esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Definitions
### Autonomous Underwater Vehicles (AUVs)  
The autonomous underwater vehicle (AUV) is a robot that travels underwater without the assistance of an operator. Underwater projects and mapping missions using AUVs can help commercial and recreational vessels detect and map submerged wrecks, rocks, and obstructions that may present navigational hazards [23]. Deep-sea data collection is a unique application of delay-tolerant networks (DTNs). There are several reasons for this, including the need for timely data reporting and the inevitable delay in acoustic transmissions in the ocean [24]. A AUV surfaces frequently to transmit the data obtained from sensors--in two-dimensional or three-dimensional search space--to surface stations. Whenever an AUV ascends to the surface to establish communication with the surface stations, an additional delay occurs in the data transmission process. In [25], the authors show that by optimizing the number and location of AUV resurfacing events, it is possible to minimize the average data-reporting delay.  
Additionally, underwater wireless sensor networks consume a significant amount of energy and are unbalanced, which poses a substantial challenge for AUVs. AUVs receive data directly from nodes located near the trajectory, while those farther away send data to their neighbors. In order to overcome this problem, a trajectory adjustment mechanism can be used, but this would result in nodes near the trajectory of the AUV consuming energy more rapidly and dying earlier than expected. In order to adjust the trajectory, a data collection scheme based on the location prediction of the AUV is incorporated in [26], wherein the nodes follow a predefined trajectory in order to predict the location of the AUV. The nodes that are located near the trajectory of the AUV send data to the AUV after collecting it from other nodes. In order to minimize the effects of the harsh underwater environment, a reliable mechanism has been developed to improve the performance of the network and to balance the load on the nodes in order to extend the service life of the network [26].
## 3 Results
### Diffusion Barrier  
Lead-free solder alloys are often used to join an electrode and a TE material. It has been confirmed that an interfacial reaction exists between the solder and the TE material, leading to the creation of contact resistance and the possible degradation of TEG module performance [50]. In practical TEG modules, a diffusion barrier, usually in \(\mu\)m thickness, is inserted in between the solder and the TE material via either ultra-high-vacuum (UHV) radio frequency (RF) sputtering, spark plasma sintering, or electroplating procedures to inhibit the formation of intermetallic compounds at the same time as ensuring a good electrical bond. Its analysis is often conducted via SEM. The effect of diffusion layers, such Au, Pt, Ti, Ni, Co-P, and Ni-P on TEG modules has been investigated [50; 51; 52; 53]. If an appropriate thickness is used, the diffusion layer can enhance the efficiency of a module. To avoid having a mismatch at the interface during expansion, the thermal coefficient of the diffusion layer should be similar to that of the TE material and the electrode. Bilayer metallization [54] and multilayer metallization [55] prove effective in lowering electrical contact resistance, providing thermal coefficient matching, and serve as a good diffusion barrier.
# The Development of an Optimized Impact Pad for a Six-Strand Tundish Using CFD Simulations
## 4 Evaluation and Discussion
### Heat Transfer  
Section 3.3 summarizes the input parameters of the incoming molten steel used in the heat transfer analysis. Figure 19 illustrates the temperature distribution within selected cross-sectional planes for the various configurations evaluated. When comparing the SP-K2 configuration with the reference K configuration, it was noted that the highest temperature zones were located within the inlet zone of the tundish. This observation suggests enhanced thermal homogenization as a result of the modified flow patterns induced by the SPHERIC-K2 and SPHERIC-K4 impact pad designs.  
Additionally, the SP-K configurations demonstrated more uniform temperature distribution in areas located further away from the impact pad, indicating improved dispersion of thermal energy throughout the tundish volume. In contrast, the reference KTHE/C impact pad configuration exhibited localized high temperatures in the upper rear and frontal regions near the ladle shroud, which correlates with a plug flow regime. This flow behavior suggests less effective mixing between the incoming molten steel and the bulk steel within the tundish.  
Figure 19: Steel temperature distribution in the planes of the tundish with the K (**a**), SP-K2 (**b**), and SP-K4 (**c**) configurations.
