esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Speed Welding of Stainless Steel with Additional Compensatory Gas Jet Blow Molten Pool
## 2 Principles and Methods  
### Analysis of Humping Bead Formation Mechanism with Relative Speed  
Humping beads appear easily during high-speed welding. The formation mechanism of humping beads is analyzed according to the molten steel flow in the molten pool and the relative speed and angle of the parent material, as shown in Figure 1.  
Arc welding is a very complicated process: modeling it accurately is difficult, as is measuring the arc zone data directly. The molten droplet transition and molten pool motion are commonly explained by principles. In the molten pool during conventional welding, the combined effects of arc force, gas flow impact force, molten droplet impact force, and pressure on the molten pool wall surface cause the velocity of the molten steel flow to exceed that of the parent material. If the molten steel transportation and supply capacities are high, the weld is even. During high-speed welding, the actual time of arc force and air impact force acting on the weld pool are decreased, and the positive momentum in the direction of the molten drop and welding increases. The entry of molten drops into the molten pool is a completely inelastic collision process in which momentum is conserved. These factors together cause the relative speed of the molten steel in the molten pool and the parent material to decrease, sometimes approaching zero. As the surface tension of the molten steel is predominant in determining the geometry of the molten pool, periodic accumulations are easily produced; these become humping bead defects.  
In this study, a gas jet is introduced to have a vertical impact on the unsolidified steel hump in a method called the compensatory gas jet impinging on the molten pool. The main functions of the method are as follows:  
1. The introduction of the additional gas jet at the rear position near the arc welding zone can affect the high temperature molten pool that has not yet solidified;
2. The impact force generated by the compensatory gas jet can significantly change the natural solidification mode of the welding molten pool, thereby apparently improving the weld formation and stirring the molten pool;
3. The gas cover formed during the process of compensatory gas jet impact on the molten pool can effectively extend the protection time on the surface of the molten pool;
4. The impact force effect formed by the compensatory gas jet and the thermal directional feedback effect caused by the gas cover can increase the molten pool width and weld depth.
# An Insight into the Chemistry of Cement--A Review
## 5 Pore Structure in OPC
### Bleeding Phenomenon Involving the Action of Water in Cementitious Paste  
Bleeding is a particular type of sedimentation, where a definite volume of mixing water remains separated at the surface of the cementitious matrix. The implication of this is the reduction in the final volume of the hardened paste soon after the placement, resulting in interparticle distance reduction and variation in the effective \(w/c\) ratio, which results being smaller than the initial one. The properties of cement paste showing bleeding are defined by the sedimentation rate and the volume of bleeding water, calculated as the difference between the initial mixing water and the water effectively inside the cement. The bleeding-induced effect increases with the \(w/c\) ratio, thus for a high \(w/c\) ratio, it is mandatory to add anti-settling agents. Examples of anti-settling agents are bentonite, hydro-soluble polymers, and inorganic salts. Such anti-settling agents act in different ways, namely: adsorbing large quantities of water to preserve the homogeneity of the slurry (i.e., bentonite) [88], increasing the cohesiveness between cement hydrates and the viscosity of the medium (i.e., hydro-soluble polymers) [89], or entrapping water within weak-bounded hydroxide structures formed within the slurry volumes (i.e., inorganic salts) [90].
## 3 DDoS Attack in SDN
### SDN Vulnerability against DDoS Attacks  
SDN can prevent DDoS assaults by separating the control plane from the data plane. The security concerns with SDN itself, however, have not yet been resolved. SDN inherently may be a victim for DDoS attacks because of its design. SDN is vertically divided into three functional levels. DDoS attacks can target these tiers. These vulnerabilities are: (a) controller saturation, (b) packet buffer overflow/saturation, (c) flow table overflow, (d) control-data plane congestion link/open flow agent overloading, and (e) flow entries duration [30; 31; 32].
