esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Quantum Machine Learning Research in China
## 5 Challenges and Prospects
### Challenges
#### 5.1.3 Barren Plateaus  
The so-called \"barren plateaus\" problem in QML refers to the phenomenon that in quantum circuits with randomly initialized parameters, the gradient of the objective loss function decreases exponentially with the increase in qubit number [50; 104]. The vanishing of gradient means the QML model becomes frozen and the optimization training process can no longer continue. Although it has been demonstrated that the barren plateau problem would not appear in QCNN models [105], this problem widely exists in those QNNs without convolutional structures [49; 82]. Some studies have tried to provide solutions by designing special algorithm models, such as variational shadow quantum circuits [85], QGAN model based on Renyi divergences [62], and variational QNN model based on PSO strategy [44].
# A TSV-Structured Room Temperature p-Type TiO\({}_{2}\) Nitric Oxide Gas Sensor
## 4 Conclusions  
This study uses the Bosch process to form a TSV structure and then uses the ALD process to fabricate a TSV-structured TiO\({}_{2}\) gas sensor. The FIB images show that the TiO\({}_{2}\) sensing film uniformly covers the TSV structure. The Hall measurement confirms that the TiO\({}_{2}\) sensing film is a p-type MOS. The respective sensor response of the TSV-structured TiO\({}_{2}\) gas sensor to NO at room temperature is 6.4%, 8.2%, 10.9%, 16.7%, and 21.3% for NO concentrations of 0.5, 1, 2, 4, and 8 ppm. In terms of the TSV-structured RT TiO\({}_{2}\) NO gas sensor's stability and reproducibility, the sensor response is 16.5% on average, with an inaccuracy of \(<\)\(\pm\)0.5%. In terms of selective gas measurement, the response of the sensor to NO is 24.4%. The sensor is only very slightly responsive to gases, such as SO\({}_{2}\), CO, H\({}_{2}\)S, and CO\({}_{2}\). In other words, a RT TiO\({}_{2}\) gas sensor with a TSV structure exhibits good stability, reversibility, and selectivity to NO gas.  
Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology, Y.-M.Y.; validation, [PERSON]-M.Y., [PERSON] and [PERSON]; formal analysis, T.-J.H.; investigation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]-[PERSON]; writing--original draft preparation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--review and editing, T.-J.H.; visualization, T.-J.H.; supervision, T.-J.H.; project administration, T.-J.H.; funding acquisition, T.-J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSTC of Taiwan grant number 110-2221-E-992-047, 111-2221-E-992-029-MY3, 111-2218-E-992-001-MBK, 111-2218-E-006-011-MBK.  
Not applicable.  
The authors thank the National Science and Technology Council of Taiwan for financial support and the Taiwanese Semiconductor Research Institute.  
The authors declare no conflict of interest.  
Figure 9: Gas measurements for a TSV-structured TiO\({}_{2}\) gas sensor.
## 3 AMO Model Construction
### Multi-Objective Optimization Model  
The multi-objective optimization model synthesizes the economic benefits of the TRS with the commuters' travel costs to generate incentive and penalty schemes. The economic benefits encompass various elements, including public transportation fare revenue, penalties for breaches of reservation commitments, and subsidies related to the strategy. The travel costs primarily focus on the commuters' expenses and time.  
Transportation system revenue  
\[Z_{1}=\sum_{n}\left(R_{n}^{m}-S_{n}^{m}\right) \tag{1}\]  
where \(R_{n}^{m}\) represents the transportation system's revenue for traveler \(n\) choosing travel mode \(m\), including public transportation fares or fines for violations. \(S_{n}^{m}\) represents the incentive for traveler \(n\) choosing travel mode \(m\).  
Travel costs  
\[Z_{2}=\sum_{n}\left(v_{t}*t_{n}+C_{n}^{m}\right) \tag{2}\]  
where \(v_{t}\) represents the value of travel time, while \(t_{n}\) denotes their total travel time. \(C_{n}^{m}\) reflects the travel cost of traveler \(n\) choosing travel mode \(m\), including fuel cost or public transport fare.  