## 3 The Methodology
### Conceptual Model  
The architecture of the proposed interoperable AI-IoMT model is presented in Figure 2. The model comprises three layers that explain the overall working of IoMT applications. Figure 1 shows that authentication in the IoT environment is based on Secretkey, and the clustering of sensed data is performed using the zSlices Triune Fuzzy Sets algorithm. Additionally, CH selection is performed using Hidden Markov Model (HMM), and the incorrect data are removed using [PERSON]'s Entropy-based Ten-Fold Cross Entropy Verification, whereas the correctly sensed data packets are encrypted using Twine-LiteNet. Finally, an optimal route is selected among the CHs using the Search and Rescue Optimization algorithm for enabling secure and efficient data transmission in the IoT network.  
The proposed method incorporates various security mechanisms to protect patient data and maintain privacy; these are Twine-LiteNet-based encryption and [PERSON]'s Entropy-based Ten-Fold Cross Entropy Verification (TCEV) and may be employed to validate data integrity and reduce the risk of data tampering.
## 3 Experiment
### Experimental Conditions  
The recommended molding ranges for resin and mold temperatures were defined by considering the resin manufacturer's recommendations and the property database of LUPOL GP1007F within Moldflow Insight 2023 (Autodesk, San Rafael, CA, USA). These temperature ranges were categorized into three levels, as shown in Table 1. The packing pressure and time were determined based on preliminary experiments to establish suitable process ranges for the product, and these were also divided into three levels and applied in actual molding experiments, as shown in Table 1. The injection time and cooling time were obtained through CAE analysis using Moldflow Insight 2023 (Autodesk, USA), and these conditions were also divided into three levels, similarly to other process parameters, for application in molding experiments. Based on the levels of process conditions in Table 1, 27 process conditions were generated using the L27 orthogonal array design. Additionally, 23 process conditions were created by randomly selecting values within the minimum and maximum ranges of the process conditions in Table 1 as shown in Table 1 (Appendix A). In total, injection molding experiments were conducted for 50 sets of process conditions, collecting data on the mass, diameter, and height of the injection-molded products to construct the dataset used for training the ANN. The mass of the injection-molded product was measured using the CUX420H (CAS, Yangju-si, Gyeonggi-do, Republic of Korea) electronic scale. The mass measurements were conducted under ambient conditions, using a case cover during the process to minimize the influence of atmospheric movement. The measurements were taken to two decimal places and the average of five measurements was  
Figure 5: Structure: (**a**) mold; (**b**) bowl product.  
used. The diameter of the product was evaluated using the average values measured at six points, as shown in Figure 6a. Diameter measurement was performed using Datastar200 (RAM OPTICAL INSTRUMENT, Westlake, OH, USA), a non-contact optical measurement device. The molded part was placed on a properly leveled measuring table and the outline of the part was measured with the device for the diameter shown as Figure 6b. The height of the product was determined using the average values measured at four points shown in Figure 7 with the digimatic height gauge (Mitutoyo, Kawasaki, Kanagawa, Japan). The height was measured by attaching a height gauge to a vertical rod and placing the product between the gauge and a leveled measuring table. For height, measurements of the points in Figure 7 were measured five times each, and the average value was used.  
To resolve this issue, the saturation problem was addressed by implementing min-max normalization within the range of 0.1 to 0.9, as outlined in Equation (4):  
\[x_{i}^{\prime}=(0.9-0.1)\times\left\{\frac{(x_{i}-Min.\ X)}{(Max.\ X-Min.\ X)} \right\}+0.1,\ \ x_{i}^{\prime}\in X \tag{4}\]
## 4 Finite Element Analysis
### Model Validation  
The finite element model was verified using the test results of the failure mode and axial load-displacement curve. The comparison of axial load-displacement curves for each specimen is presented in Figure 4, while Table 4 displays the comparison of ultimate bearing capacity. The axial load-displacement curve of the simulated circular-end aluminum tube concrete column exhibited a close resemblance to the experimental results, with the initial stiffness of the finite element results being relatively higher. This disparity could be attributed to the presence of a gap between the specimen and the loading equipment, resulting in virtual displacement during the test and subsequent reduction of the observed stiffness. However, the overall trend of the curve was consistent with the bearing capacity; the average ratio between the simulated and experimental values of the ultimate load was 0.985, with a negligible error of 1.5%.  
