esa-sceva/satcom-chunk-collection huggingface dataset:
# Combined Investigation of Indoor Climate Parameters and Energy Performance of a Winery
## 4 Conclusions  
The experimental investigation of indoor climate parameters through a complete year of winery operation resulted to the following findings:  
* Quite high CO\({}_{2}\) concentration values during the periods of fermentation at the vinification area.
* Night ventilation contributed to the decrease of the respective CO\({}_{2}\) concentration values.
* The effect of night (and partially daily) ventilation resulted also to the detection of higher VOCs concentrations for the non-winemaking period, taking place by wintertime.
* The preparation of yeast led to high CO\({}_{2}\) concentration values at the storage rooms, while the predominant VOCs for the storage room are a-pinene and 3-carene, i.e., terpenes, naturally emitted from the cork and wood (pine) made barrels of the wine storage tanks.
* In the office, CO\({}_{2}\) concentration values were affected by both the presence of people and fermentation process. The determined CO\({}_{2}\) concentration values exceeded the respective exposure limits of the respective regulations, demonstrating inadequate ventilation.
* Thermal parameters, and especially temperature, proved to be well regulated in the respective spaces (office, storage rooms) were caution was implemented.  
The formulated energy model, having incorporated determined indoor climate and energy performance parameters, and validated through actual consumption data, allowed an insight on the involved processes. The assessment of energy interventions, through the model, namely the replacement of the air-to-liquid cooling unit, and the installation of PV panels with a total area of 50 m\({}^{2}\), demonstrated 54.7% energy saving and 3.6 payback period.  
The proposed methodology can be, through proper adjustment, exploited for relevant units, i.e., premises combining building uses and agro-industrial processes, allowing the assessment of indoor environment and energy performance, as well as the evaluation of interventions towards the reduction of energy consumption and improvement of the environmental footprint of the respective units.  
Conceptualization, G.P. ([PERSON]); Data curation, E.I.T.; Formal analysis, E.I.T., A.A. and M.S.; Investigation, P.T. and G.P. ([PERSON]); Project administration, G.P. ([PERSON]); Supervision, G.P. ([PERSON]); Writing--original draft, G.P. ([PERSON]); Writing--review & editing, E.I.T. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors of this work would like to thank [PERSON] and [PERSON], owners of Domaine Karanika, for allowing their access and work on the premises, as well as for the provision of all necessary data for the analysis.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodology
#### 2.1.2 Signal Filtering
The values found for the second section of the filter are \(C_{1}=261.4\) nF and \(C_{2}=38.25\) nF. The high-pass filter is calculated using the following:  
\[f_{c}=\frac{f_{3 dB}}{k_{f}},\ R_{1}=\frac{\alpha}{2w_{c}},\ R_{2,f}=\frac{2}{w _{c}\alpha}, \tag{7}\]  
where \(f_{c}\) is the cut-off frequency of the filter, \(f_{3 dB}=12\) Hz is the design frequency of the filter, \(C_{1}=1\) uF and \(C_{2}=1\) uF are the capacitors proposed to tune the filter to the desired frequency, \(R_{1}\) and \(R_{2,f}\) are the resistors calculated to tune the filter to the desired frequency, and \(\alpha_{1}\), \(k_{f1}\) and \(\alpha_{2}\), \(k_{f2}\) are the frequency factors used in the first and second sections of the high-pass filter, respectively. The values for the first section of the high-pass filter are as follows: \(f_{c}=12\) Hz, \(w_{c}=75.39\) rad/s, \(C_{1}=1\) uF, \(C_{2}=1\) uF, \(R_{1}=12.2\) k\(\Omega\), and \(R_{2,f}=14.3\) k\(\Omega\); and for the second section of the filter, the values are as follows: \(f_{c}=12\) Hz, \(w_{c}=75.39\) rad/s, \(C_{1}=1\) uF, \(C_{2}=1\) uF, \(R_{1}=5.07\) k\(\Omega\), and \(R_{2,f}=34.6\) k\(\Omega\). Figure 4 presents the configuration used for the band-pass filtering of the EEG \(\beta\) and \(\alpha\) signals. It is important to note that for high-pass and low-pass filters, the concept of damping is crucial. In band-pass filters, damping is typically specified by the parameter Q, known as the quality factor, which is the reciprocal of the damping factor. This factor represents the degree of disturbance the filter may introduce, potentially distorting the frequency response of the acquired signals. To determine the cut-off frequency, it is necessary to know the center (or peak) frequency of the filter, which is the point of maximum gain--in our design, this is a unity gain. Our system aims for a wide bandwidth, and based on this, if a filter requires a relatively low Q, around one or less, it is best implemented as a cascade of separate low-pass and high-pass filters [36]. Additionally, tantalum electrolytic capacitors are used for their stability and as decoupling elements for operational amplifiers. Figure 4: Filtering with bandwidth from 13 to 30 Hz.
