esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Key Features
### UAV Communication
#### 2.2.5 Assistive Communication  
In the future, UAV communications will mainly develop in the direction of high efficiency, intelligence, low cost, and large coverage and will be integrated with popular technologies such as artificial intelligence and blockchain. For example, intelligent communication based on artificial intelligence can dynamically select the optimal communication path, adjust the transmission rate, and adapt to the communication needs in complex environments. In [40], the authors proposed an AI-driven algorithm for the dynamic resource allocation problem. Through real-time learning and optimization, communication resources are allocated to UAVs in different flight missions, thereby achieving more efficient data transmission and low-latency communication. In [41], the authors proposed an energy optimization method based on deep learning and reinforcement learning. It can optimize the flight path and data transmission strategy of a UAV in real time according to factors such as the remaining energy, signal strength, and transmission delay of the sensor node, thereby reducing energy consumption and transmission delay. At the same time, the introduction of blockchain is expected to solve data security and privacy issues in UAV communications. The decentralized and highly secure distributed mechanism provided by blockchain technology eliminates the dependence of UAV networks on a central server, effectively preventing data tampering and information leakage.
## 4 Biological Applications
### Therapeutic Applications
#### 4.2.1 Thermal Treatments
Several bimetallic nanoparticles have been reported as successful magnetic hyperthermia mediators, including CuNi and FeNi [114; 119; 188]. For example, mechanically milled CuNi nanoparticles can be obtained at a size of 10 nm with a T\({}_{\text{c}}\) of 45 \({}^{\circ}\)C. These particles show a significant self-heating effect over 40 \({}^{\circ}\)C, as determined by analysis using a thermogravimetric analyzer-simultaneous differential thermal analysis (TGA-SDTA) technique [119]. Additionally, \(\gamma\)-FeNi nanoparticles with a tunable Curie temperature have also been reported by [PERSON] et al. for use in self-regulated hyperthermia. In their studies, the authors found that the ratio of Fe and Ni led to significant variations in the M\({}_{\text{s}}\) and T\({}_{\text{c}}\), where higher amounts of Fe gave higher values. Moreover, FeNi nanoparticles with extra Mn precursors added can further reduce the T\({}_{\text{c}}\) from 82 \({}^{\circ}\)C to 78 \({}^{\circ}\)C [114].
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 7 Zigbee Network Traffic Dataset and Its Characteristics
### Network Traffic Characteristics
#### 7.2.3 Packet Distribution by Device  
Table 4 shows the number of packets sent directly by each device, excluding retransmissions. Figure 7 illustrates the distribution of packet numbers across different types of devices. The Hue Bridge, from the hub category, generates the most traffic, accounting for 43.4% of the total, as expected due to its routing role in the Zigbee network.  
Most lamps and bulbs contribute 2% to 5% of the traffic. An exception are bulbs from another manufacturer, each responsible for around 10%. This could be due to an implementation of the Zigbee protocol or to their location in the farthest corner of the house, potentially requiring more retries for reliable communication.  
Battery-powered devices (wall switches and a motion sensor) generate the least traffic (0.1% to 0.5%), as they minimize communication to conserve energy.  
Figure 6: Combined figure: (**a–c**) illustrate the graph of the number of packets in the dataset per second, per minute, and each 10 min, respectively.
