esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and discussion
### Wind Tunnel Test
#### 3.1.2 Variation of Droplet under Different Airflow Velocities
Table 4 shows the \(V_{<100}\) (%\({}_{vol}\)) and \(RS\) for each nozzle at six different airflow velocities. It can be seen from Table 4 that as the airflow velocity increased, the measured values of \(V_{<100}\) (%\({}_{vol}\)) of each nozzle showed a trend of decrease. The \(V_{<100}\) (%\({}_{vol}\)) range of each nozzle were 7.8% (CP02), 10.8% (CP03) and 8.4% (CP04). However, the \(RS\) regularity of each type nozzle was not obvious. Under each airflow velocity, the \(RS\) for the CP02 nozzle was in the range of 1.22 (0 m/s) to 1.68 (22.2 m/s); for CP03 nozzle was in the range of 1.29 (27.8 m/s) to 1.54 (0 m/s); and for CP04 nozzle was within the range of 1.20 (27.8 m/s) to 1.62 (0 m/s). In addition, for the three nozzles of the same type, the spray parameters also showed different significant differences under different airflow velocities, which was especially evident on CP02-2, CP03-2, and CP04-2. These were consistent with the droplet size change results in Figure 6. The relevant trend changes can also be explained by the above-mentioned reason analysis. It can also be seen from the above analysis that in selecting the aviation nozzle, the quality of the nozzle should be strictly controlled to reduce the spray deviation and achieve the best spraying effect. When the nozzle is used for a certain period of time, it must be replaced. Otherwise, the spraying performance will become unstable. Figure 6: (a) Variation of \(D_{V0.1}\), \(D_{V0.5}\), and \(D_{V0.9}\) of CP02 nozzle with the airflow velocity; (b) variation of \(D_{V0.1}\), \(D_{V0.5}\), and \(D_{V0.9}\) of CP03 nozzle with the airflow velocity; (c) variation of \(D_{V0.1}\), \(D_{V0.5}\), and \(D_{V0.9}\) of CP04 nozzle with the airflow velocity. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Nozile Type**} & \multicolumn{2}{c}{**0 m/s**} & \multicolumn{2}{c}{**16.7 m/s**} & \multicolumn{2}{c}{**19.4 m/s**} & \multicolumn{2}{c}{**22.2 m/s**} &
## 3 5G HetNet Services, Architecture, and Related Technologies
### 5G Technologies  
The most important enabler technologies for 5G cellular systems are HetNet, mmWave, Massive-MIMO, beamforming, full-duplex (FD), and D2D technologies. These technologies fulfill the requirements of future networks and have achieved high capacity, enhanced data rates, and massive and seamless connectivity of UEs.
## 2 Materials and Methods
### Methods of Signal Analysis
#### 2.2.3 WT
The temporal resolutions of WT can be dynamically adjusted according to the frequency band, thus realizing a higher temporal resolution at the high-frequency band and a higher frequency resolution at the low-frequency band. Furthermore, the base wavelet of WT can be modified, whereas the STFT wavelet type can only be a sinusoidal wave. Therefore, the modified WT, using the appropriate wavelet, performs better than STFT in aperiodic catastrophe signal detection. Capture audio randomly, and transform it into the time-frequency spectrum using STFT and WT, respectively. Figure 4a shows the time-frequency spectrum provided by STFT, and Figure 4b shows that provided by WT. It can be seen that WT retains more details of the signal, and the spectrum is clearer. For example, in Figure 4, the signals at around 50 ms, 200 ms, and 250 ms are so weak that they cannot be heard by humans and are not clear enough for STFT to detect them. However, in the case of WT, it is clear that there were some short-duration signals. Figure 2: Uncertainty principle in time–frequency analysis.
