esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology
### Model Training and Evaluation  
Considering the size of the datasets, we train our models over a total of 50 training epochs. For each epoch, we iterate over all the data in the datasets. At each iteration, we select a batch of 32 synthesized image-mask pairs and a separate batch of 32 real images from our datasets. The image-mask pairs are fed into the segmentation branch of our model, which consists of an encoder and a mask decoder. The segmentation loss is then calculated according to Equation (8). Similarly, the real images are fed into the reconstruction branch of our model, comprising the encoder (shared with the segmentation branch) and an image decoder, where the reconstruction loss is calculated as per Equation (9). The total loss is computed by summing the segmentation loss and the reconstruction loss. Figure 3 illustrates this process.  
This dual-stream approach ensures that the encoder effectively attends to both types of data, with each decoder specializing in a task corresponding to its branch--namely, segmentation or reconstruction. Model updates are orchestrated using the AdamW optimizer [32], with the learning rate set at 0.0001. For all experiments, the coefficients \(\lambda_{MSE}\), \(\lambda_{SSIM}\), \(\lambda_{Perceptual}\), \(\lambda_{BCE}\), and \(\lambda_{Dice}\) are set to 1. After training for 50 epochs, we selected the model with the highest Dice score as the best model. During the evaluation process, we assessed the model performance using the Dice score and IoU. As a baseline for comparison, we used a model developed in [7].
## 7 Synchrosqueezed CWT Scalogram Fault Prediction System
### Proposed Fault Prediction System  
The proposed method uses CWT functions in MATLAB and consists of four steps:  
* First, the time domain signal streams are read into the system to generate wavelet scalograms. Scalograms are graphs that can reflect the similarity between wavelets and analysed signals and thus find the singular frequencies in the signals.
* Second, the centre of gravity of the complex energy density distribution is adjusted to maximise the energy density of within the resolution window. The data can then be exported to a data sheet, such as Excel, for analysis.
* Third, the pulse distribution in the synchrosqueezed wavelet transform data is counted in suitable groups, such as a histogram. The number of pulses in each frequency range is an indication of the pattern of operation at the time window. To assist the visualisation of the process, a radar graph with a different time window is plotted. It is necessary to note that plotting the spider graph is purely a visualisation exercise.
* Lastly, the number of pulses in the dominant frequency ranges is checked against time. Characteristics of the changes in the number of pulses in the dominant frequency ranges are observed and associated with a fault recorded for that data set.
## 4 Analysis and Discussion
### Correlation Analysis
#### 4.2.2 Correlation Analysis of Results  
In practice, the staggered-step bridgehead settlement often occurs where the road and bridge are straight connected (no significant longitudinal slope change) or the pavement material of bridge joints is significantly exfoliated. In contrast, the non-staggered-step bridgehead settlement is studied in this paper. To make the proposed detection method applicable to the correct object, the bridge with significant longitudinal slope change and no obvious material exfoliation at the bridge joints is selected as the test object.  
For the value of model parameters in this paper, the MOT Standard [29] regulations on pavement bumping detection sampling interval with 0.1 m and considering the influence of point density on the calculation results in the practical application model. Meanwhile, the Guidelines [25] provisions on the axle distance of the detection vehicle and the influence of the slope drawing length on the calculation results in the practical application model are considered. In summary, the model parameters are valued according to the following rules:  
1. For \(D\) in the model: data is accurate to 0.05 m and the value is different according to different road profile lines of the test objects.
2. For \(\Delta d\) in the model: it takes 0.5 m when \(D>5.0\) m, on the contrary, it takes 0.1 m.
3. For \(d_{0}\) in the model: it takes 2.0 m when \(D>5.0\) m, on the contrary, it takes 1.0 m.
4. For \(\Delta S_{0}\) in the model: the test speed of each group is less than 40 km/h, according to Table 1, \(\Delta S_{0}\) is 4%.  
The research team behind this paper selected the data of entering and exiting bridges in Ningbo, Zhejiang Province, as test objects. The vehicular detection system collected the longitudinal slope data and continuous pavement images simultaneously. To ensure the uniformity of the trials, 50 groups of SPRs, including the entering and exiting bridge, were obtained through the established calculation model and algorithm. In the meantime, according to the manual test scheme, the longitudinal slope difference between the transition section and the bridge section was measured at the bridge-road junction of each test object, forming 50 sets of manual test results. _SPRs_ and \(\Delta S\) are illustrated in Appendix A.  