# Effects of Doped Hole-Transporting Layers on Perovskite Solar Cell Performances
## 2 Materials and Methods  
### Device Fabrication and Stability Measurement  
The 2.5 cm \(\times\) 2.5 cm ITO (17 \(\Omega\) per square) glass was ultrasonically washed with acetone, isopropyl alcohol, and acetone for 10 min each, sequentially. After drying ITO glass, it was surface-treated with UV-O\({}_{3}\) for 40 min, then the prepared PEDOT: PSS (Clevios AI 4083) solution was spin-coated at 4500 rpm for 50 s. For the doping process, 1,8-diiodooctane (DIO, purchased from Sigma-Aldrich, Seoul, Republic of Korea) or 1,8-dichlorooctane (DCO, purchased from Sigma-Aldrich) was added to the PEDOT: PSS solution. The doped PEDOT: PSS solutions were dispersed using ultrasonication for at least 1 h and the doping ratio was 1 wt%, 1.5 wt%, 2 wt%, and 2.5 wt%, respectively. The remaining solvent was dried through thermal treatment in an oven at 120 \({}^{\circ}\)C or through microwave treatment at 200 W for two sets of 10 s (with 10 s of rest between treatment). A perovskite precursor solution was prepared by mixing lead iodide (PbI\({}_{2}\), 99.99%, purchased from TCI, Daegu, Republic of Korea), 290 mg, methyl ammonium iodide (MAI, 99%, purchased from Sigma-Aldrich), 111.3 mg, lead (II) chloride (PbCl\({}_{2}\), 99.999%, purchased from Alfa Aesar, Seoul, Republic of Korea), 19.5 mg, in a solvent mixture of \(N\),\(N\)-dimethyl formamide (DMF, 99.8%, purchased from Sigma-Aldrich), 0.45 mL, and dimethyl sulfoxide (DMSO, 99.9%, purchased from Sigma-Aldrich), 0.05 mL. The mole ratio of PbI\({}_{2}\):PbCl\({}_{2}\) was 9:1 and of [PbI\({}_{2}\) + PbCl\({}_{2}\)]: MAI was 1:1. This perovskite precursor solution was spin-coated on top of the hole transport layer at 1000 rpm for 10 s and then at 4000 rpm for 20 s and then 0.4 mL of diethyl ether (99.7%, purchased from Sigma-Aldrich), which is an anti-solvent, was sprayed and annealed at 100 \({}^{\circ}\)C for 10 min. Phenyl-C\({}_{60}\)-butyric acid methyl ester (PC\({}_{60}\)BM, 98.5%, purchased from Nano-C, Jinju, Republic of Korea) solution, prepared by dissolving 7.5 mg of PC\({}_{60}\)BM in 0.5 mL chlorobenzene (CB, 99.8%, purchased from Sigma-Aldrich) at 40 \({}^{\circ}\)C, was spin-coated to the formed perovskite film at 2000 rpm for 40 s and dried at 80 \({}^{\circ}\)C oven for 5 min. A total of 0.5 mg bathocuproine (BCP, purchased from Luminescence Technology Corp., Taiwan, China) dissolved in 1 mL of ethanol was spin-coated at 2000 rpm for 30 s to form a buffer layer. Finally, a 100 nm thick silver electrode was deposited through a thermal evaporator on top of the BCP layer. The active area of each device was 9.00 mm\({}^{2}\). For post-annealing microwave treatment, a low-powered microwave (at 50 W) was irradiated for 5 s.  
Long-term stability of the device was studied by measuring the LT80 (with the PCE to fall below 80% of the initial value) condition at a relative humidity of 35% without device encapsulation. These experiments were proceeded in two different conditions (i.e., through exposure to 100 mW/cm\({}^{2}\) of solar simulator light and storage in scattered light and in the dark
## 3 Results and Discussion
### Cutting Force  
A comparison of the cutting force in all directions of the two milling cutters cutting TC11 material at cutting distances of 75 m and 225 m under the same cutting parameters is presented in Figure 10.  
When the milling length is 75 m, the difference between the maximum force values of the two milling cutters in the Fx direction is small, but the maximum force values of the ball end milling cutters in the Fy and Fz directions are significantly larger. When the milling length is 225 m, the tool wear is severe, and the two tools will be changed consistently. Although the variations in cutting forces in all directions of the two tools are consistent, a small increase in the cutting force of the revolving cycloid milling cutter can be observed, mainly because of the effective cutting edge length of the revolving cycloid milling cutter being significantly larger than that of the ball end milling cutter. At the same time, small chip thickness and frictional resistance may be generated in the cutting process. The larger working rake angle also effectively lowers the cutting force, contributing to low wear of the tool. At this time, the tool wear of the ball end milling cutter is more serious than that of the revolving cycloid milling cutter, with a significant increase in cutting forces in all directions.  
The ratios of cutting forces under different cutting distances of the two milling cutters are compared in Figure 11. It can be seen from the figure that the variation magnitudes of the tangential force and the axial force of the two milling cutters are remarkably greater than that of the radial force. The ratios of axial force to tangential force are 0.63, 0.53 and 0.71, 0.60, respectively, at milling lengths of 75 m and 225 m. To be specific, the ratio of axial force to tangential force of the revolving cycloid milling cutter is small. With cutting progress, the ratios of axial force to tangential force of the two milling cutters increased. The increase range of the revolving cycloid milling cutter is small, which indicates that the cutting process is stable and the tool wear is small. Further, it can maintain good cutting conditions even if the tool is worn.  