Figure 10 presents a comparative analysis of typical failure modes observed in the specimens. The figure illustrated that the aluminum tube exhibited bulging rings of varying heights and underwent oblique shear failure. Upon examining the internal concrete compression damage cloud map, a distinct V-shaped compression collapse could be observed in the area of the bulging rings, which aligned with the experimental findings. The validated finite element model of the circular-end aluminum tube concrete column was deemed reliable for conducting internal stress analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen No.** & \(\textbf{P}_{\textbf{u}}\textbf{/}\textbf{k}\textbf{N}\) & \(\textbf{P}_{\textbf{c}}\textbf{/}\textbf{k}\textbf{N}\) & \(\textbf{P}_{\textbf{u}}\textbf{/}\textbf{P}_{\textbf{c}}\) \\ \hline CA100-0 & 238.4 & 240.2 & 0.99 \\ CA115-0 & 295.4 & 348.9 & 0.85 \\ CA120-0 & 103.2 & 107.2 & 0.96 \\ CA130-0 & 365.6 & 361.7 & 1.01 \\ \hline FE-\(f_{0.2}\) = 300 MPa & 297.4 & 288.6 & 1.03 \\ FE-\(f_{0.2}\) = 400 MPa & 342.5 & 337.1 & 1.02 \\ FE-\(f_{0.2}\) = 600 MPa & 402.2 & 394.2 & 1.02 \\ FE-C40 & 274.5 & 266.4 & 1.03 \\ FE-C50 & 276.9 & 269.1 & 1.03 \\ FE-C60 & 305.0 & 299.5 & 1.02 \\ \hline Average & & & 0.996 \\ Standard deviation & & & 0.056 \\ Coefficient of variation & & & 0.056 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of calculation results.  
Figure 10: Comparison of failure modes.
# Intelligent Compaction Measurement Value in Variability Control of Subgrade Compaction Quality
###### Abstract  
Intelligent compaction (IC) is an innovative and modified technology used for quality control in subgrade digital construction. However, current intelligent compaction measurement values (ICMVs) cannot provide accurate measurement of the filling layer when the strength of the underlying layer is relatively high. Experimental field tests conducted in the cut to fill subgrade were performed to collect and analyze the variability of ICMV called the vibration modulus (\(E_{\text{rel}}\)). Furthermore, a new ICMV called the modulus of vibration compaction (\(E_{\text{cv}}\)) that could remove the interference of the underlying layer and reveal the actual compaction state of filling layer is presented based on the theoretical analysis and numerical simulation method. It was also extracted to study the variability of the filling layer's compaction state. The results of the above research indicate the influence of the underlying layer's stiffness on overall compaction quality is remarkable. It was found that it is more likely to achieve the variability control of the compaction state of the filling layer by a new ICMV called \(E_{\text{cv}}\). The measured data, improved approaches, and associated conclusions that are presented provide valuable information for researchers and employees considering the use of IC technology.  
intelligent compaction; subgrade digital construction; cut to fill subgrade; numerical simulation; variability control +
Footnote †: journal: Applied sciences
# A Blockchain and IoT-Enabled Framework for Ethical and Secure Coffee Supply Chains
## 1 Introduction
### The Need for Blockchain and IoT in the Coffee Supply Chain
#### 1.3.1. Blockchain for Immutable and Transparent Record-Keeping  
Blockchain technology provides a decentralized and append-only ledger, guaranteeing that every transaction or data entry is permanently recorded and resistant to tampering [13]. In the context of coffee supply chains, this allows critical events such as harvest registration, processing milestones, quality assessments, and shipment details to be captured in a format that is verifiable by all stakeholders. Because blockchain records are immutable, they cannot be modified or deleted after submission. This feature is essential for maintaining the integrity of claims regarding origin, certifications, and trade documentation.
###### Abstract  
Antenna arrays with adaptive filtering can protect the integrity and functionality of global navigation satellite system (GNSS) receivers against interference. However, a major problem with existing adaptive array processing algorithms is that they cause phase distortions and introduce bias errors into the carrier phase measurement, limiting high-precision applications. In this paper, a robust phase compensation technique is proposed to reduce the phase distortion. First, a phase bias detection method is developed to trigger the phase compensation technique. Then, the phase bias is estimated using a robust estimation method and compensated for in the GNSS receiver. The proposed technique operates in real time and causes no processing delay, while requiring only a minor modification to existing GNSS receivers. This technique is applied to the power inversion adaptive antenna, and can also be extended to a wide variety of adaptive antennas. The simulation experiments verify the applicability of the proposed technique and also confirm its superiority over existing techniques.  