For multi-objective optimization problems, it is possible to transform the multi-objective optimization problem into a single-objective optimization problem by assigning specific weights to each sub-objective optimization problem. Based on the analysis of optimization objectives in this study, the model is as follows:  
\[maxZ= \lambda_{1}Z_{1}-\lambda_{2}Z_{2}\] (3) s.t. \[0\leq S^{m}\leq S \tag{4}\] \[0\leq f\leq F \tag{5}\]  
where \(\lambda_{1}\) represents the weight of the revenue objective, \(\lambda_{2}\) represents the weight of the cost objective, and \(S\) and \(F\) represent the maximum for subsidies and penalties.
## 1 Introduction
### Review of Alternative Fuels for the Marine Sector
#### 1.2.1 Low-Carbon Fuels  
Low-carbon fuels refer to cleaner fossil fuels with a lower carbon content than conventional marine fuels [11]. Natural gas is a low-carbon fuel with no sulphur and nitrogen atoms compared to conventional marine fuels. Due to that, it can easily be used for operation in Emission Control Areas (ECAs) [12; 13]. For transportation purposes, it can be used in compressed form, i.e., Compressed Natural Gas (CNG), or in liquefied form, i.e., Liquefied Natural Gas (LNG) [14]. Natural gas is liquefied by cooling to \(-163\)\({}^{\circ}\)C to make handling easier, occupying 600 times less volume than in its gaseous state [15]. Nowadays, most LNG-powered ships are powered by dual-fuel engines, which ensure a smooth transition from fuel to fuel without affecting performance and efficiency [16]. However, current investment costs, undeveloped infrastructure, and safety issues are major limitations for its use as an alternative fuel [17; 18; 19].  
Liquefied Petroleum Gas (LPG) is also considered an alternative to conventional marine fuels due to its high energy density and clean burning properties [20]. According to [PERSON] et al. [21], LPG is suitable for small to medium-sized domestic ships, such as fishing vessels. Moreover, as onboard LPG energy systems are compatible with ammonia-fuelled systems with only minor modifications, LPG can serve as a transitional fuel for zero-emission shipping with ammonia [22].  
Another low-carbon fuel already used in the shipping sector is methanol. Due to its liquid state, methanol can be used in existing diesel infrastructure with minor modifications [23]. Many studies investigated methanol as a marine fuel and concluded that its use reduces harmful emissions [24; 25; 26; 27]. Its major drawback is energy density, which is more than 50% lower than the energy density of conventional fuels [28]. However, methanol is still a suitable alternative fuel for the shipping sector, and nowadays, it is being used onboard ferries, cruisers, tankers, etc. [29].  
Dimethyl-ether (DME), a clean-burning liquid fuel of high density, is produced through methanol dehydration. Since its physical properties are similar to LPG, DME can be used in LPG infrastructure and dual-fuel engines intended for LPG [30; 31]. When combusted, it results in low CO\({}_{2}\) and NO\({}_{\chi}\) emissions, while SO\({}_{\chi}\) and PM are not emitted [32].
## 5 Discussion  
In this paper, time-dependent reliability was evaluated considering the influence of reinforcement corrosion on bridge bearing capacity and vehicle load effect. The computational results reveal that the LLD coefficient has time-dependent characteristics. This study proves that bridge stiffness is related to reinforcement corrosion. As one of the most important parameters of the LLD theory, the vehicle load effect is also shown to be inevitably influenced by reinforcement corrosion. As a result, the LLD coefficient changes with the increase in reinforcement corrosion. However, it may present an increasing or decreasing trend based on the reinforcement corrosion degree of each beam. The LLD coefficient of Beam 1 decreases over time, which means Beam 1 bears less load effect. However, the reliability index of Beam 1 still presents a downward trend. This indicates that the reliability assessment should synthetically consider the influence of reinforcement corrosion on bridge performance and load effect.  
