esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Results and Discussions
### Discussion  
The CRTD network proposed in this study demonstrates significant improvements in ship detection under thin cloud cover. By integrating the Thin Cloud Removal (TCR) and Small Target Detection (STD) models, CRTD effectively addresses challenges such as low contrast and occlusion, enabling more accurate ship detection. Compared to methods like SSD, YOLOv5, and Faster R-CNN, which struggle with false positives and missed detections under cloud interference, CRTD offers superior robustness and reliability. This is attributed to the enhanced image clarity provided by TCR and the precise detection capabilities of the STD module. However, the small sample size in this study limits the generalizability of the results. The difficulty of obtaining labeled data for small targets in satellite imagery under diverse conditions is a common challenge in remote sensing. Despite this limitation, CRTD has shown strong performance, and expanding the dataset to include more diverse ship types, weather conditions, and regions will further validate its robustness. Future work will focus on addressing the dataset limitations and improving performance under more complex conditions, such as heavy clouds, fog, and low-resolution imagery. Additionally, enhancing detection in cluttered environments and for small targets will be prioritized to improve real-world applicability. In summary, while CRTD shows great promise, addressing the dataset limitations and expanding its application to more complex scenarios will be essential to further improve its accuracy and real-world applicability.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 2 Transparent Nano-Ceramics for Solid-State LASERs  
### Brief Introduction  
Solid-state lasers play a leading role in the field of laser application because they have the advantages of high peak power, high efficiency, long service life, safety, and reliability. In solid-state lasers, the gain medium has the most significant influence on the laser's output performance. Doped yttrium aluminum garnet (YAG) has the advantages of high thermal conductivity, high melting point, stable chemical properties, high mechanical strength, and high creep resistance. Compared with a single crystal and glass, the main advantages of YAG transparent nano-ceramics are: (1) the process of high concentration doping is simple, which can easily improve its properties; (2) it is easy to prepare ceramics with a large size and complex shape; (3) the preparation cost is low and the cycle is short; (4) it is convenient to realize special structures and functions. Therefore, it is the focus of research in the academic community, and many countries have invested a lot of human and material resources. Nowadays, YAG transparent nano-ceramics are widely used in the gain medium of solid-state lasers. According to the literature, the performance of transparent nano-ceramics is related to the controllable doping of rare earth ions and the size of nano-powder, which are critically reviewed in this section to elaborate on the progress of transparent nano-ceramics for solid-state lasers.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Datasets
#### 3.2.2. Sysu-Cd  
The SYSU-CD dataset, developed by Sun Yat-Sen University in 2022, is a comprehensive large-scale open-source dataset designed for change detection tasks. It comprises 20,000 pairs of image patches, each with a resolution of 0.5 m, extracted from 800 pairs of 1024 \(\times\) 1024 orthophoto aerial images of Hong Kong. Each image patch is accompanied by a corresponding pixel-level binary change map, providing precise annotations for change detection analysis.  
This dataset not only captures typical urban and suburban changes but also includes detailed annotations for high-density building changes and offshore constructions, making it particularly valuable for urban planners and remote sensing applications. In our experiments, we utilize the original 256 \(\times\) 256 image patches for model training and evaluation. The dataset is divided into a 6:2:2 ratio, consisting of 12,000 training samples, 4000 validation samples, and 4000 test samples.  
Figure 7: Visualization of the LeVIR-CD dataset. T1_img and T2_img are images at different times, and columns (**a–e**) are the corresponding instances.  
Figure 8 illustrates a representative selection of the dataset, showcasing five distinct data samples. Columns a to c highlight changes occurring in offshore areas, such as port constructions and land reclamation projects. In contrast, columns d and e focus on terrestrial changes, particularly the development and expansion of high-rise buildings and urban infrastructure. These examples demonstrate the dataset's ability to capture diverse change scenarios, from large-scale urban developments to subtle offshore modifications, underscoring its utility for both academic research and practical applications in urban planning and remote sensing.