To further analyze the correlation between the output results of the calculation model and the manual measurement results, the above 50 groups of SPRs and the absolute value of the longitudinal slope difference \(\Delta S\) by manual measurement are analyzed for correlation. The correlation coefficient r = 0.934, indicates that the two are significantly correlated. Furthermore, we use SPSS software to establish a linear regression model between the two. The model-fitting results are shown in Figure 8. The analysis shows that the determination coefficient of SPRs and manual measurement results is 0.872, indicating that the established regression model has high fitting accuracy. This study confirms the capability of using this methodology for measuring the non-staggered-step bridgehead settlement.
## 4 Results and Discussion
### Performance Analysis  
The performance of the proposed system has been evaluated in accordance with F1-Score, Precision, Recall and Accuracy in relation to the three considered datasets. The outcomes are discussed in this section.
## 4 Further Analysis with Discussions and Implications
### Angular Analysis and the Inferences of Fact
#### 4.2.1 Classifying the Technological Powers with the Angular Analysis Method
The destinations of the arrows for the USA, CHN, EU, GBR and CAN fall in quadrant II, while those for GER, JPN, TWN and KOR fall in quadrant III. As such, these countries are roughly classified into two distinct groups due to the divergence in the STS in each country. * they are both manufacturers of exports which are in demand across the world, they have excellent engineering skills and leadership in manufacturing and craftsmanship\" [30]. This is indeed a surprising finding. According to statements made by experienced patent attorneys and historians who are familiar with the world's industrial history, this is perhaps because these two countries are the two leading experts in the field of precision machinery, e.g., in the machine tool and automobile industries. In addition, the collaborations between them is quite strong, so technology company patenting strategies in these two countries are extremely correlated with each other. * _Germany plays a special role in Europe_. Another surprising fact is that GER and the EU are located in different quadrants (i.e., they are classified into two different groups). This implies that the market and industrial properties of GER are totally isolated from the rest of the EU. So, many overseas technology companies would thus be interested in applying for patents in GER in order to protect their IP directly there, rather than in applying for patents first via the EU's official patent office and then claiming their IP rights in GER later (an approach which would be more time-consuming).
# Traffic Signal Optimization for Multiple Intersections Based on Reinforcement Learning
## 3 Methods
### Reward  
The traffic flow changed during the time interval \(\Delta t\) by the action \(A_{t}\) was given as a reward to the model. Reward \(R_{t+\Delta t}\) is defined as Equation (2). \(q^{i}_{t+\Delta t}\) is defined the number of vehicles stopped in the incoming lane \(i\) at time step \(t+\Delta t\) and \(f^{o}_{t,t+\Delta t}\) is defined as the number of vehicles located in the outgoing lane among the vehicles passing through the intersection between time step t and time step \(t+\Delta t\). Accordingly, the reward \(R_{t+\Delta t}\) was defined as the number of passing vehicles compared to stopped vehicles. As the number of stopped vehicles decreased and the number of passing vehicles increased, the reward was increased.  
\[R_{t+\Delta t}=\sum_{l=1}\left(f^{o}_{t,t+\Delta t}\right)/\sum_{i=1}\left(q^{ i}_{t+\Delta t}\right) \tag{2}\]
# Modeling of Integrated Nanoneedle-Microfluidic System for Single Cell Temperature Measurement
## 2 Proposed Nanoneedle-Microfluidic System  
### System Concept and Design  
The idea of the device is to integrate a thermal nano-sensor with a microfluidic channel to have a high throughput system for rapid analysis. The system design is shown in Figure 1a. The concept can be summarized as follows: By manipulating the flow rate of water using an external syringe pump through the inlet hole, cells will flow towards the nanoneedle and would have the necessary force imposed on it that will enable the penetration into the nanoneedle for temperature measurement, as shown in Figure 1b,c.  