Figure 10: Comparison of the maximum cutting force between the two kinds of cutters.
# An Implementation of the HDBSCAN* Clustering Algorithm
## 2 Background and Literature Review
### HDBSCAN* Implementations
#### 2.3.2 Java HDBSCAN* Implementation  
There is an existing Java Implementation of the HDBSCAN* algorithm. It was developed as part of the framework for cluster analysis and outlier detection, based on the HDBSCAN* algorithm, introduced in [8]. The code for this implementation is carefully reviewed and several tests are performed using different input datasets. In general, the code is well-written, and logically organized. This Java implementation of the HDBSCAN* algorithm will be referred to as the reference implementation throughout this work. Interestingly, this same reference implementation is also used as the reference implementation by the authors of [12] and in the documentation of the hdbscan Python module's GitHub repository. The reference implementation is a good basis for a new Java implementation. However, there are some changes needed and issues which should be addressed. The most significant among them are as follows:  
* There are cases where important results are written to disk and subsequently read in again using customized file offset logic. Machine learning libraries do not commonly require access to the file system to persist results. Further, I/O can lead to performance issues;
* No unit tests. The reference implementation has no self-contained unit tests to verify the algorithm;
* There is some constraints functionality present in the reference implementation which adds complexity to several steps of the algorithm. This feature is not required;
* None of the logic leverages parallelization. There are several blocks of logic with high asymptotic complexity which could cause performance issues.  
Beyond these issues, it should be noted that this reference implementation is not being actively maintained and can not be easily integrated with an existing Java application. This highlights the value of developing a new HDBSCAN* implementation as a feature of an existing machine learning library for Java.
## 2 Materials and Methods
### Extraction of Cereal Proteins  
Protein fractions were extracted according to the method of [PERSON] [20], modified by [PERSON] and [PERSON] [21]. Since albumins/globulins are not relevant for the baking quality [22], this fraction was extracted, but excluded from further analysis, and only gliadins and glutenins are presented and discussed in this paper. For the extraction, 100 mg of the whole flour was first mixed with 1 mL of extraction buffer 1 (0.067 M HKNaPO4, 0.4 M NaCl, pH 7.6) in an overhead shaker (Multi Bio RS-24 from bioSan, Riga, Latvia) at 20 \({}^{\circ}\)C for 5 min, incubated on ice for 10 min with repeated vigorous vortexing, and centrifuged (13,800\(\times\)\(g\), 6 \({}^{\circ}\)C, 10 min). The extraction step was repeated two more times, and supernatants containing albumins and globulins were discarded. The remaining pellet was then extracted three times with 0.5 mL of 70% (\(v/v\)) ethanol at 20 \({}^{\circ}\)C for 5 min in an overhead shaker and centrifuged again at 13,800\(\times\)\(g\) (6 \({}^{\circ}\)C, 10 min) to yield the gliadin fraction. After a washing step with 1 mL of dH2O and centrifugation (13,800\(\times\)\(g\), 6 \({}^{\circ}\)C, 5 min), the glutenin fraction was extracted three times using 0.8 mL of extraction buffer 2 [2 M urea, 1% (\(w/v\)) dithiothereitol, 50% (\(v/v\)) 2-propanol, 0.05 M Tris, pH 7.5] for 5 min in an overhead shaker at 20 \({}^{\circ}\)C, followed by incubation at 60 \({}^{\circ}\)C for 10 min, cooling to room temperature, and centrifugation at 13,800\(\times\)\(g\) (6 \({}^{\circ}\)C, 10 min). All samples were frozen at \(-\)20 \({}^{\circ}\)C for later use. Two independent extractions were conducted for each sample.