## 3 Results
### Intelligent Variable-Flow Models  
#### 3.2.1 Results of the ANN Models  
ANN classification models, including GA-BP, ELM, PNN, and LSTM, were used to adjust the circulating pump's frequency. The upshifting operation of circulating pump frequency was labeled as 1, and downshifting operation was labeled as \(-1\). The classification process was regarded as a binary classification problem. The classification accuracy of both training set and test set data was calculated. For the BPNN model, the GA algorithm was applied to optimize the model performance. Models were tested by cross-validation to prevent the overfitting problem. ANN models were implemented by programming in Python 3.8.5 [33]. For the BPNN model, the maximum epoch was set to 1000 iterations, and the learning rate was set to 0.01 during the training process. The GA method optimized the BPNN model with the lowest error rate (2.59%) at 25 generations. The GA-BP model had the best validation performance (0.12) at epoch 142. For the LSTM model training process, loss and accuracy gradually converged after 350 iterations. The accuracy of the training set reached 100% when the loss was below 0.05.  
Table 2 presents the evaluations of the ANN classification models. Results showed that the training accuracy of all the ANN models was beyond 90%. PNN and LSTM achieved the most accurate classification (100%). For the test set, the LSTM model had a 96.84% accuracy rate; however, the accuracy rates of other models were less than 90%. Thus, the optimal model was identified as the LSTM model, with the highest accuracy for both the training set (100%) and test set (96.84%) among the ANN models.
## 2 System Model and Methodology
### PID Controller Design  
A PID controller is a classical feedback controller, and its control law consists of three components: Proportional (P), Integral (I), and Derivative (D):  
\[F_{PID}(t)=K_{p}e(t)+K_{i}\int\limits_{0}^{t}e(\tau)d\tau+K_{d}\frac{de(t)}{dt} \tag{9}\]  
In the PID controller, K\({}_{\text{P}}\) is the proportional gain, K\({}_{\text{I}}\) is the integral gain, K\({}_{\text{d}}\) is the derivative gain, and e(t) is the error signal (the difference between the setpoint and the actual value). The setpoint in this case is the displacement of the upper layer, which is defined as 0, and the actual value is the measured displacement of the upper structure. By adjusting the proportional, integral, and derivative parameters, the PID controller ensures that the system reaches the desired control target (with the upper layer displacement approaching zero). The proportional controller output is directly proportional to the error. Its primary role is to quickly respond to the current error and reduce the steady-state error. The integral controller accumulates the error over time to eliminate any residual steady-state error. For persistent deviations, the integral term increases until the error is corrected. The derivative controller reacts to the rate of change in the error, predicting the error trend. This improves the system's response speed and stability, helping to reduce overshoot. Together, these three components allow the PID controller to effectively regulate  
Figure 3: Transmission rate of hybrid seismic isolation system.  
the system's response to achieve optimal seismic isolation performance by driving the upper layer displacement to zero.
## 5 Parametric Analysis  
The antenna resonance for various parameter changes is analysed. The parametric analysis of various parameters is carried out using ANSYS(r) HFSS. It can be observed that the radius of the CDNG structure plays a major role in antenna resonance frequency. The slot length L\({}_{4}\) = R\({}_{2}\) - \(W_{2}\)/2 also vary with change in radius. The radius change shifts the resonaces towards lower frequencies in two resonances. The variation in the resonant frequencies with respect to the radius of CDNG-MTM (R\({}_{2}\)), keeping all other parameters constant, is illustrated in Figure 9. The parametric analysis shown as Figure 9 points out that the shift in lower band is more significant when compared to the shift in the higher band. When R\({}_{2}\) is varied from 4 mm to 8 mm, the \(-\)10 dB bandwidth of higher band is progressively enhanced.  
Figure 10 represents the variation in the resonant frequency with respect to slot length (L\({}_{5}\)). When L\({}_{5}\) is varied from 1 mm to 7 mm, shift of resonances towards left is observed at both operating frequencies. The shift in lower band is more significant when compared to the shift in the higher frequency band. Lower frequency tuning up to 1.8 GHz is possible by varying L\({}_{5}\). However, tuning of L\({}_{5}\) is optimised at 2.8 mm corresponding to WLAN operating frequencies of 2.4 GHz in the lower band and 5.2 GHz in the higher band.  