## 4 Discussion
Curiously, while plasma successfully reduces proliferation in MCF7 lines, it is not capable of producing the equivalent reduction in metabolic activity. In fact, cell mass decreases while metabolic rate remains high, suggesting an increase in metabolic rate per cell as seen in Appendix A Figures 11 and 12 when we compare the plots of metabolic activity and protein content for MCF7. These cells display an intermediate response to plasma between resistant and susceptible cells, which we categorize as stressed cells. We hypothesize that this enhanced MTT index represents an adaptation to stress by the selection of a subpopulation of highly metabolic cells or, alternatively, activation of survival processes with hyperactive mitochondria or increased mitochondrial mass. Studies suggest that some established tumor cell lines, such as MCF7 [77], have a subpopulation of highly metabolic cancer stem cells that have the capacity of self-renewal and differentiation [78]. The proportion of this subpopulation can increase with treatment either by induction of pluripotency in non-stem cells or by the selection of more resistant stem cells [77]. Alternatively, cytotoxic events were shown to induce mitochondrial biogenesis and hyperactivation in MCF7 cells, leading to increased metabolic viability and MTT reduction [75]. The other unexpected case was seen with PC3 and MNNG/HOS which displayed a reduced MTT index, which can be explained by a decrease in mitochondria content most likely in the context of autophagy. Cytotoxic events often induce autophagy, but whether this is a death mechanism or an unsuccessful attempt at cellular preservation is often unclear [79]. Autophagy can drive survival, but not necessarily in nutrient-deprived conditions, as it can be induced by ROS [80]. Despite the role of autophagy in supporting cell survival, cell death following progressive cellular consumption has been attributed to unrestricted autophagy. Thus, autophagy can explain a reduction in metabolic activity disproportional to the proliferation rate. Several studies report the superior antiproliferative effect of plasma in cancer cells when compared to benign parenchymal counterparts or stromal cells [13; 28; 81]. While we did not perform the same assays in normal counterpart cells, we tested the same methodology in the standard somatic cells of connective tissue, fibroblasts HFF-1, which was one of the cell lines categorized as resistant. However, an explanation for this differential effect and selectivity is still lacking. While a low dose of non-thermal plasma may have little to no effect on cancer cells, higher doses may ablate both cancer cells and phenotypically normal counterparts. In between, there is a range of conditions where selective damage to malignant cells might be possible. In fact, in the future, we aim to characterize treatment effect by varying setup conditions, which were maintained arbitrarily in this study, such as electric potential difference and frequency of oscillation of the AC circuit.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 4 Future Research Challenges
### Verifying the Effects of Machine Learning  
When we learn the best process parameters and other data items through machine learning, we cannot immediately judge the effectiveness of machine learning, because we can only admit that machine learning has effectively discovered useful knowledge of material data after applying the acquired knowledge to the production of materials and really improving the manufacture of materials. However, the important issue is that this process is time consuming and labor intensive, so it will make the whole process of data fusion and knowledge discovery most inefficient. After we use machine learning to obtain material knowledge, how to conduct experiments to judge the validity of knowledge and give effective feedback on machine learning is a research difficulty. Therefore, the main research direction in the future is how to combine the corresponding domain knowledge, so that the entire artificial intelligence work process can be feed backed by the generated knowledge to improve the structure of the neural network. Material data encounters many inconveniences in actual use. For example, when querying thermal barrier ceramic coating data, if you use relational database storage, it only works out on the situation where the relationship is relatively simple, but many materials have complicated structures, so the graph database storage is used, which has stronger semantic expression capabilities, and the knowledge graph provides a feasible way to organize material entities into entity networks. Many experiments in the materials industry are often very time consuming and expensive.  