The detection is done by applying voltage to the electrodes of the nanoneedle and measuring the output current for results. The current value will change corresponding to the change in the internal temperature of the cell. The nanoneedle works in the same principle as the resistance temperature detectors (RTD) having a positive temperature coefficient of resistance (TCR). The sensing element was chosen to be of tungsten for its high electrical conductivity, mechanical strength and promising fabrication procedure [23; 24; 25]. The rigidity of Tungsten and the small size of the nanoneedle ensure higher chances of penetration. Equations (1) and (2) [26] present the working principle of the sensor.  
\[V=R\ \times I \tag{1}\]\[\rho=-1.06871+\left(2.06884\times 10^{-2}\times T\right)+\left(1.2797110^{-6} \times T^{2}\right)+\left(8.5310110^{-9}\times T^{3}\right)-\left(5.1419510^{-12} \times T^{4}\right) \tag{2}\]  
\(V\) is the voltage (V), \(I\) is the current (A), \(R\) is the resistance (Ohm), (\(\rho\)) is related the resistivity which relates resistance to temperature, and \(T\) is the temperature (K).
# Fatigue of Friction Stir Welded Aluminum Alloy Joints: A Review
## 5 Fatigue Life of Friction Stir Welded Joints  
### The Stress Cycle (SN) Analysis  
Miner's Rule is a linear damage accumulation model used to compute damage caused by cyclic loading, as shown in Equation (3).  
\[\mathrm{D}=\sum_{i=1}^{k}\frac{n_{i}}{\mathrm{N_{i}}} \tag{3}\]  
where, \(k\) is the number of different stress levels, \(\mathrm{N_{i}}\) is the fatigue life corresponding to the load level I, and \(\mathrm{D}\) is the damage fraction, when damage is equal to \"1\", failure occurs. However, Miner's Rule ignores the influence of the cyclic loading sequence, the load interaction, and the small load below the fatigue limit [87].  
The stress cycle (SN) analysis estimates the loading cycles to initiate and grow a crack until the material breaks into parts. The S-N curve is a plot of the alternating stress against the number of cycles to failure and is used to record the fatigue life of welded joints. [PERSON] formulated S-N data mathematically in a power law:  
\[\Delta\sigma\mathrm{N_{\_}{f}^{a}}=C \tag{4}\]  
where, \(\Delta\sigma\) is stress range, \(N_{\_}{f}\) is the number of cycles to failure, and \(a\) and \(C\) are constants. Basquin is not valid in the low cycle fatigue region.  
The majority of stress life investigations have been conducted on the aluminum alloys. The S-N data of common aluminum alloy materials in the literature were summarized in Figure 14[23; 25; 32; 55; 65; 23]; \(TH\) and \(R\) denote the thickness of the workpiece and stress ratio, respectively. There is no general trend found in the diagram, since the test condition and weld qualities were different for each experiment. This indicates the sensitivity of fatigue behavior to the influencing factors, such as the process parameter, tool geometry, weld thickness, microstructure, and postweld treatment.
## 2 Heavy Metals and Their Effects
### Arsenic  
It is found in literature that many human beings are affected by arsenic exposure, predominantly in countries such as Bangladesh, India, Chile, Uruguay, Mexico, and Taiwan, where underground water is polluted with large amounts of arsenic. In water, its permissible limit is 10 ppb, even though its higher concentration is found in mineral resources. Arsenic is a metalloid present in a trace amount in nature. It is found primarily in organic forms such as monomethylarsonic acid, dimethylarsinic acid, and trimethylarsine oxide, as well as inorganic forms such as arsenite As\({}^{3+}\) and arsenate As\({}^{5+}\). In general, inorganic arsenic is more hazardous in comparison with organic forms. Moreover, arsenite is more poisonous as it is more soluble in water than arsenate. The sources of arsenic are volcanic explosions, abrasion of soil, agriculture, industry, dyes, electronic industry, and medical fields. Arsenite binds to the thiol moiety on proteins and enzymes during biochemical reactions and deactivates around 200 enzymes. It can cause neurologic problems, lower RBC counts, resulting in anemia, and lower white blood cell counts, resulting in leucopenia. Exposure to high concentrations of arsenic causes vomiting, diarrhea, and pulmonic hypoxemia. The permissible limit of arsenic in water as recommended by the EPA is 0.01 mg/L [24].