## 4 Methodology
### Arithmetic Block
#### 4.1.2 16-Bit Ripple Carry Subtractor  
Binary subtraction is the binary addition of a negative number. Hence, the full adder module can still be used to create the binary subtraction operation with a few changes. Binary subtraction is performed by 2' complementing the \(B\) bit and adding them to the \(A\) bit as illustrated in Figure 5. The \(M\) line here acts as the control line. A value of 1 is given from the \(M\) line to each EXOR gate and as the input to Carry in (\(C_{in}\)) for the full adder block. The ripple carry subtractor can be designed to accept more than 1 bit parallelly to create a bit wide array subtractor. So this means that it could also be used for making a 16-bit carry subtractor. These blocks with a full adder and EXOR cascade together, forming a 16-bit ripple-carry subtractor [22]. The control line passes '1' as both the Carry in and each existing EXOR gate, which performs the EXOR operation with each of the \(B\) bits (\(B[1]\) to \(B[16]\)). The full adder block adds the input bits and gives out SUM \(S\) parallelly for each bit (\(S[1]\) to \(S[16]\)). The overflow is carried forward to the adjacent block as Carry in, so the Carry out for the \(B[1]\) and \(A[1]\) would be Carry in for the second full adder block. This process carries on till the final block, where the Carry is finally given out as Carry out (\(C_{out}\)). The acquired sum bits would give the difference between the 16-bit \(A\) array and the 16-bit \(B\) array. This same block can also be used as a ripple carry full adder by giving '0' as input through the control line \(M\). Figures 6 and 7 show the circuit and waveform results from our subtractor module.  
Figure 6, similar to Figure 3, displays a ripple carry structure, but this time with an additional XOR gate at the second input, this XOR gate is connected to input carry and a second bit to perform 2's complement. The four visible blocks are 4-bit ripple carry subtractor architectures with an AND gate for the last two carries to generate an overflow output. Figure 7 demonstrates the waveform that this module generates on simulation. The first two inputs are at the top; the output and overflow are the bottom two waveforms.  
Figure 5: The 16-bit ripple carry full subtractor block diagram.
# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 3 Morphological Semantics Memory Model for Visual Stimuli
### Working Memory: Memory Operation by Consolidation and Reconsolidation  
The second function of working memory is registering an SDR in case of a new input as a new memory pattern or recalling a stored memory pattern and updating it by fusion with the newly input SDR. Here, the registration process mimics the concept of consolidation in cognitive psychology and updating process mimics reconsolidation in cognitive psychology [30].  
As shown in Figure 8, if there is no registered memory pattern, an input SDR is registered as a new memory pattern. However, when there are registered memory patterns, a similarity comparison between the input SDR and stored memory patterns is performed. At this time, the similarity is measured by the active bit overlap ratio between input SDR and stored memory patterns. If the active bit overlap ratio does not exceed a predefined threshold, the input SDR is also registered as a new pattern. In the current implementation, the newly registered memory pattern is just input SDR itself.  
In other cases, if the active bit overlap ratio exceeds a predefined threshold, the input SDR and the stored memory pattern which has the highest overlap ratio make superposition SDR. Then, this superposition SDR is stored as an updated memory pattern.  
There are things to consider when recalling the stored memory pattern. When a memory pattern is firstly registered, it is an SDR itself which has active bit intensity 1. However, as the input SDR and the stored memory pattern overlap through the update process, the active bit intensity of the stored memory pattern gradually increases. As a result, memory patterns with many updates have a wide distribution of active bits, and each active bit intensity is different. Therefore, it is necessary to determine at what level  
Figure 11: Processes for spatial pooling.  
the stored memory pattern will be recalled in the process of loading the stored memory pattern to measure similarity with input SDR.  
Figure 12 represents this concept as a picture, and the recall depth is automatically adjusted to maintain the sparsity of SDR when it is recalled.  
The advantage of this structure is that we can make comparisons with different precision of recall. For example, it provides a structure where you can ask questions such as 'Is it a little similar?', 'Is it very similar?' and 'Is it the same?'
## 5 Comparative Analysis of Theoretical and Experimental Data  
For experimental verification of the formulas obtained, the bearing capacity of a single throttling bearing without microgrooves was found, and its compliance, stiffness, and load capacity were compared with the data of [31]. In addition, for a correct comparison, the parameter of the relative spreading of the pressurization lines \(\lambda_{1}\) = 0.5 was adopted. Comparative data are shown in Table 1.  