If only the results of real experiments were used to verify the effectiveness of machine learning, the cost of money and time will be greatly increased. Therefore, the design and implementation of simulation experiments with results close to real experiments will be a very important research direction. Moreover, how to improve the effectiveness and accuracy of simulation experiments is an important research subject. Existing simulations can simulate the process of material synthesis, but it is impossible to verify the working effect of the material, and there is a long way to approach the real process of manufacturing and using materials. Therefore, if the reference value of simulation experiments to data verification can be increased in the future, the whole process of data fusion and knowledge reasoning will be accelerated. The simulation experiment includes not only the simulation program running on the computer, but also directly verifying the material performance with a professional material experiment facility [75].
## 3 Data Collection and Experiment Analysis  
### Data Collection  
This paper takes advantage of GPS L1 and BDS B1 frequency observation data, and the sampling rate is 1 Hz. The reference values of the three-dimensional position, velocity, and  
Figure 1: Flowchart of the proposed Multipath/NLOS Detection method based on K-means Clustering for GNSS/INS tightly coupled system.  
Figure 2: Observation environment of the base station and GNSS receiver: (**a**) The base station; and (**b**) Trimble GNSS-Ti earth-type check-ring antenna.  
attitude of the vehicle are acquired through GNSS RTK/INS tightly coupled positioning, which is used to verify the improvement of positioning accuracy after excluding outliers. The NovAtel ProPak6 receiver of the rover station and the Trimble receiver of the three static base stations provide the GNSS carrier phase observations. Moreover, the solution of the reference value is realized using Inertial Explorer post-processing software developed by NovAtel Company [44]. The experimental platform is shown in Figure 3, and Table 1 is the specific technical parameters of the IMU.  
The experiment was carried out in the urban area of Nanjing, where the typical scenarios included tree shade and urban canyons, etc. Figure 4 shows the vehicle trajectories corresponding to two different observation periods. The two sets of datasets are marked as D1 and D2 in the order of time. D1 is an offline dataset, and D2 is an online dataset for real-time positioning verification. In addition to the occlusion of satellite signals caused by obstacles such as huge buildings, the strong reflection effect of modern building materials can also cause serious multipath and NLOS signal reception. Furthermore, the lush tree canopy on both sides of urban roads can also lead to complex multipath effects.  
The specific information of the dataset is displayed in Table 2. The number of valid epochs refers to the number of epochs for which the position solution is obtained. Owing to the influence of the observation environment, some epochs do not have GNSS data output, or the number of observation satellites is too small to conduct the double system positioning solution. Therefore, these epochs will be considered to be invalid epochs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Gyroscope** & **Accelerometer** \\ \hline Bias stability & 0.5 deg/h & 1250 \(\upmu\)g \\ Scale factor & 100 ppm & 100 ppm \\ Random walk & 0.3 deg/\(\sqrt{\text{h}}\) & 100 \(\upmu\)g /\(\sqrt{\text{h}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: IMU technical data.  
