecture for the parallel matrix multiplication is proposed. In addition, hardware-software codesign targeting FPGA System-On-Chip (SoC) is applied to achieve high performance. The development of the hardware-software codesign is carried out via commercial tools to adjust the software algorithms and parameters of the system. The ECT system is used in this work to monitor the characteristic of the molten metal in the Lost Foam Casting (LFC) process. The hardware system consists of capacitive sensors, wireless nodes and FPGA module. The experimental results reveal high stability and accuracy when building the ECT system based on the FPGA architecture. The proposed system achieves high performance in terms of speed and small design density.  
capacitance measurements; electrical tomography; image reconstruction algorithm; LFC; FPGA +
Footnote †: journal:
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 5 Future Research Issues
### Energy Collection of UAVs  
Flying UAVs have limited energy for transmission, maneuver, control, data processing, and other purposes. As a result, UAV flight time is often short and insufficient to provide long-term continuous wireless network coverage [59; 60]. The UAV's energy consumption also depends on the operation mission, weather conditions and path conditions. This energy limitation limits the UAV's flight and hover time. Therefore, in the design of UAV communication system, it is necessary to pay attention to the effective energy collection mechanism to prolong the life of the network system.
## 3 Results
### Case Study  
Figure 8 presents the MAEs and RMSEs of precipitation forecasts for TC Ma-on by the GFS and PM models at various lead times, indicating that the PM model exhibits lower values for both metrics compared with the GFS model. The average cumulative precipitation for the PM model forecast, GFS model forecast, and GPM observation over 0-24 h are 12.02 mm, 10.87 mm, and 12.76 mm, respectively, indicating that the PM model effectively corrects the GFS model's precipitation underestimation within 0-24 h. Similarly, the average cumulative precipitation for the PM model forecast, GFS model forecast, and GPM observation over 0-48 h are 21.59 mm, 20.82 mm, and 21.88 mm, and over 0-72 h are 28.94 mm, 28.42 mm, and 28.83 mm, respectively. The PM model's average precipitation forecast aligns more closely with GPM observation compared with the GFS model.  
Additionally, we select a TC point where TC Ma-on caused the heaviest 0-24 accumulated precipitation before its landfall and analyze the spatial distribution pattern of forecasted and observed precipitation at that point. Figure 9 illustrates the TC precipitation forecasts by the PM and GFS models, along with the GPM precipitation observation, when TC Ma-on was located at 113 E, 20.5 N, causing heavy precipitation in Hainan province. The spatial correlations between the precipitation forecasts by the PM model and the GPM observations over 0-24 h, 0-48 h, and 0-72 h are 0.81, 0.80, and 0.78, respectively, higher than the spatial correlations between precipitation forecasts by the GFS model and the GPM observations, i.e., 0.71, 0.70, and 0.69, respectively. Precipitation forecasts by the PM model are more similar to the GPM precipitation observations in terms of precipitation distribution patterns, indicating an improvement over the GFS model in TC Ma-on precipitation forecasts.  
Figure 8: (**a**) RMSE and (**b**) MAE for precipitation prediction for TC Ma-on by different models.
## 7 System Performance Optimization
### Packet Loss Rate Calculation  
When channel interference is used to interfere with the attacker's obtained CSI measurements, the essence of channel interference is to make the attacker's receiver lose ICMP ping packets. However, how many packets does the attacker's receiver need to lose so that the attacker cannot recover the input sensitive information successfully? For public APs, in order not to influence the network service for normal users, the packet loss rate for the network service should be less than a threshold.
###### Abstract  
Weather forecasting is essential for various applications such as agriculture and transportation, and relies heavily on meteorological sequential data such as multivariate time series collected from weather stations. Traditional numerical weather prediction (NWP) methods applied to multivariate time series forecasting are grounded in statistical principles such as Autoregressive Integrated Moving Average (ARIMA); however, they often struggle with capturing complex nonlinear patterns among meteorological variables and temporal variances. Currently, existing deep learning approaches such as Recurrent Neural Networks (RNNs) and transformers offer remarkable performance in handling complex patterns among meteorological multivariate time series, yet frequently fail to maintain weather-specific physical properties such as strict values constraints, while also incurring the significant computational costs of large parameter scales. In this paper, we present a novel deep learning plug-and-play framework named Post Constraint and Correction (PCC) to address these challenges by incorporating additional constraints and corrections based on weather-specific properties such as multivariant correlations and physical-based strict value constraints into the prediction process. Our method demonstrates notable computational efficiency, delivering significant improvements over existing deep learning time series models and helping to achieve better performance with far fewer parameters. Extensive experiments demonstrate the effectiveness, efficiency, and robustness of our method, highlighting its potential for real-world applications.  