Figure 10: Variation in resonant frequency with respect to slot length.  
Figure 9: Variation in resonant frequency with respect to radius of CDNG-MTM.  
Figure 11 represents the variation in the resonant frequency with respect to position of CDNG structure (L\({}_{6}\)). Increase in L\({}_{6}\) shifts the upper band towards lower frequency by keeping first resonance unaltered.  
From the parametric analysis we can conclude that the first resonance is solely based on the size of the CDNG-MTM. The subsequent resonance is obtained by the combined effect of patch and CDNG-MTM.
## 2 Results
### Sufficient Supply of reduced Nicotinamide Adenine Dinucleotide Phosphate (NADPH)  
The engineered _E. coli_ consumes 2 NADPH to convert 3-hydroxy-3-methyl-glutaryl-CoA (HMG)-CoA to MVA via the MvaE enzyme of the (\(-\))-\(\alpha\)-bisabolol biosynthetic pathway (Figure 1). Therefore, if the intracellular NADPH pool of engineered _E. coli_ increases, it will improve (\(-\))-\(\alpha\)-bisabolol production. In _E. coli_, an endogenous GAPDH generates a reduced nicotinamide dinucleotide (NADH) to convert glyceraldehyde 3-phosphate into pyruvate. Therefore, we replaced the endogenous GAPDH gene (_gapA_) of _E. coli_ with an NADP\({}^{+}\)-dependent GAPDH gene (_gapC_) of _C. acetobutylcium_ to increase the intracellular NADPH pool in _E. coli_. To do this, we inserted the _gapC_ gene of _C. acetobutylcium_ into the downstream of the _ispA_ gene of the pTSN-Bisa-Mm plasmid, which resulted in a pTSN-Bisa-Mm-GapC plasmid (Figure 5A). The _E. coli_-pTSN-Bisa-Mm-GapC strain showed similar (\(-\))-\(\alpha\)-bisabolol production and cell growth to the _E. coli_-pTSN-Bisa-Mm strain (Figure 5B,C). This might be caused by competition between endogenous GapA and heterologous GapC in the _E. coli_-pTSN-Bisa-Mm-GapC strain. To investigate this, the _gapA_ gene was repressed by clustered regularly interspaced short palindromic repeats (CRISPRR) interference (CRISPRR). The CRISPR system comprises L-Hammose-inducible deactivated Cas9 (dCas9) and a constitutively expressed single guide RNA targeting _gapA_ gene (sgRNA-GapA) by J23119 promoter, respectively (Figure 5A). The _E. coli_ strain harboring both pTSN-Bisa-Mm-GapC and pdCas9-sgRNA-GapA plasmids produced 1.4-fold higher (\(-\))-\(\alpha\)-bisabolol compared to the _E. coli_ strain containing the pTSN-Bisa-Mm-GapC plasmid alone. Interestingly, the cell growth of the _E. coli_ strain repressing _gapA_ by CRISPR showed better cell growth than the control _E. coli_ (Figure 5C). Given that the (\(-\))-\(\alpha\)-bisabolol/OD\({}_{600}\) are similar between the two strains, it is likely that the increased production of (\(-\))-\(\alpha\)-bisabolol is due to increased cell mass.  
Figure 4: (\(-\))-\(\alpha\)-Bisabolol production in _E. coli_ DH5\(\alpha\) containing pTSN-Bisa-Mm and pSSN12 Didi-MrBBS-IspA which has genes involved in the MVA lower pathway including _Sp_MvaK1, IspA and _MrBBS_. (**A**) Plasmid constructs for expressing genes of entire (\(-\))-\(\alpha\)-bisabolol biosynthetic pathway or lower MVA pathway. The concentrations of (\(-\))-\(\alpha\)-bisabolol (**B**) and MVA (**C**) produced by the engineered _E. coli_ DH5\(\alpha\) harboring pTSN-Bisa-Mm and pSSN12 Didi-MrBBS-IspA. Cells were cultivated in TB medium containing 10 g/L glycerol and 20% (v/v) of \(n\)-dodecane in the presence of different IPTG concentrations (0, 0.025 and 0.1 mM) at 30 \({}^{\circ}\)C for 72 h. The error bars represent the standard deviation of the concentrations of (\(-\))-\(\alpha\)-bisabolol and MVA from three biological replicates.