## 2 FSW Experiments Considering Productivity
### The FSW Experimental Results According to the Conditions  
In order to select the optimal process conditions, this study fabricated FSW test pieces under 25 conditions using the rotation speed and traveling speed of the tool as variables and performed a tensile test on each test piece. The tensile test was performed by using an Instron-type tensile tester (SHIMAZU, AG-IS, 5 Ton, Kyoto, Japan) at a cross-head speed of 1 mm/min under room temperature. In addition, we used test pieces that conformed to ASTM-E8 specifications which were electric-discharge machined perpendicular to the welding line, as shown in Figure 5. Figure 6 shows the stress-strain curves of the tensile tests on the test pieces for each condition. The average value of the tensile strength was used for analysis. Except for a few conditions, most of the test pieces exhibited a tensile strength of 120-135 MPa and an elongation of 20%-25%. From those results, we can confirm that the FSW process range to obtain a sound weld for aluminum alloy 6063-T4 extruded plates is relatively wide, and that there is no critical influence within the range of the tool's rotation speed and traveling speed in this study. Based on the results above, the optimal process conditions for the tool's rotation speed and traveling speed were selected as 2000 RPM and 1000 mm/min, considering productivity.  
Figure 4: Configuration of FSW experiment.  
Figure 5: Pieces for uniaxial tensile tests.  
Calculating the Inherent Value under an Actual Welding Process Using a Local Thermomechanical Model
## 3 Results
### The PDI According the Spatial Resolution  
The NIR-red linear regression was obtained to calculate the soil line (Figure 5), and the M value was determined (Table 2) by means of Equation (4) to determine the PDI (perpendicular drought index).  
To validate the PDI, the in situ SM (soil moisture) data measurements every 10 m were compared with the PDI, obtaining the results shown in Figure 6 according to land use. Among these results, correlation is strongest between PDI and soil moisture under bare soil (R2 = 0.5062), followed by pasture and then potatoes.  
[PERSON] et al. [32] state that visible and near infrared spectral data are closely related to soil moisture at a soil depth of 10 cm.  
The results obtained from repeating the same process at 3 m spatial resolution using the Planet Scope images are shown in Table 3. They show that there is a high correlation between the red and the NIR bands on satellite images with a spatial resolution of 300 cm, whereas the correlation between the PDI and soil moisture is lower than the UAV (unmanned aerial vehicle) 4 cm spatial resolution correlation.  
A comparison of the PDI and soil moisture can be influenced by plant albedo and shade. The results shown in Figure 7 clearly demonstrate that potatoes at 4 cm resolution correlate less than bare soil and past  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Land Use** & **NIR – Red Equation** & **M** & **PDI – SM (R2)** \\ \hline Pastures & y = \(-\)0.4276x + 0.5824 & \(-\)0.4276 & 0.4392 \\ \hline Potatoes & y = \(-\)3.9215x + 0.65 & \(-\)3.9215 & 0.002 \\ \hline Bare soil & y = 0.5921x + 0.165 & 0.5921 & 0.5062 \\ \hline \hline \end{tabular}
\end{table}
Table 2: NIR-red linear equations to obtain the M value to calculate the PDI at 4 cm.  
Figure 6: Polynomial linear correlations of NIR-R reflectance at 10,000 random points on the study site according to land use. (**a**) pasture study site, (**b**) potatoes study site, (**c**) bare soil study site. Source: Results of the current research.
## 3 Results and Discussion  
### Fatty Acid Profile and Positional Distribution  
Fat is the most important metabolic component of the human diet, performing many important structural and physiological functions. It is a component of the cell membranes and structures of the entire body; its elements are precursors of hormones and vitamins [14; 16]. The fatty acid profiles of cold-pressed apricot and peach kernel oils are presented in Table 1. Apricot kernel oil A1 contained mainly unsaturated fatty acids in the amount of 92.12%, of which the largest shares were oleic acid--68.77% and linoleic acid--21.94%. The remaining unsaturated acids detected did not exceed 1.5%. Saturated fatty acids accounted for 7.88% and were mainly represented by palmitic acid--6.35% and stearic acid--1.32%. The content of unsaturated fatty acids in apricot kernel oil A2 was 88.69%. The main unsaturated fatty acids were oleic acid (46.06%) and linoleic acid (41.57%). Saturated fatty acids accounted for 11.31% and were mainly represented by palmitic acid in the amount of 7.87% and