Figure 3: The test platform and equipment of the experimental vehicle: (**a**) The measurement vehicle; (**b**) satellite signal receiving antenna; (**c**) integrated system sensors.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range Free Technique  
Range free localization techniques do not require distance measurements, they utilize network data for a location estimate. They entirely depend upon the content of the received packet and have a lower cost than the range-based techniques [30]. In this approach, localization is based on geometrical interpretation, constraints minimization and region development, and it is a straightforward, economical and energy-efficient technique. We discussed important range-free techniques, viz. centroid, hop count, analytical geometry, mobile anchor node and an approximate point-in-triangulation test below.
## 2 Architecture and Operation of Proposed RO PUF
### Security Analysis
#### EM Side-Channel Attack  
EM side-channel attack has been successfully adopted to break the RO PUFs [12]. The analysis is based on the study and comparison of the detectable EM emanations' frequency spectrum for the active RO. According to [PERSON] transmission equation [20]:  
\[P_{r}=G_{t}G_{r}(\frac{\lambda}{4\pi R})^{2}P_{t} \tag{12}\]  
where \(G_{t}\) and \(G_{r}\) are the antenna gains of the transmitting and receiving antennas, respectively, \(\lambda\) is the wavelength, \(R\) is the distance from the detector probe to the device, \(P_{r}\) is detectable magnitude of the EM radiation and \(P_{t}\) is the device's working power consumption. Our proposed hybrid RO PUF has lower power consumption over the classic RO PUF. Since \(P_{r}\) is proportional to \(P_{t}\), it will have a lower magnitude of EM radiation.  
To validate our proposed PUF's resilience against EM side-channel attack, _FLS 106_ IC scanner is used to capture the EM radiation close to the fabricated chip's surface. Figure 11a,b illustrate the measured spectrum of the regular RO and the proposed RO fabricated in the same chip, respectively. It is noticed that an EM radiation of 100.38 dB\(\mu\)V at 278 MHz is detected for the regular RO, however, the EM radiation magnitude of the proposed RO is too small to be distinguished from the noise floor. Additionally, as the proposed RO PUF only occupies a tiny silicon area with an interleaving structure, it is quite challenging to pinpoint it on the chip, which further elevated the proposed RO's security performance [21].  
Figure 10: The prediction accuracy by support vector machine (SVM) for the proposed PUF with and without reconfigurability.
## 4 Results and Discussion
### Simulation of the Effects of Rainfall Intensity on Slope Stability
#### 4.5.2 Simulation Case of a Silt Clay Soil
Figure 22: Safety factor simulated for rainy conditions in silt clay soil slope using **(a)** Lower Karafiath method and **(b)** Gle/Morgenstern Price method. Figure 23: Simulated FoS of the silt clay soil slope using FLACSlope in sunny to rainy conditions with water table with 1000 m\({}^{3}\) volume of water. Note that maximum shear strain simulated ranges between \(2.500\times 10^{-8}\) to \(4.432\times 10^{-7}\). Figure 24: Simulated FoS of the silt clay soil slope using FLACSlope in rainy conditions with a water table with 1500 m\({}^{3}\) volume of water. Note that maximum shear strain simulated ranges between \(2.500\times 10^{-8}\) to \(6.718\times 10^{-7}\). Figure 25: Simulated FoS of the silt clay soil slope using FLACSlope in rainy conditions with a water table with 2000 m\({}^{3}\) volume of water. Note that maximum shear strain simulated ranges between \(5.000\times 10^{-9}\) to \(1.491\times 10^{-7}\). The simulation results in Figures 23-26 show a rapid drop in FoS. This is indicative of the contribution of rainfall to the FoS which leads to a less stable slope. To put it another way, landslides are more likely to occur in this material when extremely wet. In summary, historical rainfall data and visual observations corroborate the simulation results. It is posited that the soil phase quickly changes during heavy rains. This turns the soil into a liquid-like material. At this stage, the shear strength rapidly drops and weakens the bond between particles. Finally, deformation ensues in the form of a landslide.