## 5 Conclusions  
In this paper, we proposed a compact dual-band band-pass filter (BPF) with short stubs and a T-shaped band-stop filter (BSF). The proposed BPF is adapted to a stepped impedance resonator (SIR) with a folded and T-type structure. The advantages of this BPF are its compact size, low insertion loss, and sharp cut-off frequency. The conventional stub BPF operates only in the single band. In addition, the existing dual-band BPFs are connected individually to the BPF and BSF, which makes the existing BPFs bulky. In contrast, the BPF proposed by us is integrated with the BSF using a single structure, thereby reducing the size of the new BPF. The overall size of the proposed BPF is 0.0708 \(\lambda\)g \(\times\) 0.0533 \(\lambda\)g. The fabricated BPF has center frequencies of 2.43 GHz and 7.49 GHz, and the insertion loss is 0.102 dB and 0.103 dB at first and second center frequencies. Additionally, the return loss of first and second center frequencies for the proposed BPF is 19.13 dB and 19.96 dB, respectively. The bandwidth of the BPF is 120% and 105%, respectively. The proposed BPF can be mass-produced as a semiconductor owing to its planar structure. It can be applied to military mobile systems, medical systems, C-band weather radar systems (7.5 GHz), satellite communications (Satcom) (4-8 GHz), and industrial systems of wireless local area network (WLAN).  
**Author Contributions:** Design and simulation; K.Y., analysis, and supervisor; K.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Gachon Gil Medical Center(FRD2019-11-02(3)) and New Technology Development of AI based on High Value-added in the Ministry of SMEs and Startups (S2929853), respectively.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available owing to privacy and ethical restrictions.  
**Acknowledgments:** This fabrication and measurement was supported by RFIC Research Center (Director: [PERSON] and [PERSON]) at Kwangwoon University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Performance Evaluation  
This section evaluates the performance of BLE in terms of energy consumption, latency and piconet size, for various use cases and configurations, and discusses application layer BLE throughput. The size of the notification, command and response messages considered in this study is the maximum one (_i.e._, 37 bytes at the Physical Layer), whereas polls sent by the master and acknowledgments are assumed to be empty PDUs (_i.e._, PDUs without payload). Latency results have been obtained by exploiting simulation tools that we developed for this purpose (given that current simulators do not support BLE yet), and have been complemented by means of experimental measurements. The simulation tools model two connected BLE devices that communicate with each other, taking into account all the BLE stack layers and their behavior in the presence of bit errors. Piconet size results have been obtained theoretically, whereas energy consumption has been analyzed both theoretically and empirically. Further details about the evaluation methods are provided in each corresponding subsection.
## 3 Results and Discussion
### Linearity and Sensitivity
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & \multicolumn{4}{c}{**Food**} & \multicolumn{4}{c}{**Serum**} & \multicolumn{4}{c}{**Urine**} \\ \cline{2-13}  & \multicolumn{4}{c}{**RE\%**} & \multicolumn{4}{c}{**RE\%**} & \multicolumn{4}{c}{**RE\%**} & \multicolumn{4}{c}{**RE\%**} \\ \hline
