esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The benefit of this study is that it also quantifies the investigated dependence between the selected parameters and describes the relationships between them. Based on the above information and studies, it is assumed that the time of delivery of materials and services for construction depends on several factors. The research question, which stems from the previous knowledge of this issue, is whether it is possible to shorten the delivery time of materials and services for construction by the result of the big data concept and SCM. In connection with this, there is another question of whether the construction time is also shortened. If the considerations are confirmed, it is logical to focus this research on the assumption of the cost side of CRM and big data (BG) concept use. Based on these assumptions, primary research questions will be opened:  
1. Does the use of the big data concept in SCM impact on the delivery time of materials and services? 2. Does the use of the big data concept in SCM impact on the delivery time of construction? 3. Does the use of the big data concept in SCM impact on project cost? All these scientific questions are based on the perception of ecological and economic sustainability in the design of buildings. The use of digital technologies and innovations as a supply chain management by big data concept can potentially reduce the time of material delivery and thus save material (ecological and economic aspects of sustainability), shorten construction time (the economic aspect of sustainability) and reduce costs (the economic aspect of sustainability).
# Development of a Magnetostrictive FeNi Coated Surface Acoustic Wave Current Sensor
## 4 Sensor Experiment and Discussion  
### Experimental Setup  
The physical map and schematic diagram of the experimental setup for current sensor characterization were shown in Figure 10. The experimental setup was composed of the developed SAW current sensor, DC current source (YangZhou Huatai Electronics Co., Ltd., Yangzhou, China), Helmholtz coil (Tianduan Industry Co., Ltd, Shanghai, China) for stimulating the magnetic field under giving current, PC and Gauss meter (Tianduan Industry Co., Ltd) for calibration (Figure 10a). Usually, the strongest magnetic field intensity and the best uniformity occur at the central axis of the Helmholtz coil, hence, the sensor was positioned at the central axis of the platform in the middle of the coil. As shown in Figure 10b, the applied current for Helmholtz coil stimulates electromagnetic field, and the corresponding sensor response induced by the magnetostrictive effect was taken from the oscillation frequency signal connected at 1 point per 50 ms by the FPGA.  
Figure 10: The physical picture and schematic diagram of the experimental setup for sensor performance evaluation, (**a**) experimental setup; (**b**) working principle.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 5 LoRaWAN Analysis
### The Gateway Role  
The current specification states that the gateway is only a relay. This is linked to the fact that the packets sent by the devices have no destination address (which saves a few bytes) and that there is no association between a device and a gateway. Indeed, as several gateways can receive the same message from a device, only one of them should reply to it. It falls back to the network server to choose the best gateway.  
The only task that should be handled by the gateways is the timing of the downlink messages. This timing should be accurate so that the device receives the message in its receive window. It is not specified whether the gateways receive a message to send from the server along with the time at which it should be sent or if the gateway sends the message received from the server as soon as it receives it, and it is unclear which solution is implemented in existing gateways. As the round trip time of the backhaul interface of the gateways cannot be controlled, the first solution should be implemented. It would also allow one to synchronize the transmissions of the different gateways, avoiding collisions between them.  
In the current specification, each gateway is dedicated to a specific network server, as shown in Figure 1. This means both the gateways and the data collected are \"owned\" by the entity that runs the only network server. In the future, it would be interesting to extend the function of the gateways so that they can forward the packet to specific network servers, as shown in Figure 12. This may effectively reduce the expense of devices and network deployment.
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
## 2 Materials and Methodology
### Methodology  
The ecosystem's source of both material and energy is the GPP that plants make during photosynthesis. The final crop yield is directly correlated with the amount of GPP accumulated over the course of crop growth and development. The accumulated GPP of summer maize over the study area was calculated using the EC-LUE model [42]. Then, the GPP was transformed into yield by using the HI.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
###### Abstract  
During blasting in deep mining and excavation, the rock masses usually suffer from high in situ stress. The initial seismic energy generated in deep rock blasting and its attenuation with distance is first theoretically analyzed in this study. Numerical modeling of the multiple-hole blasting in a circular tunnel excavation under varied in situ stress conditions is then conducted to investigate the influences of in situ stress levels and anisotropy on the blasting seismic energy generation and attenuation. The case study of the deep rock blasting in the China Jinping Underground Laboratory (CJPL) is finally presented to demonstrate the seismic energy attenuation laws under varied in situ stress levels. The results show that with the increase in the in situ stress level, the explosive energy consumed in the rock fracture is reduced, and more explosive energy is converted into seismic energy. The increasing in situ stress causes the seismic \(Q\) of the rock mass medium to first increase and then decrease, and consequently, the seismic energy attenuation rate first decreases and then increases. Compared to the condition without in situ stress, the blasting seismic energy decays more slowly with distance under in situ stress. Then the seismic waves generated in deep rock blasting are more likely to reach and exceed the peak particle velocity (PPV) limits stipulated in the blasting vibration standards. Under non-hydrostatic in situ stress, the generation and attenuation of the blasting seismic energy are anisotropic. The highest seismic energy density is generated in the rock mass in the minimum principal stress orientation. Its attenuation is dependent upon the in situ stress aligning the wave propagation orientation.  