adaptive array processing; anti-jamming; antenna array; GNSS receiver; phase distortion 1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], H. Anti-Jamming GNSS Antenna Array Receiver with Reduced Phase Distortions Using a Robust Phase Compensation Technique. _Remote Sens._**2023**, 15, 4344. [[https://doi.org/10.3390/rs15174344](https://doi.org/10.3390/rs15174344)]([https://doi.org/10.3390/rs15174344](https://doi.org/10.3390/rs15174344))
## 2 Materials and Methods
### Clean Air Delivery Rate (CADR) Calculations  
The term \"clean air delivery rate\" (CADR) refers to the amount of particulate-free air that a domestic electric room air cleaner with a cord connection can deliver. In technical terms, CADR is the product of the test chamber's volume expressed in cubic meters using Equation (15) and the rate of particulate contaminant reduction in the test chamber during operation minus the rate of natural decay during nonoperation. Every unit's performance as a whole system for every kind of particulate pollution, including dust, aerosols, tobacco smoke, and others, is measured by CADR. There is no linear correlation between CADR and air movement or any specific particle removal methodology's features [18].  
After the randomly distributed particles were injected into the room, particle movement tracking calculations using DPM were performed with 0.1 s time intervals until all the particles were removed from the indoor air. Calculated data were used to obtain the CADR value for each case considered in the study. The clean air delivery rate of an air cleaner was calculated through Equation (15).  
\[\text{CADR}=\text{V(k}_{\text{e}}-\text{k}_{\text{n}}) \tag{15}\]  
In this equation, V represents the room volume, \(\text{k}_{\text{n}}\) the natural decay rate, and \(\text{k}_{\text{e}}\) the total decay rate including tracking by walls and the air cleaner outlet. The value of \(\text{k}_{\text{e}}\) was calculated using Equation (16), using the concentration change in the room:  
\[C(t)=C_{i}e^{-k_{\text{e}}t} \tag{16}\]  
[PERSON] et al. [49] performed 15 different measurements for the \(\text{k}_{\text{n}}\) value in a room with a volume of 70 m\({}^{3}\). Within the results of these measurements, the mean value of \(\text{k}_{\text{n}}\) was 0.00531 min\({}^{-1}\). The \(\text{k}_{\text{n}}\) value for PM2.5 was estimated at 0.0057 1/min\({}^{-1}\) by [PERSON] et al. [50], while [PERSON] et al. [51] took \(\text{k}_{\text{n}}\) as 0.00667 min\({}^{-1}\) in their VOC measurements. In the present study, \(\text{k}_{\text{n}}\) was assumed to be 0.00531 min\({}^{-1}\), that is, the average value of [PERSON] et al. [49].  
Finally, the collection efficiency of the air cleaning device was calculated by using Equation (17).  
\[\eta=\text{CADR/Q} \tag{17}\]  
In this equation, \(\eta\) and Q (m\({}^{3}\)/h) represent the collection efficiency and the rated volumetric flow rate of the air cleaner device, respectively.  
By using Equation (16), the time interval required to reduce the particle concentration to any desired rate can be calculated as in Equation (18):  
\[\Delta\text{t}=-\text{ln}(\text{C}^{*})/\text{k}_{\text{e}} \tag{18}\]  
\[C^{*}=\frac{C(t)}{C_{i}} \tag{19}\]  
where C\({}^{*}\) is the particle concentration ratio at time t. For example, to find the time required to clean 90% of the particles, (\(\Delta\text{t}_{\text{10\%}}\)), in the room, the value of C\({}^{*}\) should be taken as 0.1.
# Contemporary Approaches in Evolving Language Models
## 3 Neural Network Models
### Word Representation
#### 3.1.3 FastText  
Word-embedding techniques like word2 vec and GloVe offer varied vector representations for words in a vocabulary but fall short at capturing a language's internal structure; this is particularly noticeable in morphologically rich languages in which they overlook syntactic connections between words. Improving vector representations for linguistically complex languages becomes feasible by incorporating character-level information.  