A reinforcement corrosion model combined with bridge inspection data based on [PERSON]'s second law is established in this work. The model is proposed as a generalized method that is also suitable for bridges with different geometrical and structural characteristics. Combined with the corrosion model, an LLD coefficient calculation method with reinforcement corrosion considered is proposed using a modified eccentric compression method and an iterative algorithm. Therefore, the proposed method is only applicable for the vehicle load effect analysis of the kind of bridges calculated by the modified eccentric compression method, of which the simply supported T-beam bridge is the most typical bridge model. The method is not limited by other parameters; thus, the proposed method and analytical results can be extended and suitable for bridges under different conditions.  
Moreover, the traditional reinforcement corrosion model is analyzed by chloride diffusion, of which the distribution parameters can be only obtained by literature and theoretical analysis. A reinforcement corrosion model combined with bridge inspection data is proposed. In other words, it can perform specific analysis on the target bridge. As for the traditional LLD theory, it is an accurate and effective method for uncorroded bridges. The proposed LLD coefficient calculation method takes into consideration the influence of reinforcement corrosion on vehicle load effect, which reduces the analysis model error. The method is also suitable for uncorroded bridge or structural designs. In addition, a reliability assessment is established by the proposed methods. An iterative algorithm is adopted to deal with the interaction between effective stiffness and LLD coefficient considering reinforcement corrosion. Compared with the proposed method, the reliability index calculated by the traditional method is more conservative. This demonstrates that the influence of reinforcement corrosion on bridge reliability cannot be ignored.
## 3 Results and Discussion
### Consumer Evaluation and Acceptance of Cooked Samples  
Consumers tasted cooked samples of gilthead seabream and seabass fillets and evaluated them with regards to some sensory freshness characteristics and to their liking. Both species did not show any significant differences either as to the sensory perception or as to the degree of liking, when MAP-PAD and PAD samples were compared between highest quality and the end of high-quality shelf-life time points (Supplementary Table S3). Firmness of the cooked tissue was also evaluated and analyzed by the chi-square test; however, no differences were observed in any of the species at any time-point (Supplementary Table S6). The fact that no perceived difference was observed in freshness characteristics may be attributed to the cooking process that may mask any perceivable differences present in the raw fillets [34]. Nevertheless, overall freshness tended (\(p\) = 0.095) to be scored higher in seabass fillets at the high quality time points for the MAP-PAD compared to the MAP samples.  
This is the first study to investigate consumers' perceived freshness of cooked fish samples, since previous research with untrained [35] or semi-trained [36] consumers has focused on the sensory profiles of different cooked fish species, different fish origin (farmed vs. wild) [37] or culinary preparation effects [38]. Recently, [PERSON] et al. [39] looked into the effect of cooking on the perceived freshness of seabream fillets in different shelf-life time-points and processing, albeit using a trained sensory panel. Unlike our results, they found that differences in freshness were still perceivable after cooking. However, it should be noted that a trained panel differs from consumers in the capacity of detecting and describing differences [40].
# RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images
## 3 Results
### UID Dataset Comparison Experiment

Table 2: Comparison of experimental results for generic object Detection algorithms on the UID dataset. To further validate the performance of RML-YOLO, we also conducted comparisons with other algorithms that have performed well in the field of insulator defect detection, including LiteYOLO-ID proposed by [PERSON] et al. [29] and CACS-YOLO proposed by [PERSON] et al. [40]. Since LiteYOLO-ID is an improvement based on YOLOv5s, and CACS-YOLO is an enhancement built upon YOLOv8-m, to ensure a fair comparison of the performance among these algorithms, we not only compared them with the versions used in the original papers but also evaluated their performance when improved upon the most lightweight versions of the models. The experimental results are summarized in Table 3. Both versions of the CACS-YOLO algorithm exhibit lower performance compared with RML-YOLO. RML-YOLO and CACS-YOLO are improvements based on YOLOv8. In the lightest \(n\) version, RML-YOLO has 8% higher \(AP\) and 11.1% higher \(AP_{S}\) than CACS-YOLO-n, while RML-YOLO has 0.13 million fewer parameters than CACS-YOLO-n. Compared with CACS-YOLO-m, \(AP\) is 2.2% higher. LiteYOLO-ID-m demonstrates greater success in lightweight improvements. LiteYOLO-ID-n has a parameter count of only 0.94 million and a model size of 2.14 MB, but its performance is subpar, with \(AP\), \(AP50\), and \(AP75\) scores that are 16.7%, 11.7%, and 21.7% lower than those of RML-YOLO, respectively. In particular, LiteYOLO-ID-n performance in detecting small objects is 20.7% lower than that of RML-YOLO. Compared with its lighter \(n\) version, LiteYOLO-ID-m shows improved performance with an \(AP\) of 55.9%, but this comes at the cost of increased parameter count and model size, which are 1.25 million higher than those of RML-YOLO, while its \(AP\) is still 9.8% lower than that of RML-YOLO. These results demonstrate that our model, RML-YOLO, can maintain high detection accuracy at a lower computational cost, making it particularly suitable for applications on devices with limited resources.
# Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone
## 3 Results
### Motor Fed from a Inverter Coupled to a DC Generator
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Resistance**} & \multicolumn{2}{c}{**Experimental**} & \multicolumn{2}{c}{**Algorithm**} & \multicolumn{2}{c}{**Relative Error**} \\ \cline{2-7}  & \(\Omega_{m}\) **(r/min)** & \(\omega_{m}\) **(Hz)** & \(\Omega\) **(r/min)** & \(\omega_{F}\) **(Hz)** & \(\epsilon\) **(\%)** \\ \hline R7 & 1621 & 27.02 & 1620.1 & 27.00 & 0.0545 \\ R6 & 1701 & 28.35 & 1702.1 & 28.37 & 0.0675 \\ R5 & 1815 & 30.25 & 1816.4 & 30.27 & 0.0775 \\ R4 & 1885 & 31.42 & 1886.7 & 31.45 & 0.0912 \\ R3 & 1960 & 32.67 & 1962.9 & 32.71 & 0.1475 \\ R2 & 2016 & 33.60 & 2015.6 & 33.59 & 0.0186 \\ R1 & 2025 & 33.75 & 2024.4 & 33.74 & 0.0289 \\ R0 & 2074 & 34.57 & 2074.2 & 34.57 & 0.0105 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Motor fed from a frequency converter, driving a DC generator. Measured and estimated values of rotor speed and frequencies, as well as relative errors at a frequency supply of 35 Hz. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multirow{2}{*}{\(\Omega_{1}\)**\({}^{**}\)**} & \multicolumn{8}{c}{\(\Omega_{m}\) **(r/min)**} & \multirow{2}{*}{} \\ \cline{2-3} \cline{5-10}
**(Hz)** & & & & & & & & & \\ \hline
30.00 & 1800 & 1464 & 1490 & 1581 & 1640 & 1681 & 1722 & 1739 & 1779 \\
35.00 & 2100 & 1621 & 1701 & 1815 & 1885 & 1960 & 2016 & 2025 & 2074 \\
45.00 & 2700 & & & 2369 & 2460 & 2533 & 2593 & 2616 & 2673 \\
60.00 & 3600 & & & & 3220 & 3400 & 3451 & 3564 \\
70.00 & 4200 & & & & & 3790 & 3922 & 4142 \\
80.00 & 4800 & & & & & & 4243 & 4700 \\ \hline \hline \end{tabular}  
* Values not reported correspond to cases out of the converter capability. **\(\Omega_{1}\)** is the synchronous speed for the supply frequency.  
\end{table} 
Table 3: Motor fed from a frequency converter, driving a DC generator. Values of speed measured (tachometer) for different values of resistance and the corresponding rotor speed estimations *. Figure 9: Motor fed from a frequency converter (inverter) and driving a DC generator: (a) sketches of the test; (b) RMS spectra of vibration signals for rotor speeds ranging from 2369 r/min to 2673 r/min. As can be seen in Tables 4-6, the obtained accuracy of the estimations when an inverter is used as a power supply is similar to the case when the motor is fed from the grid. The relative error always remained below 0.15%.