**Phytoestrogen** & **LOD** & **LOQ** & **r\({}^{2}\)** & **100 ng/mL** & **LOD** & **LOQ** & **r\({}^{2}\)** & **16 ng/mL** & **40 ng/mL** & **LOD** & **LOQ** & **r\({}^{2}\)** & **200 ng/mL** \\ \hline Resveratrol & 0.025 & 0.084 & 0.994 & 44 & 0.04 & 0.12 & 0.992 & 92 & 86 & 0.018 & 0.06 & 0.998 & 50 \\ Biochanin A & 0.006 & 0.022 & 0.979 & 74 & 0.01 & 0.04 & 0.977 & 77 & 68 & 0.002 & 0.008 & 0.951 & 97 \\ Seciosolaricinesional & 0.023 & 0.078 & 0.993 & 66 & 0.07 & 0.25 & 0.987 & 99 & 99 & 0.008 & 0.027 & 0.993 & 59 \\ Luteolin & 0.009 & 0.033 & 0.995 & 20 & 0.01 & 0.02 & 0.998 & 82 & 72 & 0.028 & 0.096 & 0.982 & 80 \\ Cournestrol & 0.003 & 0.117 & 0.977 & 88 & 0.01 & 0.05 & 0.971 & 30 & 29 & 0.001 & 0.003 & 0.960 & 101 \\ Formonontein & 0.007 & 0.023 & 0.998 & 78 & 0.04 & 0.13 & 0.978 & 73 & 65 & 0.003 & 0.011 & 0.970 & 92 \\ Daidein & 0.011 & 0.037 & 0.987 & 67 & 0.001 & 0.003 & 0.972 & 102 & 94 & 0.042 & 0.141 & 0.984 & 76 \\ Enterodiol & 0.043 & 0.144 & 0.948 & 72 & 0.06 & 0.20 & 0.981 & 98 & 88 & 0.039 & 0.133 & 0.983 & 103 \\ Equal & 1.061 & 3.541 & 0.992 & 94 & 0.53 & 1.77 & 0.968 & 104 & 96 & 0.068 & 0.227 & 0.981 & 90 \\ Kaempfer & 0.008 & 0.029 & 0.991 & 15 & 0.05 & 0.16 & 0.986 & 76 & 63 & 0.003 & 0.012 & 0.989 & 76 \\ Glycitein & 0.212 & 0.709 & 0.968 & 92 & 0.13 & 0.45 & 0.980 & 90 & 88 & 0.075 & 0.251 & 0.969 & 111 \\ Matairesinol & 0.012 & 0.041 & 0.968 & 87 & 0.01 & 0.04 & 0.984 & 93 & 91 & 0.059 & 0.198 & 0.980 & 98 \\ Naringenin & 0.014 & 0.041 & 0.988 & 70 & 0.01 & 0.04 & 0.997 & 84 & 71 & 0.005 & 0.019 & 0.985 & 83 \\ Quercetin & 0.011 & 0.036 & 0.989 & 80 & 0.06 & 0.21 & 0.984 & 83 & 76 & 0.061 & 0.206 & 0.986 & 84 \\ Enterolactone & 0.002 & 0.008 & 0.978 & 85 & 0.01 & 0.03 & 0.979 & 80 & 74 & 0.001 & 0.003 & 0.976 & 110 \\ Genistein & 0.006 & 0.021 & 0.981 & 113 & 0.003 & 0.01 & 0.988 & 101 & 89 & 0.001 & 0.006 & 0.975 & 87 \\ \hline \end{tabular}
\begin{tabular}{c} Limits of detection (LOD) and quantification (LOQ) were set as the lowest final concentration in the sample, with a signal-to-noise ratio of 3 a 10, respectively, expressed in ng/mL. Linearity was performed three times, and each level of concentration was injected in triplicate. The linearity of the method was evaluated in the range of 0.02 to 2000 ng/mL for serum, and 0.008 to 1200 ng/mL. For food and urine. Mean recoveries were calculated as the ratio of the peak area of phytoestrogen spiked before extraction to the peak area of the standard multiplied by 100 in blank samples on three different days.  
\end{table} 
Table 2: Limits of detection (LOD), limits of quantification (LOQ), method linearity (r\({}^{2}\)) and recoveries (RE\%) of phytoestrogens in food, serum and urine.
## 4 Experiments and Results
### Fault Tolerance and Clustering  
During the initial experiments, communication was interrupted with the sink nodes and the _Cloud_, which made the use of the mechanism for selecting the leader necessary. The experiments consisted of the repetition of rounds which continued until the total amount of energy in the system was consumed and all the nodes had become inactive. The model was employed 33 times for each value of \(\alpha\) adopted (Equation (5)) and the averages of the number of nodes working on each round are shown in Table 3. The last row of the table refers to the scalability testing of the system, using the best \(\alpha\) value obtained (\(\alpha=0.5\)) but running the simulation with 500 nodes instead of 100.  