To enhance the quality of vector representations for morphologically rich languages, the FastText approach [23] was introduced. FastText excels in its training speed, outperforming previous methods in efficiency and handling out-of-vocabulary (OOV) words. FastText introduces character n-gram embeddings, representing words as the average of these embeddings. Unlike the Word2 Vec model, which emphasizes embedding entire words, FastText delves into the word structure at the character n-gram level. Both Word2 Vec and FastText leverage the CBOW and Skip-gram methods for vector computation.  
FastText can handle words not in the vocabulary, generating embeddings for OOV words by embedding their character n-grams. In FastText, each word is depicted as the average vector representation of its character n-grams and the word itself. For example, considering the word \"apple\" with \(n=3\), its representation comprises the character n-grams <ap, app, ppl, ple, le> and <apple>. Consequently, the word embedding for \"apple\" is determined as the cumulative vector representation of its character n-grams and the word.  
It was presented that fastText has various scoring functions to retain subword information. For a given word \(w\), the set of n-grams present in \(w\) is denoted as \(T_{w}\subset\{1\),..., \(T\}\), where \(T\) represents the n-grams' dictionary size. A vector representation \(V\) is assigned to each n-gram n and context word (\(C_{w}\)) and vector (\(C_{v}\)). Consequently, the derived scoring function is defined as follows [24]:  
\[F(w,\ C_{w})=\sum_{n\ \in\ T_{w}}V^{T}C_{v}\]
## 3 Summary of the Considered Mobility Models and Routing Protocols  
### The Mobility Models  
#### 3.1.1 The Random Waypoint Model  
The RWP mobility model [2] is frequently used for simulations in MANET, mainly because of its relative simplicity and wide availability in simulators, like NS-2 [32], NS-3 [33] and OPNET [34]. It works as follows. A node randomly picks a location within the simulation area and moves to that location in a straight line, using a randomly-chosen speed. Upon arrival at that location, the node pauses and picks another location. However, the node is subject to sudden stops, sudden accelerations and sudden speed changes. The same applies to direction, where a node can suddenly make a 180\({}^{\circ}\) turn. Figure 1 shows the simulation trajectory trace of a UAV under RWP in a 1000 m \(\times\) 1000 m area.  
As mentioned earlier, airborne vehicles have mechanical and aerodynamic constraints that prevent them from making sharp turns like the ones shown in Figure 1. Obviously, despite the widespread usage of this mobility model, it is not very realistic. Therefore, it can lead to deceiving simulation results when used to evaluate the performance of routing protocols.
## 5 Results and Discussion
### Effect of Back-Rake Angle  
The back-rake angle is an important parameter of the PDC bit [22]. A reasonable back-rake angle not only reduces the vibration of the cutter, but also prolongs the bit life. To investigate the effect of back-rake angles of the cutter, five different back-rake angles are respectively set as \(5^{\circ}\), \(10^{\circ}\), \(15^{\circ}\), \(20^{\circ}\), \(25^{\circ}\) to carry out the numerical simulation under three cutting depth (1.0 mm,1.5 mm,2.0 mm). Effect of different back-rake angles on rock breaking is exhibited in Figure 12. Firstly, the axial force is larger than the tangential force. Secondly, the axial and tangential force increase gradually when the back-rake angle increases from \(5^{\circ}\) to \(25^{\circ}\) under different cutting depths. Meanwhile, the amplitude of cutting forces' fluctuation gradually increases, indicating that the cutting stability gradually deteriorates, as shown in Figure 12a,b. Finally, MSE gradually increases with the increasing of back-rake angle, which means that the rock breaking efficiency narrows down (Figure 12c).  
In order to further explore the effect of the back-rake angle, the plastic strain of the rock was analyzed, combined with Figure 13 and Equation (3). It can be seen that both the distribution area and the peak of the plastic deformation enlarge with the increase of back-rake angle. This might be attributed to the fact that the actual back-rake angle and the side-rake angle become larger with the increase of back-rake angle. Then, the squeeze effect of the rock in frontal of working faces is enhanced. As a result, the plastic strain becomes larger.  
Figure 11: Major Principal Stress in rock-cutting process: (**a**) Conventional cutter; (**b**) 3-RDE cutter.  
Figure 10: Von Mises of rock under the action of (**a**) Conventional cutter and (**b**) 3-RDE cutter.  
The above analysis shows that a small back-rake angle is beneficial to generate a large stress concentration in the cutting surface, making the 3-RDE cutter easier to penetrate into the formation and improving the cutting stability and rock breaking efficiency.