blasting; in situ stress; seismic energy; energy attenuation; rock fracture
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 5 Defense Strategy
### Modifying Model
#### 5.2.2 Defensive Distillation  
[PERSON] et al. [14] proposed a defensive distillation method to resist attacks on the basis of distillation technology [88]. The original distillation technology aims to compress the large-scale model into small-scale and retain the original accuracy, while the defensive distillation does not change the scale of the model, and produces a model with a smoother output surface and less sensitivity to disturbance to improve the robustness of the model. As is shown in Figure 16, they firstly trained an initial network \(F\) on data \(X\) with a softmax temperature of \(T\), and then used the probability vector \(F(X)\), which includes additional knowledge about classes compared to a class label, predicted by network \(F\) to train a distilled network \(F^{d}\) at temperature \(T\) on the same data \(X\). The authors demonstrated using defensive distillation can reduce the success rate of adversarial attack by 90%.  
However, the effectiveness of defense distillation cannot be guaranteed in black-box attacks. Therefore, [PERSON] et al. [50] proposed extensible defense distillation technology.
# Effects of Temperature on the FT NIR Raman Spectra of Fish Skin Collagen
###### Abstract
Moreover, hydroxyproline is an important amino acid in maintaining thermal stability of collagen molecules [34; 35]. The most common secondary structure in collagen type I is the \(\alpha\)-helix forming a triple helix along with the \(\beta\)-sheet and irregular conformation [36; 37]. The triple helix structure is upheld mainly by hydrogen bonds between the glycine - NH group and carbonyl group C=O of residues of another polypeptide chains. Additionally, the triple helix's stability is due to hydrogen bonds between the C\(\alpha\)H group from glycine and C=O and hydrogen bonds with water molecules. The tree of hydrogen bonds is essential for maintaining the triple helix's stability. On the contrary, its absence leads to a variety of pathological situations [32; 38]. The collagen helix is additionally stabilized by van der Waals forces, along with hydrophobic and electrostatic short range interactions [39]. The secondary structure of native collagen can be affected by different factors such as temperature, mechanical forces and chemicals [40; 41; 42; 43; 44; 45]. Any deformation in colla  
Figure 1: Schematic model of glycine (**a**), proline (**b**) and hydroxyproline (**c**). gen/protein structure and conformation can be followed by means of Raman spectroscopy, which is particularly useful for this purpose. Any alteration/variation in collagen molecules is noticed in the shift of the position of Raman bands, their intensity and spectral width. The aim of the study was to characterize the secondary structure of silver carp skin collagen in terms of exposure to temperature by means of Fourier transform near IR Raman spectroscopy. NIR Raman spectroscopy appears to be an extremely useful in biomaterials studies. Using laser excitation with wavelength of 1064 nm, beyond the range of electronic absorption, we avoid fluorescence effects. Additionally, lower energy excitation allows us to avoid decomposition (e.g., thermal, photochemical, chemical) of the biomaterials during study. As the reference material, bovine Achilles tendon collagen was used.
# Ferrites and Nanocrystalline Alloys Applied to DC-DC Converters for Renewable Energies
## 7 Conclusions  
The analysis and construction of MFTs are key to developing new advanced DC-DC converters in terms of maximal power density, low cost, and greater efficiency. The core materials and the design process essential in defining the characteristics and performance of MFTs.  
The nanocrystalline MFT has 2.3 times higher power density than the ferrite MFT. Regarding construction costs, the ferrite-core MFT costs are half that of the nanocrystalline ones. Both transformers have high efficiency (>98%). The nanocrystalline-core MFT in this work surpasses those presented in cutting-edge literature in terms of power density, 36.91 kW/L, and efficiency, 99.7%. Therefore, this works contributes to putting a step forward in obtaining DC-DC converters better suited to modern day applications such as fuel cell supplied systems, photovoltaic systems, electric vehicles, and power electronic transformer (solid-state transformers).  