stearic acid in the amount of 3.08%. The apricot kernel oils differed significantly in the contents of oleic acid and linoleic acid. Oil A1 contained over 20% more oleic acid, while oil A2 contained approximately 20 percentage points more linoleic acid. [PERSON] et al. [30], in cold-pressed apricot kernel oil, determined the content of oleic acid at the level of 70.36%, linoleic acid--22.63%, and palmitoleic acid--0.70%. According to [PERSON] et al. [31], the content of oleic acid in apricot kernel oil was approximately 69%, and that of linoleic acid was 24%. The A1 oil was characterized by a fatty acid profile similar to the oils analyzed in the cited study. However, the A2 oil differed in the contents of oleic acid and linoleic acid compared to the literature data. The genotypic cultivars, ripening stage, and environmental conditions may be the main causes of the differences in fatty acid content among the studied apricot kernel oils. The findings were similar to those reported by [PERSON] et al. [32] and [PERSON] et al. [33]. The peach kernel oils did not differ significantly in the contents of the groups of fatty acids and individual fatty acids (Table 1). Similarly to the apricot kernel oils, unsaturated fatty acids were determined in the highest contents in the peach kernel oils: 91.98% in the P1 oil and 91.92% in the P2 oil. Oleic and linoleic acids were present in the highest amounts in both peach kernel oils: 68.57% of oleic acid and 22.54% of linoleic acid in the P1 oil;
## 3 Results
### Experimental Analysis of Distributed Learning Classification Module  
In order to determine the optimal experimental parameters in this module, we conducted a series of comparative experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise** & **Method** & **MSE** & **SNR** & **PSNR** \\ \hline \multirow{4}{*}{15 dB} & U-NET & \(8.18\times 10^{-5}\) & 18.02 & 38.97 \\  & UNET++ & \(7.40\times 10^{-5}\) & 19.29 & 41.37 \\  & Attention U-Net & \(6.82\times 10^{-5}\) & 22.43 & 43.78 \\  & RSDM & \(4.98\times 10^{-5}\) & 26.16 & 47.68 \\ \hline \multirow{4}{*}{25 dB} & U-NET & \(5.42\times 10^{-4}\) & 17.36 & 30.26 \\  & UNET++ & \(3.32\times 10^{-4}\) & 20.94 & 33.09 \\ \cline{1-1}  & Attention U-Net & \(2.16\times 10^{-4}\) & 21.77 & 37.36 \\ \cline{1-1}  & RSDM & \(8.67\times 10^{-5}\) & 24.51 & 41.65 \\ \hline \multirow{4}{*}{50 dB} & U-NET & \(6.19\times 10^{-4}\) & 16.53 & 26.32 \\  & UNET++ & \(4.32\times 10^{-4}\) & 17.64 & 31.74 \\ \cline{1-1}  & Attention U-Net & \(3.25\times 10^{-5}\) & 19.84 & 35.96 \\ \cline{1-1}  & RSDM & \(1.63\times 10^{-4}\) & 22.74 & 38.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Noise reduction results of the RSDM.
###### Abstract  
This study investigates the sliding wear resistance of the Co-28 Cr-6 Mo alloy manufactured by laser-based powder bed fusion through linear reciprocating sliding wear tests. Varied loads and sliding speeds, in both dry and lubricated contact scenarios, were examined using a hardened steel ball as a counter material. Microhardness profiles were obtained to understand the alloy's mechanical behavior, revealing consistent average microhardness values of 409.67 HV through the thickness and 404.05 HV along the wear track. The coefficient of friction (COF) indicated a decrease in COF with an increase in the applied normal load in both dry and lubricated contacts. On the other hand, the COF increased with higher sliding speeds, and remained stable under lubricated contact but fluctuated in dry conditions over time. Increased applied load and sliding speed correlated with escalated wear rates. Investigation of the impact of different lubricants on tribological properties highlighted synthetic gear oil as the most effective in reducing wear rate among tested lubricants. Wear mechanisms were elucidated through scanning electron microscopy and energy-dispersive X-ray spectroscopy. The presented results can serve as a guide for selecting suitable operating conditions and lubricants to reduce wear in Co-Cr-Mo alloy applications.  
cobalt-chromium-molybdenum; Co-Cr-Mo alloy; selective laser melting; sliding wear; tribology Article
## 7 Conclusions  
We have proposed a one-bit quantization scheme for the distributed detection of an unknown scale parameter in both the ideal and the distortion channel. Our analysis indicates that for the proposed quantization scheme, the optimum quantization thresholds are independent of the unknown variance under \(H_{1}\) with either perfect links or binary symmetric channel between sensors and the FC. The proposed one-bit quantization scheme can achieve the same detection performance as a unquantized detector by slightly increasing the number of sensors.
# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 1 Introduction
for small object detection, its substantial computational cost and parameter count render it unsuitable for resource-constrained SAR applications. RetinaNet achieves comparable inference speeds but requires meticulous tuning of focal loss hyperparameters, exhibiting instability when processing SAR-inherent speckle noise. Although SSD maintains non-negligible throughput, its limited feature pyramid resolution compromises small target detection capabilities. In contrast, the adopted YOLOv8 [45] Nano exhibits superior computational efficiency with a lightweight parameter count, while maintaining competitive performance on the AP_S metric of the COCO dataset [46], making it the optimal choice for this study. Moreover, YOLOv8 excels in multi-scale target detection. It effectively identifies and locates targets of varying sizes, whether they are small objects or large ones occupying most of the image, providing precise detection results. This capability is especially important in complex real-world scenarios, as it can handle targets of various sizes and shapes. Through comparative experiments and ablation studies on the SSDD and SSDv0, we demonstrate the superior performance of ST-YOLOv8 in complex maritime environments. The contributions of this study are as follows:  
1. Introduction of ST-YOLOv8, an advanced vessel detection model tailored for SAR imagery, with innovations in handling multi-scale targets and small object detection. 2. Optimization of detection accuracy, leveraging techniques C_OREPA Model, ASPP Model, and Shuffle Attention Model to improve feature extraction and model robustness. 3. Improve the YOLOv8 loss function by using W-IoU [47] to mitigate the impact of low-quality bounding boxes and enhance detection accuracy. Comprehensive evaluation, through experiments on real-world SAR datasets, demonstrating the effectiveness of the proposed model in complex maritime scenarios. In summary, this study not only advances the field of vessel detection in SAR imagery but also provides a robust solution to the ongoing challenges of multi-scale and small object detection in dynamic maritime environments.
# Dynamic Controllability of Processes without Surprises
## 5 Sudden Termination Constellations and Patterns
### Sudden Termination Constellation and Pattern STP-6  
A _Sudden Termination Pattern STP-6_ (see Figure 9) is defined as follows: A contingent activity \(C\) and a semi-contingent activity \(S\) with constraints \(ubc(C.e,S.e,w)\) and \(ubc(S.s,C.s,v)\) as above are in an STP, if there is no way to schedule the start of \(S\) and the start of \(C\), such that the value for the end of \(S\) can be fixed, when \(S\) starts. This means that for all admissible start times of \(S\) and \(C\) and all possible durations of \(S\) there is a possible duration of \(C\) such that one of the constraints is violated.  
**Definition 12** (Sudden Termination Pattern 6).: _Let \(P\) be a dynamically controllable process. Let \(S\) and \(C\) be activities in \(P\), with the duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(ubc(S.s,C.s,v)\) be constraints in \(P\)._  
\(S\) _and \(C\) are in a sudden termination pattern STP-6 iff \(\forall S.s,C.s\)\(\forall S.d_{min}\leq S.d\leq S.d_{max}\)\(\exists C.d_{min}\leq C.d\leq C.d_{max}:\ S.s+S.d>C.s+C.d+w\), or \(C.s>S.s+v\)._  
In the following we derive conditions to check whether an STP-6 might occur for a given pair of activities \(S\) and \(C\).  
Figure 9: Sudden Termination Pattern 6 (STP-6).  
**Theorem 11**: _Let \(P\) be a dynamically controllable process. Let \(S\) be a semi-contingent activity, and \(C\) be a contingent activity in \(P\), with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(ubc(S.s,C.e,v)\) be constraints in \(P\). A sudden termination of \(S\) cannot occur, iff \(S.d_{min}-v<S.e-C.s\leq C.d_{min}+w\) holds in \(P\)._  
Please refer to Appendix A.5.  
**Theorem 12**: _Let \(P\) be a process. Let \(S\) be a semi-contingent, and \(C\) be a contingent activity in \(P\) with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.s,v)\) be constraints in \(P\). A sudden termination of \(S\) cannot occur, iff the constraints \(lbc(C.s,S.e,S.d_{min}-v)\) and \(ubc(C.s,S.e,C.d_{min}+w)\) hold in \(P\)._  
The constraints express exactly the condition described in Theorem 11. Hence in a process model \(P\) including these constraints \(S\) and \(C\) cannot be in an STP-6.