deep learning; multivariate time series forecasting; weather multivariate time series +
Footnote †: journal: Applied sciences
# Performance Evaluation of LoRa Considering Scenario Conditions
## 4 Material and Methods
In this case, the base-station was placed in the rooftop of the Technology Transfer Center in the Fuente Alamo Technology Park, a 4 -story building. The selected route traveled up to 20 km east from the location of the base-station through low transited roads. As aforementioned, prior to our experimental study, we have examined the expected signal level in the afore-described scenarios by using the well-known Okumura-Hata propagation model. To this end, an accurate radio planning tool including topographic maps and high grade of flexibility that permits introducing the characteristics of the employed equipment has been used [8]. The Okumura-Hata model is an extensively used propagation model for planning wide-area deployments. It covers a wide range of frequencies and types of services, so it is necessary to properly adjust the equipment features such as receivers sensitivity, antennas type, transmission power, carrier frequency, etc. (please, see Table 2).
# Cooperative Data Collection Mechanism Using Multiple Mobile Sinks in Wireless Sensor Networks
## 4 The Proposed CDCA Algorithm  
This section proposes the data collection mechanism which aims to partition the whole wireless sensor network into \(k\) disjoint sets and assign each set to a mobile sink \(m_{i}\) to collect the data generated in this set. For each set of sensors, the proposed mechanism will further determine the set of CPs and establish a path visiting all CPs while the lifetime of the sensors in this set can be maximized, under the constraints (6)-(9). The proposed data collection algorithm is composed of three major phases: Network Partition Phase, CP Selection and Path Construction Phase as well as Speed Control Phase. In the Network Partition Phase, the algorithm partitions the n sensor nodes into k disjoint subsets. Then the CP Selection and Path Construction Phase aim to select \(k_{i}\) CPs from the locations of sensors in \(T_{i}\) and establish a path \(\pi_{i}\) which visits the \(k_{i}\) CPs for data collection. In the _Speed Control Phase_, the algorithm coordinates to control the speed of mobile sinks \(m_{0}\) and \(m_{i}\). As a result, each mobile sink \(m_{i}\) can collect data generated by sensors in \(T_{i}\) while the mobile sink \(m_{0}\) further collect data from each mobile sink \(m_{i}\). The following gives the details of the three phases.
## 3 Numerical Results
### Sensitivity Analysis  
Before proceeding with the assimilation, it is important to evaluate the actual influence of the uncertain parameters on the model output. We compute the Sobol' indices using the gPCE-surrogate solution of the model output. The results are presented in Figure 3. First-order indices clearly show the higher impact on the total variance with a negligible influence of the factors interactions. In particular, parameters \(\lambda^{*}\) and \(R\) appear to have the major impact on the solution at several times, except for the first two years where \(\rho_{x}\) and \(\rho_{h}\) have a non-negligible effect. However, the significance of the quantity of interest, that is, vertical displacements, is quite limited at the beginning of the production period, because both the pressure variation and the geomechanical answer of the system are still very small and possibly influenced by the measurement errors. Thus, we limited our sensitivity analysis to the time interval between 3 and 10 years. In this temporal window, based on the total Sobol' indices \(S_{T,i}\) (Figure 3c,d) and setting \(\varepsilon_{S}=0.1\), we can remove from the parameter space the ratios \(\rho_{x}\) and \(\rho_{y}\). However, it is important to point out that further analysis may be needed in order to investigate the model sensitivity based on different metrics, for example, on the expected value, skewness and kurtosis [59; 60]. This may be helpful to better understand the role of each parameter, thus ensuring its exclusion from the parameter space is negligible.  