As can be seen, due to the characteristics of the simulation, the consumed energy in each round remains almost constant and only has variations with regard to how the consumption is shared between the nodes. Thus, all the performances had the same starting-point when the nodes began to be inactive due to lack of energy, being the only difference the number of nodes that remain active in  
\begin{table}
\begin{tabular}{l r} \hline \hline \(E_{elect}\) & \(5*10^{-8}\) nJ/bit \\ \(e_{fs}\) & \(1*10^{-11}\) \\ \(e_{mp}\) & \(1.3*10^{-15}\) \\ \(E\) & 15,000 J \\ \(E_{f}\) & 241,920 J \\ \(d\) & 87.7 m \\ \(n\) & 100 \\ Voltage and Amperage (idle) & 0.5 A / 5.0 V \\ Leader energy consumption & 50 J \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters [23].  
each round. Hence, \(\alpha\) values = 0.8, 0.9 and especially 0.5 distributed the charge better over the nodes, thus allowing more nodes to remain working in the following rounds. This is an important factor as if there are more active nodes, a greater amount of data can be collected and a larger region included in the monitored area. As a result, it is possible to perform more accurate forecasts or even alert a larger number of people.
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 4 Discussion
### Challenges of the Seasonal Midpoint Method  
Slovenian forests are generally situated in hilly/mountainous landscapes with high cloud cover. This means reduced cloud-free data coverage of different vegetation indices, which leads to difficulties in observing the seasonality in vegetation indices, and thus poor estimates of phenology metrics [53]. Since the SM method is based on the yearly maximum and minimum vegetation indices values as well as the trend over the year, it is very important to reconstruct the seasonal trend of the vegetation growth as reliably as possible. The smoothing algorithm or curve fitting method chosen for reconstructing continuous daily remote sensing data would greatly affect the seasonal pattern, and hence influence the results of the SM method [11; 36] even more when large gaps exist in the time series. In this study, the choice of the double logistic functions is supported by the fact that it proved to describe the vegetation indices well over the growing season, by handling outliers effectively [26]. In the few years where the double logistic function failed to capture the seasonality due to a lot of missing data, for instance, the Gaussian function performed relatively well. Therefore, instead of using a single method as in most previous studies, we used the best fit between a Gaussian function and two double logistic functions for each year, in order to ensure the best fit and guarantee the lowest errors in modelled SOS and EOS.  
The popularity of the SM method is due to its simplicity. Despite its good performance in modelling plant phenology, it is sensitive to several factors that could affect the seasonality reflected by satellite data. Based on the midpoint of the amplitude of one year of remote sensing vegetation indices or GPP, the SM method can be dynamically applied to each year separately, in which case it is very sensitive to partial snow and/or cloud cover, droughts, etc. of that year, introducing large variations in the results of the method. This is the reason for adopting a constant threshold over multiple years [11; 57]. On the other hand, fixed threshold approaches have limitations such as a poor estimation of the phenology of some particular years, and the need to use a given threshold only for a number of years since long time series can show a positive trend in vegetation indices, leading to early SOS estimates in future [57]. Threshold methods often use a constant and the same threshold for defining SOS and EOS. The most common threshold used is 50% of the amplitude of the considered vegetation index over the season. However, [PERSON] et al. [58] found that there is no optimal threshold that works for all plant species. In fact, the threshold for SOS (and EOS) depends on the plant species, the plant community, and the background reflectance properties at a particular site [59; 34]. These support the choice of a tuned threshold for each combination of stations and species in our study, therefore offering the best possible estimates of the SM method across different situations.
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 2 Materials and Methods  
### Study Site  
This pilot study was conducted in Rwanda (Figure 1), a small country (approximately 25,300 km\({}^{2}\)[30]) located in sub-Saharan Africa. Rwanda is characterized by a temperate tropical highland climate, with annual precipitation ranging between 1000 and 1400 mm, depending on the region, and daily temperatures ranging from 15 to 27 \({}^{\circ}\)C.  
Approximately 75% of the total land area in Rwanda is devoted to agriculture [30], which represents 24% of Rwanda's gross domestic product [31]. Fields in Rwanda are small (often \(<\)1 ha) and intercropped, although larger mono-cropped fields (consolidated land use areas) do exist. The crop calendar reflects two main seasons: Season A extends from September through February, and Season B extends from March through June [30]. The starts and ends of the agricultural seasons depend on the type of crop, the region of the country, and the onset of rain. The major crops grown in Rwanda are maize (16%), beans (23%), bananas (19%), and roots and tubers such as cassava (15%), potatoes (4%), and sweet potatoes (7%) [30].
# Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing
## 7 Conclusions  
AQRB integrates a post-quantum, dual blockchain architecture to enable quantum-resistant computation. This architecture adopts SPHINCS+ and CRYSTALS-KYBER as its core cryptographic algorithms, ensuring robust data security against potential quantum attacks. The dual-chain design effectively reduces the burden of full network synchronization, thereby significantly lowering the overall storage requirements across the system. Furthermore, storage costs are dynamically adjusted based on real-time supply and demand conditions. Experimental evaluations demonstrate that the proposed architecture achieves superior efficiency and reduced storage consumption compared to conventional blockchain systems, highlighting its scalability and resource optimization capabilities. In addition, AQRB employs an asynchronous balancing contribution mechanism, wherein only relevant parties participate in each data sharing or acquisition event. This selective involvement enhances consensus efficiency by minimizing unnecessary communication overhead. Users are also required to contribute information before accessing new data and must expend earned reputation points to obtain information. This reputation-based access model contributes to maintaining data security and supports the sustainable operation of the blockchain ecosystem.  
Looking ahead, research will focus on further optimizing the consensus efficiency and scalability of dual blockchain architectures, particularly in high-throughput or real-time applications. In addition, investigating hybrid cryptographic systems that integrate multiple post-quantum algorithms will enhance the system's resilience against diverse quantum attack vectors. To strengthen privacy and data sovereignty, the integration of advanced privacy-preserving technologies such as zero-knowledge proofs, homomorphic encryption, and biometric authentication will be explored. These technologies aim to provide strong security protections without compromising user anonymity, making it feasible to build blockchain systems that can handle both scalability and post-quantum threats in real-world applications.  
Conceptualization, J.-S.L.; Methodology, Y.-Y.F.; Formal analysis, C.-J.C.; Investigation, J.-S.L.; Writing--original draft, Y.-Y.F.; Writing--review & editing, J.-S.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company AAA Security Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results from the Used Geophysical Methods
### Deep Seismic Cross-Sections
#### 4.1.1 Seismic Profile No. 83/85  
This seismic profile shows the deep geological structure of the northern edge of the ESB. The Humenne Unit (the main aquifer), as visible on the cross-section (Figure 5), is covered by sediments of the Central Carpathian Paleogene, while its lateral limitation is tectonic.  
The overburden boundary is highlighted by its lithology, where seismic reflexes show considerable angular discordance. This discordance is related to the hiatus which occurred during the Paleocene as well as the partial emergence of the Humenne Unit during this period. Rough positions of claystones in the lower parts of the Central Carpathian Paleogene and positions of sandstones in the upper parts indicate a monotonic development. This is marked in the cross-section by continuous well-defined lateral reflexes. An interesting visible feature of the cross-section is the lateral transition (from left to right) from a reflective zone into a non-reflective zone. These changes are related to strong tectonic faulting close to the Pieningy Klippen belt (PKB).  
The same faults influenced the Humenne Unit in the basement. In this part, the sediments have chaotic reflexes. Sometimes the reflexes are steeply inclined. The antithetical orientation of these faults is related to the subduction of the Outer Western Carpathians (flysch zone) beneath the Inner Western Carpathians. The antithetical faults are normal, and they behave like half-grabens that contain rotating blocks. As these blocks rotated, they also moved vertically. The Klippen belt forms the northeastern tectonic border of the CCP and the Humenne Mesozoic Unit. The internal structure of the Klippen belt was formed by multi-deformational tectonic phases which mainly occurred during the Cretaceous to Miocene periods. The Humenne Unit located in the basement has distinct reflexes and is geometrically arranged into multiple duplex structures. The shape of these reflexes is not linear, they are significantly bent depending on their proximity to the Klippen belt. Often-occurring reflex bending indicates a significant deformation of the carbonates and the presence of overthrust structures.  
The lower boundary between the Humenne Unit and the Inacovo-Kricevo Unit basement is not clear. The Humenne Unit is overthrusted over the Inacovo-Kricevo Unit (borehole MLS-1). Due to lithological similarities, it is difficult to distinguish between the two units. The lowest unit (Inacovo-Kricevo Unit) is characterized by alternating parallel reflexes with low lateral endurance and low-reflex areas [32,33]. The deepest unit is an unspecific basement marked by inclined reflexes.