Performed prototype experiments, D.R. and J.O.; proposed the idea, D.R.; supervised the research, C.F. and E.M.; gave technical support and conceptual advice, H.V., A.V. and V.V.; wrote the paper, D.R.; paper review, C.F. and E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** The authors thank the UMSNH (Universidad Michoacan de San Nicolas de Hidalgo), the ENES Juriquilla--UNAM (Escuela Nacional de Estudios Superiores Unidad Juriquilla, Universidad Nacional Autonoma de Mexico) and CONACYT for supporting our research and projects leading to the writing of the present paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Time Complexity  
Processing videos is a time-consuming process, and when the medical field is involved, it is even more complex, because medical videos have lots of uninformative frames, noise, and background details. Some of the work above [76; 86] has proved to give acceptable accuracy, but the time taken by the model to process the video is very high. Due to time complexity, these models can be used offline rather than in real time.  
One more problem observed during the survey is a lack of standard performance metrics [87]. Time complexity is a very important factor but is completely ignored by some of the research works. The lack of standard metrics makes it difficult to compare the performance of different approaches.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 6 Resin Adhesive Systems
### Functional monomers
PENTA has five vinyl groups compared to one in 10-MDP monomer, and these four additional vinyl groups make PENTA more resilient to hydrolytic degradation [202; 203; 51]. Thus, when hydrolysis occurs and eliminates one vinyl group from the main structure of the monomer, four vinyl groups will still be present for the conservation of the P group. Accordingly, copolymerization to other monomers and adhesion to tooth structure happen at the same time [189; 202]. This finding could be elucidated by a previous study which demonstrated that PENTA incorporated into the formulation of the adhesive Prime & Bond Elect (Dentsply Caulk, Milford, DE, USA) had more stability than 10-MDP, as the BS is maintained even at the end of its shelf-life. This is explained by the sustained connection between the four vinyl groups and the P group [202; 204] (Figure 14). Both MDP and PENTA monomers are acidic, so they react with the dental surface by adhering to Ca ions. Thus, these ions were integrated into HL by forming Ca-P complexes. PENTA monomer, unlike 10-MDP, is unable to show a nano-layer (like MDP-Ca salts) due to its massive structure, however, with five double bonds, and a hydrophilic core (unlike MDP, which is hydrophobic), this molecule is capable of improving the wetting process [205; 206]. A difference in the interfacial interaction of each functional monomer has been designated in the AD concept that displayed the way molecules interact with HAp-based tissues [33]. This model displays that, primarily, all acids chemically bond to Ca of HAp (Phase 1). This primary bonding phase drives together with a release of hydroxide and P ions from HAp into the solution to achieve an electron neutrality. The stability of the correspondingly generated monomer-Ca salt determines whether the monomer will stay bonded (Phase 2, \"adhesion route\") or de-bond coupled with an abundant decalcification (Phase 2, \"decalcification route\") [33; 207]. As an example, since 10-MDP-Ca follows the adhesion route while GPDM-Ca essentially follows the decalcification route, the 10-MDP-Ca salt is more stable than the GPDM-Ca salt [207]. Knowing that there is a large number of bonding agents in the dental market, it is challenging to choose the best version. Additionally, a number of these adhesives are introduced as modifications of previous iterations. Therefore, it is crucial to emphasize the features of each component inside the adhesive of choice for better performance. Moreover, the incorporation of various co-monomers (cross-linkers or adhesion promoters), catalysts,  
Figure 14: (a) Dipentaerythnitol penta-acrylate phosphate has a more reliable bond than (b) 10-methacryloyloxydelyd dihydrogen phosphate-based adhesives. Reactive double bonds (circles) and acidic moieties (triangles) are marked [204]. and solvents strongly altered the adhesive film properties, which affected the BS of the adhesive system [208].
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 2 Flow-Induced Vibrations (FIVs) and Energy Harvesting  
FIVs phenomena are caused by various aerodynamic instabilities classified as VIVs, galloping, flutter and wake galloping, each of which results in distinct aeroelastic response, with galloping and VIV being widely explored for energy harvesting. The power generation process from these aeroelastic instabilities encompasses the combined effects of fluid forces, mechanical coupling with the device structure (described as FSI) and the energy transduction mechanism utilized in the device. The interaction of these three research areas necessitates a highly interdisciplinary approach, as illustrated in Figure 2. Researchers in fluid dynamics, structural mechanics and electrical engineering interact to design systems that can effectively harvest energy from aeroelastic instabilities into useful electricity. This is especially useful in scenarios where standard wind or water turbines are impractical.