# The Viable but Non-Culturable (VBNC) State, a Poorly Explored Aspect of Beneficial Bacteria
## 3 The VBNC State in Beneficial Bacteria
### Actinobacteria and the VBNC State  
The Actinobacteria group includes _Sinomonas albida_, formerly known as _Arthrobacter albidus_, isolated from a seepage substrate composed of volcanic rock from Niigata, Japan [92]. Among the main applications of this microorganism, it has a demonstrated ability to exit the VBNC state in the presence of the RpF protein, along with its efficient flocculant activity [55]. This flocculent activity represents a possible application in wastewater treatment and residual sludge deviating. At present, little is known about this species, which represents an alternative and challenge in terms of taking advantage of the biotechnological properties that _S. albida_ can provide.  
The presence of bacteria in the human digestive tract offers various properties related to health, such as the regulation of intestinal microbial homeostasis, the inhibition of pathogenic bacteria, the modulation of the immune response, anticancer effects, the production of bacterioxins, or the bioconversion of diet components into bioactive compounds [93]. Recently, the production and consumption of products with beneficial strains for human health has increased considerably [94]. _Bifidobacterium longum_ and _Bifidobacterium animals_ subsp. _lactis_ are multifunctional probiotic Actinobacteria with clinical effectiveness, including immunomodulatory, anti-inflammatory, antimutagenic, and anticancer properties and alleviating gastrointestinal diseases [94]. In both species, it has been observed that their storage at low temperatures in fermented foods can cause a loss of cultivability (Table 1). In this respect, the determination of the viability and activity of probiotic bacteria is of great economic, regulatory, and technological importance to ensure that fermented products or formulations with probiotics carry bacteria capable of exerting their beneficial effects.
# Design and Additive Manufacturing of TPMS Heat Exchangers
## 3 Additive Manufacturing of Gyroid HXs
### Wall Thickness and Density Test of Gyroid HXs  
A partial HX (HX70-555, 0.5 mm wall thickness) was polished in order to test its thin wall quality in terms of thickness, relative density, and uniformity. Density was confirmed with the microscope image method and the Archimedes method. The device was divided into four sections in order to check the gyroid structures, as shown in Figure 20.  
Figure 19: The interface layer distribution at different layer heights. ((**A**) means bad quality slice method and (**B**) means good quality slice method).  
Figure 18: Additively manufactured gyroid HX tested with water: (**Left**) partial HX to test water leakage and wall thickness. (**Right**) a complete HX.  
From the measurement, the thicknesses of the gyroid structures were within 450-600 \(\upmu\)m. Both the gyroid structures and enclosure walls had a relative density higher than 97%. Figure 21 shows the relative density tested at four randomly selected locations. Very few defects were observed. The sizes of the defects were within 14-50 \(\upmu\)m.
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 1 Introduction
Existing accuracy inspection methods are designed for the precise assembly of prefabricated components [26], which cannot be applied to the geometric accuracy evaluation of an entire subway station construction site, and systematic evaluation indexes and evaluation systems need to be established to provide efficient guidance for the subway station geometric accuracy evaluation process. The main contributions of this study are summarized as follows: (1) a coarse-to-fine registration method is proposed to effectively integrate point cloud and BIM to the same survey coordinate reference, and (2) a hierarchical evaluation index and system applicable to the structural surface of subway station buildings is proposed to achieve effective geometric accuracy evaluation through statistical analyses and visualization. This article is organized as follows. In Section 2, existing registration problems and geometric accuracy evaluation methods are reviewed. Section 3 describes the proposed registration method and evaluation index. A case study is presented in Section 4. The experimental results are discussed in Section 5. Finally, conclusions are presented in Section 6.
# Detecting and Mitigating Attacks on GPS Devices
## 4 Factors Contributing to GPS-Denied or GPS-Disrupted Environments
### Propagation-Induced GPS Degradation
Furthermore, it is difficult to conclude what factors provide the largest contribution to the results without an attempt to isolate degradation factors, such as shadowing and multipath fading.