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 4 Discussion
### Limitations of the Study  
While the results are promising, certain limitations should be acknowledged. Controlled Environment: This study was conducted in a controlled freshwater environment to isolate spectral effects. Future studies should investigate the method's performance in open marine conditions with variable salinity, wave action, and biological activity.  
This study focused on measurements taken at a fixed transducer angle. Future research should explore the effect of varying angles of incidence on the spectral return.  
This study tested only two distinct soil types. To confirm the method's robustness, further research should analyze a broader range of sediments, including mixed compositions and heterogeneous substrates. Potential Noise and Interference: In practical applications, biological activity, sediment resuspension, and underlying geological layers may introduce spectral variability. Refining signal processing techniques will be crucial for mitigating such effects.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 4 Results
### Comparisons
#### 4.1.4 Comparison with Method in [7]  
In [7], a two-stage comb decimation filter with a sharpened comb in the second stage was proposed. A linear optimization technique determines the coefficients of sharpening polynomial. The complexity of the design expressed in the number of adders per output sample (APOS) equals \(163\), while the minimum stopband attenuation is \(30\)**dB**, and the passband deviation is \(0.16\)**dB**. This work is compared in [7] with the compensated Chebyshev sharpening comb using polynomial \(p(x)=1-2^{9}x^{2}+2^{15}x^{4}\)[11] for \(\omega_{p}=0.226\pi/M\) and \(M=32\). This filter requires \(138\) APOS with a minimum stopband attenuation of \(38.12\) dB and a passband deviation of \(0.02\) dB.  
The proposed compensator from the first row in Table 11 is applied to the Chebyshev sharpened comb, resulting in a passband deviation of \(0.0169\) dB and the number of APOS equals \(139\), which is a much better result than in [7].
## 3 Materials and Methods
### Model Classification and Tuning
#### 3.3.2 Support Vector Machine (SVM)  
SVM is one of the supervised learning techniques for classification, regression, and outlier detection. SVM is a classifier that works by creating a hyperplane or multiple hyperplanes for separation, which implies giving the training data labels, based on the optimal hyperplane, that will categorize the new sample [33]. SVN's hyperparameters include the kernel, which has a default value of RBF and which was used to hyper-tune the C parameter, which is the regularization parameter, and Gamma, which is the kernel coefficient [34].
# Optimal Routing for Time-Driven EH-WSN under Regular Energy Sources
## 5 Criterion for Optimal Routing
So, the reconnection only increases the traffic load of node \(v\), whereas the traffic load of the rest of the nodes remain unchanged. Accordingly, the average traffic load of the layer containing node \(v\) increases, whereas the average traffic load of the rest of layers does not experience any change. Altogether, this means that the average traffic load calculated over the entire network increases for routing criteria that generate intra-layer connections. Let us focus now on the effects of backward inter-layer connections. Figure 5 shows an elementary change, where a (forward) inter-layer connection from node \(u\) to node \(y\) is replaced by a backward inter-layer connection to node \(x\). The new balance is as follows:  
* \(\sigma_{new}(u)=\sigma_{old}(u)=\sigma(u)\). * \(\sigma_{new}(x)=\sigma_{old}(x)+1+\sigma(u)\)
* \(\sigma_{new}(v)=1+\sigma_{new}(x)=1+\sigma_{old}(x)+1+\sigma(u)=\sigma_{old}( v)+1+\sigma(u)\). * \(\sigma_{old}(y)=1+\sigma_{old}(v)+1+\sigma(u)\). * \(\sigma_{new}(y)=1+\sigma_{new}(v)=1+\sigma_{old}(v)+1+\sigma(u)=\sigma_{old}( y)\). So, now the reconnection causes an increase in the traffic load of nodes \(x\) and \(v\), whereas the traffic load of the rest of the nodes remain unchanged. Accordingly, only the average traffic load supported by layers \(l_{i+1}\) and \(l_{i}\) increases, meaning that the average traffic load calculated over the entire network  
Figure 5: Converting an inter-layer connection into an intra-layer (1) or a backward inter-layer (2) connection. increases for routing criteria that generate backward inter-layer connections. In summary, any routing scheme generating intra-layer and/or backward inter-layer connections will incur an average traffic load larger than the average traffic load produced by a routing scheme that only generates (forward) inter-layer connections. \(\square\)  
Note that creating a routing topology that only includes (forward) inter-layer connections is equivalent to applying the minimum hop count criterion. Therefore, this is the optimal routing strategy for time-driven duty-cycled EH-WSN under regular energy sources.