# Gating Hysteresis as an Indicator for Silicon Nanowire FET Biosensors
## 3 Results and Discussion
### Measurements Concept  
A source meter (Keithley 2602, Tektronix, Koln, Germany) was used for voltage supply and current measurement of FET arrays. Source and drain electrodes were contacted via probe station needles. The gate was connected to the reference electrode and to the back-gate. The conventionally applied method for measuring the bioanalytes in NW FET is realized either with a fixed gate voltage \(V_{G}\) or at fixed source drain current \(I_{SD}\) using a feedback loop. In contrast, we monitored the signal changes in the FET by sweeping \(V_{G}\) and recording source drain current \(I_{SD}\) continuously at a sampling rate of around 50 data points per sweep [32]. The gate voltage resembles a triangular shaped signal with a frequency of about 1 Hz. The \(V_{G}\) range was chosen such that the complete switching characteristic of the FET device was recorded in each sweep. The measurements were controlled by a Matlab-based program (The Mathworks Inc., Natick, MA, USA) for continuous monitoring of \(I_{SD}\) and \(V_{G}\). This approach allows us to extract the threshold voltage shift \(dV_{T}\) at a fixed \(I_{SD}\) from the recorded data and to analyze its shift in real time. Compared to a feedback loop regulating a fixed \(I_{SD}\) it delivers additional information on any possible change in shape of the I-V characteristics. This feature is specifically helpful in our proposed method since the separated analysis of the \"positive\" (from \"+\" to \"-\") and \"negative\" (from \"-\" to \"+\") branches of the sweep allows us to monitor evolution of the hysteresis in the transfer characteristics upon thrombin molecules loading. For the analysis purposes, we define the hysteresis width \(V_{Inst}\) via subtraction of positive and negative \(V_{G}\) scans during live measuring of the transfer characteristics of the NW FET.
## 2 Related Work  
Over the last few years, the detection of Android malware has been considered a crucial research field, prompting numerous teams to propose various methods and techniques to address the increasing number of malware-related issues. In this section, we specifically focus on malware detection using GNNs, which is relevant to the approach presented in this paper.  
GNNs have been successfully employed in various fields, including malware detection. GNNs are well-suited for capturing complex relationships and patterns between entities represented as nodes in a graph. By leveraging the attributes and connections of nodes, GNNs can effectively detect and classify malicious behavior in Android applications. BejaGNN [17] is a revolutionary Java malware detection method employing a GNN including a GCN [14], graph attention network (GAT) [18], and graph isomorphism network (GIN) to create a control flow graph inherent in Java program files, and then refine them to maintain the precision of the graph. Another method for classifying families of Android malware is GDroid [19], which utilizes GCNs. It involves constructing a heterogeneous network with two types of relations, processed by the GCN model to generate node representations that consider both topological structure and node properties. The first work to combine Android malware detection with heterogeneous graphs is the Hindroid [20]. It first creates a heterogeneous network outlining the connections between APIs and Apps as well as their internal relationships. It then isolates feature matrices from decompiled APKs and uses a multiple kernel SVM (support vector machines) classifier to categorize them. Hawk [21], with its description of Android entities and behavioral links as a heterogeneous information network, is a malware detection framework for applications on the Android operating system. Additionally, to address dynamically appearing applications, an incremental learning model is developed rather than the entire heterogeneous graph and subsequent embedding model. The method proposed by [22] captures the behaviors of applications through runtime executions and constructs a heterogeneous graph to produce node representations.  
Due to the success of malware detection methods based on GNNs, we drew inspiration from the heterogeneous graph transformer [23], which has been proven effective on large academic heterogeneous graphs, and developed a customized Android APK Transformer module specifically for Android malware detection tasks.
## 4 Results
### Analysis of the Recorded Speech Supports the Findings of the Video Analysis  
A total of 823 audio samples were recorded in the experiment, but many were unusable (were completely silent, contained very loud background noise, etc.), leaving us with 517 voice recordings. These voice recordings were analyzed using the LSTM-based neural network described in Section 3.2.2. The results are summarized in Table 5. It can be seen that the most frequent emotions of the recorded voice were disgust and anger, i.e., negative valence and low arousal values, and negative valence and high arousal values, respectively. These results are consistent with those obtained from the analysis of the volunteers' facial expressions, as shown in Figure 6. Happy, calm, and surprised initial states were rare but present in the interactions.  
Unfortunately, it was not possible to improve the accuracy of emotion estimation from the original video frames, but since the negative emotion estimates from the audio matched negative valence values and the positive ones matched positive valence values, it helped to validate, albeit qualitatively, the results of emotion estimation from facial expressions.