The new parameter space is defined by \(\mathbf{p}=(\lambda^{*},R)\) with \(n=2\). This allows to reduce significantly the computational effort and, for the further analysis, retain the gPCE order to \(N=3\) with a total number of high-dimensional quadrature nodes \(n_{q}=16\). The Sobol' indices for this new parameter space have the same behaviour as the one shown in Figure 3.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 2 Functional Effects of EGCG
### Stimulating Mineralisation and Enhancement of Bone Formation  
One of the main effects influencing bone structure is the stimulation of bone metabolism, leading to the enhancement of mineralisation. [PERSON] et al. [26] demonstrated in their study on the D1 cell line (a murine bone marrow mesenchymal stem cell line) that the administration of EGCG at a dose of 1 or 10 \(\upmu\)mol/L promotes mineralisation by increasing the osteogenic gene expressions and upregulation of ALP activity. In another study, [PERSON] et al. observed that treatment with 1-5 \(\upmu\)M of EGCG in SaOS-2 cells (human osteoblast (HOB)-like cells) led to an increased formation of mineralised bone nodules by enhancing osteoblast differentiation, along with a decrease in RUNX2 protein levels [48].  
The improvement in bone structure and the enhancement of bone formation following EGCG supplementation have also been observed in various studies. [PERSON] et al. demonstrated that it can modulate osteoblast cell function towards bone formation by inhibiting p44/p42 MAP kinase in cloned osteoblast-like MC3T3-E1 cells derived from newborn mouse calvaria [27]. Various bone defects and fractures can be alleviated by this compound. [PERSON] et al. observed in parietal bone rats that the combination of EGCG and \(\alpha\)-calcium triphosphate (whose derivative is hydroxyapatite, responsible for mechanical bone strength) reduced bone defects by inducing maximum bone regeneration [28]. EGCG itself enhanced the healing of bone defects by de novo bone formation, as noted by [PERSON] et al. [44]. One of the possibilities of facilitating the healing of bone fractures by EGCG may be its influence on enhancing bone callus formation. The study performed by [PERSON] et al. on the right tibial bones of rats indicated that it acts by increasing the expression of bone morphogenetic protein 2 (BMP-2), which subsequently enhances the bone mechanical properties [21]. [PERSON] et al. demonstrated in the in vivo study on the dorsal part of the rat cranium that EGCG can also be used to modify collagen membranes, which in turn induces bone formation [45]. They indicated that an increase in the number of macrophages (M2) promoted the secretion of the vascular endothelial growth factor (VEGF) and BMP-2 and upregulated the expression of RUNX-2 and osteopontin (OPN) after implantation of the EGCG-modified collagen membranes. EGCG was also found to be an anti-angiogenic agent. The study performed by [PERSON] et al. on rats indicated the reduction in the activity of ALP and calcium content, accompanied by an increase in the content of type II collagen, which enables the simultaneous inhibition of ectopic osteogenesis and the enhancement of chondrogenesis [49].
###### Abstract  
The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth's surface processes and geohazards. In this paper, the Parallel Small BASeline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trained to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014-2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up \(-2.3\) cm/year vertical and \(-1.0\) cm/year east-west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.  
SAR; radar interferometry; InSAR; SBAS; Sentinel-1; ground deformation; big data; high-performance computing; subsidence; landslides +
Footnote †: journal: Remote Sensing  
11 January 2021  
Accepted: 22 February 2021  
Published: 26 February 2021
# Analysis and Design of Tunable THz 1-D Leaky-Wave Antennas Based on Nematic Liquid Crystals
## 3 Optimization and Design
### Liquid-Crystal Switching  
Here, we provide some brief considerations regarding the LC biasing scheme and characteristics. As shown in Figure 2, the LC bias voltage is applied along the \(z\)-axis, i.e., between the PRS and the bottom PEC ground plane. At the lateral walls, the metallic patch array of the PMC will behave as a uniform conducting surface at the LC bias frequency, which is typically between 1 and 10 kHz. Hence, the PMC walls have to be electrically disconnected from the PRS/ground electrodes in order to avoid short-circuiting by introducing a small lateral gap, e.g., by slightly reducing the width \(d\).  