## 3 Materials and Methods
### Low-Cost Disaster Monitoring System  
This experiment utilized the IoT devices based on the system architecture depicted in Figure 2. The testbed was implemented using the ESP8266 or NodeMCU board, with the firmware developed in C-sketch programming language. The ESP8266 had a 32-bit microcontroller, 80 MHz clock speed, 4 kB flash memory, 64 kB RAM, WiFi IEEE802.11b/g/n, and a \(-40\)\({}^{\circ}\)C to 125 \({}^{\circ}\)C temperature range. The NodeMCU's voltage was 3.3 V. The weather sensor comprised DHT22 for humidity and temperature monitoring, PMS7003 for pm 1.0 and 2.5 monitoring, wind speed with direction, and rain gauge. Three sensors, wind, rain gauge, and PMS7003, were connected via RS232. The voltage of these sensors was 3.3 V, and they related to the digital pin and the 5.0 V pin. Additionally, a relay was connected between ESP8266 and the network module's power line. The IoT device is connected to the Internet via Wi-Fi, connected to a 4G IoT gateway. The power source was solar power connected to the solar cell and battery of 12 V 500 A. Figure 3 illustrates the connection between the ESP8266 microcontroller and its sensor, while the right-hand side shows its installation in the sensor field.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 2 Materials and Methods
### Methodology
#### 2.3.2 Satellite-Based Flood Damage Assessment and Its Validation
For steady and unsteady analyses, standard depth is the most employed boundary state and, accordingly, the HEC-RAS model uses this value to estimate the depth of a flood. The energy slope can be estimated by calculating the slope of the river stretch downstream of the modeled river reach. To run an unsteady model, external boundary conditions are required. These are the boundary conditions that must be applied to the upstream and downstream ends of the river. Internal border requirements are optional and allow the user to identify gate operations and associated modifications to the flow along the length of the river. The level of the flood along the different stretches of the river was simulated using the peak river flow introduced into the HEC-RAS model. This peak river discharge corresponded to floods with a return period of 5-, 10-, 50-, 100-, and 150-year, along with using the maximum instantaneous discharge value of the 2014 flood. The model produced as outputs the flow depth, the cross-sectional discharge, and other related data. After deleting all errors and after the water surface generation and floodplain delineation, the output file from HEC-RAS was imported into ArcGIS, and a floodplain map was produced. Only the flood depth could be obtained on this map; therefore, a smooth water surface was created. The UNITAR Operational Satellite Application Network (UNOSAT) analyzed the 2014 Indus River flood using a MODIS image taken on July 31. This image was used to examine the extent of the flood and its damage and to compare it with that simulated by the model.
# Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring
## 2 Method Principle
### Density Based Clustering Algorithm  
The most significant difference between density-based clustering algorithms and other methods is that the former use density as the basis for algorithm judgment. Before starting clustering, density-based algorithms require setting relevant density thresholds. Such algorithms only consider the density values of neighboring points within a certain range and do not rely on setting centroids, thus avoiding the impact of distance and direction factors on the clustering process. This allows for clustering of irregularly shaped datasets based on their shape and extension trends and overcomes the impact of noise points that do not have clustering on the clustering results.  
Classic density-based clustering algorithms include OPTICS, DPC, DENCLUE, DBSCAN, among others. Although density-based clustering algorithms have high clustering accuracy, they still have some issues that require improvement. For instance, [PERSON] [22] proposed the LDP-MST clustering algorithm based on the classic MST clustering algorithm, to address the impact of noise points on the algorithm's time consumption. In [PERSON] [23], it was suggested that the use of a pair of thresholds in traditional three-way clustering methods cannot accurately represent the clustering structure, and the threshold is difficult to determine. To address this issue, they proposed a multistep three-way clustering (M3W), which improves the probability of correct data allocation by constructing multi-step data structures and corresponding allocation strategies.  
This article focuses on the time-consuming nature of the traditional clustering algorithm DBSCAN when processing massive stream data. It proposes parallelizing the DBSCAN algorithm using the distributed Flink framework and a result merging rule that acts on the main node. This enables each parallelized result to be reasonably merged into the final result. Although the above methods have improved density-based algorithms under certain specific conditions, improving their applicability and accuracy, traditional density clustering algorithms are still simple and efficient, and consume less time when facing massive data. Therefore, this article chooses the traditional DBSCAN algorithm as the model clustering algorithm.