As can be seen from the table, on the whole, the experimental and theoretical data are in satisfactory agreement. The theoretical bearing capacity is slightly higher than the experimental data. The reason is probably that the theoretical model is based on the assumption that discrete feeders can be replaced with a continuous pressurization line. As can be seen, this assumption gives an error of no more than 10%, which can be considered a completely satisfactory agreement between theory and experiment. This tendency is also observed when comparing the stiffness characteristics for \(L\) = 1.5. For \(L\) = 1.0, on the contrary, the theoretical stiffness was less than the experimental one.  
Figure 10 shows the experimental values of \(W\) (points) obtained by [PERSON] [32] for an aerostatic bearing with two sets of orifices of the \"simple diaphragm\" type for \(h_{0}\) = 33 \(\times\) 10\({}^{-6}\) mm, \(n\) = 6, \(d\) = 0.56 mm, \(L\) = 1.5, and \(L_{1}\) = 0.  
Dependencies 1 and 4 were obtained by [PERSON] [33], 2 and 5 are points of the [PERSON] experiment [32], and 3 and 6 are calculated by the formula  
\[W=\frac{2(P_{s}-1)\varepsilon}{\pi K}.\]  
The former corresponds to \(P_{s}\) = 6, the latter \(P_{s}\) = 2.3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(L\)(\(l\)/\(r_{0}\)), & \(\mathbf{Supply Pressure}\) & \multicolumn{2}{c}{**Load Capacity (N)**} & \multicolumn{2}{c}{**Stiffness (N\(\cdot\)m\({}^{-1}\))**} \\ \cline{2-6} \(h_{0}\) (mm) & \(p_{s}\) (MPa) & **Experiment** & **Theory** & **Experiment** & **Theory** \\ \hline \multirow{2}{*}{1.5, 0.0225} & 0.3 & 64 & 69 & \(1.0\times 10^{7}\) & \(1.2\times 10^{7}\) \\ \cline{2-6}  & 0.5 & 105 & 112 & \(1.5\times 10^{7}\) & \(1.6\times 10^{7}\) \\ \hline \multirow{2}{*}{1.0, 0.015} & 0.3 & 28 & 31 & \(1.5\times 10^{7}\) & \(1.4\times 10^{7}\) \\ \cline{2-6}  & 0.5 & 72 & 76 & \(2.0\times 10^{7}\) & \(1.8\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative bearing characteristics at relative eccentricity \(\varepsilon\) = 0.5.  
Figure 10: Comparison of the load characteristics of a double-row bearing.  
Linear dependences 3 and 6, presented in Figure 10, have a satisfactory coincidence with the compared data in the region of small eccentricities \(\varepsilon\).
# Assistance of a Person with Muscular Weakness Using a Joint-Torque-Assisting Exoskeletal Robot
## 3 Real-Time Gait Detection Method  
### Classification of Gait Phases  
Human walking can be regarded as a periodic movement composed of several phases. Using insole sensors with two sensors on a foot (i.e., toes area and heel area), four phases can be detected; heel strike, mid stance, terminal stance, and swing. However, if the leg muscles or joints are weakened or damaged, the sub-phases of the stance phase (i.e., heel strike, mid stance, and terminal stance) may not be obvious in gait analysis. For example, patients with equinus gait (i.e., tiptoe gait) do not touch their heel to the ground during walking. In this case, a gait can be classified into two main stages: (1) a stance phase in which any part of the foot touches the ground and (2) a swing phase in which the foot is completely off the ground. Figure 5 shows the simplified gait phases of a leg. Considering the phase of the opposite leg, the stance phase of the leg can be divided into the first double limb support phase (DS1), single limb support phase, and the second double limb support phase (DS2). Therefore, the gait can be classified into four index phases as follows.  
* Gait index 1: Double limb support with the right leg forwarded (DS1)
* Gait index 2: Single limb support of the right leg (left swing)
* Gait index 3: Double limb support with the left leg forwarded (DS2)
* Gait index 4: Single limb support of the left leg (right swing)  
The double limb support phase of the right leg includes the stance phase of the left leg, and the single leg support phase of the right leg means the swing phase of the left leg. Therefore, the proposed four-level classification covers the entire gait stage of both legs. People with paretic gait generally have longer double limb support times than healthy people. Except for those who are severely paralyzed or lack muscle strength enough to be unable to lift their legs at all, people walk in sequential phases when walking forward.