## 6 Application of the Optimization Techniques: Routing Protocols
### Hierarchical Routing Protocols
#### 6.3.2 PEGASIS (Power-Efficient Gathering in Sensor Information Systems)  
It is considered an optimization of the LEACH algorithm. Rather than classifying nodes in clusters, the algorithm forms chains of the sensor nodes. Based on this structure, each node transmits to and receives from only one closest node of its neighbors. With this purpose, the nodes adjust the power of their transmissions [22]. The node performs data aggregation and forwards it the node in the chain that communicates with the sink. In each round, one node in the chain is elected to communicate with the sink. The chain is constructed with a greedy algorithm.
## 2 Literature Review
### Theoretical Background
#### 2.2.4 Internal Users' Context  
Regarding the Internal Users' Context, this study initially adopted two constructs, namely performance expectancy and effort expectancy from the Unified Theory of Acceptance and Use of Technology (UTAUT). This is appropriate for measuring an individual's intention and behavior in adopting information technology [41; 42]. The performance expectancy is defined as \"the degree to which an individual believes that using the system will help him or her to improve on job performance\". The effort expectancy is defined as \"the degree of ease associated with the use of the system\" [41].
###### Abstract
It was also demonstrated that the formation of water networks within the MOF pores was crucial to further lower the corresponding reaction barrier via a proton-exchange mechanism and that physical obstruction of this network with inert molecules (He, Ar) hinders the reaction [14]. In the present work, we explore the role of co-adsorbed alcohol molecules and present a kinetic analysis of the water dissociation reaction inside Zn-MOF-74 in the presence of additional guest molecules. We find that alcohol molecules affect the dissociation rate, either by enhancing or blocking the reaction depending on the type of interaction resulting from such co-adsorption. Specifically, methanol enhances the reaction due to H-bonding interactions, while isopropyl alcohol (IPA) hinders it because steric interactions dominate in that case. The knowledge derived from these combined studies is directly relevant to fields such as catalysis and sensors by providing a means to control water-related reactions, as well as to applications such as gas storage and separation by suppressing water dissociation and therefore extending the MOF lifetime.
# Shaping 1,2,4-Triazolium Fluorinated Ionic Liquid Crystals
## 2 Materials and Methods
### Synthesis of 1,2,4-Oxadiazole (3)  
The 1,2,4-oxadiazole (3) was prepared as previously reported, reacting \(N^{\prime}\)-Hydroxy-3,5-dimethoxybenzimidamide (2) with pentadecafluorooctanoyl chloride to obtain compound 3 [26]. Then, \(N^{\prime}\)-Hydroxy-3,5-methoxybenzimidamide (2) (5.5 g, 28 mmol) was dissolved in toluene (75 mL). Pyridine (2.8 mL, 1.2 eq.) and pentadecafluorooctanoyl chloride (8.3 mL, 33.6 mmol) were then added to previous solution. The reaction mixture was refluxed for 6 h, and the reaction was monitored by TLC (petroleum ether and ethyl acetate 20:1). Subsequently, the mixture was evaporated, water (100 mL) was added, and the pH was adjusted with a NaHCO\({}_{3}\) solution. The organic compound was extracted with ethyl acetate (3 \(\times\) 40 mL), and the organic phase was dried with Na\({}_{2}\)SO\({}_{4}\). Compound 3 was isolated by chromatography, using petroleum ether/ethyl acetate 10:1 as eluents.  
**3-(3,5-di-methoxyphenyl)-5-(perfluoroheptyl)-1,2,4-oxadiazole (3):** colourless solid. Yield = 34%. \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\), ppm: 3.84 (s, 6H), 6.72 (t, J = 4 Hz, 1H), 7.19(d, J = 4 Hz, 2H). ESI-MS analysis for [C\({}_{17}\)H\({}_{9}\)F\({}_{15}\)N\({}_{2}\)O\({}_{3}\)\({}^{+}\)]: Calc.: 574.0403 g mol\({}^{-1}\), exp.: 574.0606 g mol\({}^{-1}\).