The electric breakdown strength for Zeonor is \(E_{\text{b}}>100\) V/\(\upmu\)m [69]; hence, a micron-sized gap provides sufficient protection of the material without notably perturbing the THz modal properties. Regarding potential fringe-field effects from the slotted PRS, the aspect ratio of the slot width over the waveguide height \(w/h\) is less than 10%, which is too low to induce detrimental effects [70] considering that the NLC layer is located in the middle of the waveguide.  
Regarding the operation bias voltage, it can be estimated by first considering the threshold voltage in a standard planar splay NLC cell, which is given by \(V_{\text{th}}=\pi\sqrt{K_{11}/\left(\varepsilon_{0}\Delta\chi_{e}\right)}\), where \(K_{11}\) is the NLC splay elastic constant, \(\varepsilon_{0}\) is the free-space permittivity, and \(\Delta\chi_{e}\) is the low-frequency NLC dielectric anisotropy. In particular, at 1 kHz, for the NLC mixture 1825, these parameters are \(K_{11}=12.5\) pN and \(\Delta\chi_{e}=17\)[35]; hence, \(V_{\text{th}}\simeq 0.9\) V. Given the NLC filling factor FF\(=0.2\) and the difference of the low frequency relative permittivities of the NLC and Zeonor, the threshold voltage for the LC switching in the proposed antenna is estimated as 10 V.  
Finally, the operation speed of the device is determined by the NLC response time as given by \(\tau_{\text{on}}+\tau_{\text{off}}\): \(\tau_{\text{on}}\) can be controlled by increasing the voltage, and generally \(\tau_{\text{on}}<<\tau_{\text{off}}\), unless operating close to the threshold voltage, which, however, provides limited tunability. Hence, the response time is ruled mainly by the switch-off relaxation process, \(\tau_{\text{off}}=\gamma_{1}h^{2}/\left(K_{11}\pi^{2}\right)\)[24], where \(\gamma_{1}=311\) mPa\(\cdot\)s is the NLC rotational viscosity [71]. The resulting value is approximately one second, and this can be further reduced by employing optimized low-viscosity and/or dual-frequency NLC mixtures.
## 2 Literature Review
### Application of Kernel Density Estimation Method  
Kernel density estimation (KDE) is widely applied in spatiotemporal behavior analysis due to its adaptability and robustness to noise in datasets and is often used in the fields of transportation planning and logistics path planning [30; 31; 32; 33]. KDE's capacity to manage multi-dimensional data and its non-parametric nature make it an ideal tool for standardizing data and identifying patterns in user travel behavior [34; 35; 36]. Our research applies kernel density estimation (KDE) to transform time-space distances, weight, volume restrictions, and urgency into density values, which are then utilized to identify regular activity hotspots and optimize parcel locker (PLC) selection.  
In summary, the literature highlights the need for innovative solutions in last-mile logistics that consider both user convenience and operational efficiency. Our study contributes to this field by incorporating user spatiotemporal behavior with kernel density estimation (KDE) to optimize parcel locker selection, addressing a gap in the current literature and presenting a practical approach to enhancing last-mile logistics.
# The Extended Information Systems Success Measurement Model: e-Learning Perspective
## 4 Methods and Materials  
### Research Instrument  
This research followed a quantitative approach, being implemented through the survey method.  
According to the analyzed theoretical models and reviewing the relevant literature sources, the authors operationalized nine factors (6 representing IS success and 3 representing workforce agility) with 48 manifest variables in the final questionnaire. Per guidelines for theoretical models, 39 out of 48 manifest variables were grounded, and depending on the specific functionalities of the IS, the rest varied. Given that the use behavior factor depends entirely on the functionality of the IS under analysis, nine variables representing this factor were identified to examine the IS success in the teaching process for this research. The final model with factors and associated manifest variables is presented in Table 2.  
This research used a self-reporting (subjective) assessment of IS success and workforce agility as perceived by respondents. To capture respondents' subjective assessment of performance expectancy, effort expectancy, social influence, and facilitating conditions, a continuum of five-point, unipolar Likert-type scale from 1,\"strongly disagree\", to 5, \"completely agree\", was used. A five-point, unipolar Likert-type scale from 1, \"never\", to 5, \"very often\", was used to capture respondents' subjective assessment of behavioral intention and use behavior. Ten system functionalities were observed through the use behavior factor within this study: announcements, discussion forum, learning materials, video materials, assignments, instructions, test, chat, gradebook, and participants list.