## 4 Results
### Evaluation of Recommendation Results
#### 4.3.1 Precision of Recommendation Results  
We adopt two commonly used metrics, _Precision_ and _Recall_, to evaluate the accuracy of recommendation results obtained by each recommender method. Precision and Recall are defined as:  
\[\begin{array}{c}Precision=\frac{\sum_{u\in U}[R(u)\cap T(u)]}{|R(u)|}\\ Recall=\frac{\sum_{u\in U}[R(u)\cap T(u)]}{|T(u)|}\end{array}\]  
where \(R(u)\) denotes the recommendation list based on the training dataset, and \(T(u)\) denotes the recommendation list based on the test dataset.  
To evaluate the accuracy of recommendation results obtained by each recommender technique, we set the number of recommendations from top10 to top100.  
Figure 5 shows the comparison of the accuracy of recommendation results generated by each recommender technique.  
We can observe that the accuracy of recommendation results generated by topic-based methods CTR, SVD-LDA, CoAWILDA, AR-LDA, and EUU-CF are very close to each other and are significantly worse than Graph-CNN, HyperCTR, and Topic-HGNN. This is because topic-based methods focus on improving recommendations for items with textual content. They infer the user's interest based on the user's purchase behavior, which is difficult to refine user preferences for different product attributes and capture high-order correlations between users and items. Different from topic-based recommendation models, Graph-CNN, HyperCTR, and Topic-HGNN infer the user's preference from rich user-product interaction information. Although Graph-CNN and HyperCT also produces good accurate recommendations, it performs worse than Topic-HGNN. This is due to the Graph-CNN and HyperCT only focusing on learning user interaction characteristics with products during and after purchase (e.g., purchase and online review) and ignoring the interactive features in the process of users' learning about product information through search queries before they make a purchase (e.g., product information search). However, user association with a product is a coherent process that should not be isolated into different nodes. Our Topic-HGNN integrates a topic model and a hypergraph neural network, which can deal with the interaction behavior of consumers in the whole process of shopping (i.e., searching  
Figure 5: Comparison of recommendation result accuracy: (**a**) precision of top-k items; (**b**) recall of top-k items.  
understanding-purchasing-using). Besides, the Topic-HGNN obtains the convolutional semantic features of users and items, and uses the topic model to obtain the corresponding topic features. The result shows that incorporating the topic information from users and items into a convolutional neural network can effectively represent user preferences and item features, which can significantly improve the accuracy of prediction scores. The result also demonstrates the structural superiority of the searching-scenario oriented hypergraph, which gives the recommender system a chance to identify different semantic facets of the input search phrases.
## 3 Circuit Implementation
### On-Chip Transformer Matching Circuit
As illustrated in the Figure 16, a variable current source passes through the diode-connected NMOS to produce the voltage, with the current being generated by the BandGap circuit. The proportional to absolute temperature (PTAT) current is utilized to compensate for variations in the threshold voltage caused by temperature changes. The relationship between the threshold voltage of the common-source transistor and the temperature variation is demonstrated in Figure 17. To maintain the stability of the bias voltage, the generated voltage is then fed into a voltage buffer. This voltage buffer prevents any fluctuations in the bias voltage, which could otherwise impact the linearity of the power amplifier. Moreover, it ensures the stability of the output impedance of the bias circuit. The gain-bandwidth product (GBW) of the voltage buffer circuit is also meticulously designed to suppress the RF frequency while ensuring the DC voltage output. Figure 16: (a) Schematic of the common-source bias circuit. (b) Schematic of the common-gate bias circuit. The schematic of common-gate bias circuit, illustrated in the Figure 16b, utilizes a variable resistor to generate the required voltage. Similarly, the output of this circuit is connected to a voltage buffer to stabilize the output impedance of the bias circuit. The simulated output impedance of the bias circuit, as displayed in the Figure 18a, indicates that the addition of the voltage buffer results in a consistent output impedance at different bias voltages. Figure 18b shows that bias circuit impedance without a voltage buffer has a large variation at different output dc voltages, which will affect the linearity of the power amplifier (PA). Figure 17: (**a**) Simulated threshold voltage versus temperature. (**b**) Simulated PTAT current versus temperature.