# Quality Parameters of Wheat Bread with the Addition of Untreated Cheese Whey
## Materials and Methods
### Physicochemical Analysis
#### 3.3.3 Semi-Quantitative Determination of Volatile Compounds  
Volatiles were determined on day 0 and 4 (end of shelf life) of storage according to the method of [PERSON] et al. (2011) after appropriate modification. First, 1 g of homogenized bread (crust and crumb) and 20 \(\mu\)L of 4-methyl-2-pentanol (324 mg/kg) as an internal standard were placed into a 20 mL glass serum vial and sealed with an aluminum crimp cap provided with a needle-pierceable polytetrafluoroethylene/silicone septum. Solid-phase microextraction (SPME) was performed with a 50/30 \(\mu\)m Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fiber mounted to a SPME manual holder assembly (Supelco,Bellefonte, PA, USA). The sample vial was incubated in a 50 \({}^{\circ}\)C water bath. After allowing 5 min for the sample to equilibrate at 50 \({}^{\circ}\)C, the needle of the SPME device was inserted into the vial through the septum, and the plunger of the SPME apparatus was pushed down to expose the DVB/CAR/PDMS fiber to the vial headspace and sorb the volatiles. After 15 min of exposure time, the fiber was retracted into the needle assembly, removed from the vial, and transferred to the injection port of the GC unit. Desorption of volatiles took place in the injector port (270 \({}^{\circ}\)C) of the GC/MS Agilent 7890 GC System (Wilmington, DE, USA) for 10 min in split mode (2:1). The chromatographic column used was Agilent DB-5 MS (5% phenylmethylpolysiloxane) with a 0.25 \(\upmu\)m film thickness, 60 m \(\times\) 0.32 \(\upmu\)m i.d. The temperature program was 40 \({}^{\circ}\)C maintained for 5 min; then, it increased by 10 \({}^{\circ}\)C /min to 100 \({}^{\circ}\)C, by 5 \({}^{\circ}\)C /min to 205 \({}^{\circ}\)C, and by 10 \({}^{\circ}\)C /min to 260 \({}^{\circ}\)C, where it was held for 5 min. The carrier gas was helium, with a column flow of 1.5 mL/min. Source temperature: 230 \({}^{\circ}\)C, quadrupole temperature: 150 \({}^{\circ}\)C; transfer line temperature: 280 \({}^{\circ}\)C. Mass spectra were recorded on a GC-MS 5975C inert XL MSD mass spectrometer (Wilmington, DE, USA) in the mass range _m_/_z_= 29-350. The above conditions were the result of preliminary optimization including sample weight, fiber type, equilibration time, and temperature. The identification of volatiles was achieved by comparison of the MS data with those in the NIST05 library. For the determination of retention indices, a mixture of _n_-alkanes (C5-C7 and C8-C20) dissolved in _n_-hexane was employed. The mixture was supplied by Fluka (Buchs, Switzerland). The calculation was carried out for components eluting between _n_-pentane and _n_-eicosane.
## 3 Electrodeposition
### Properties Relevant to Applications
#### 3.3.1 Corrosion Resistance
[30] reported that the FeCoNiCr alloys (DC, sulfate-citrate bath) with the highest chromium content (42 at%) and with other metals at nearly constant levels exhibited the best corrosion resistance. Chromium played a key role not only in enhancing the corrosion resistance but also in increasing the hardness of the coatings, while the friction coefficient and wear rates remained relatively unchanged across all alloy compositions. In turn, [PERSON] et al. [31] compared the corrosion behavior of FeCoNiCr alloys in different media and found that the corrosion resistance followed the order 1 M H\({}_{2}\)SO\({}_{4}\) (0.11 mm/y) \(<\) 3.5% NaCl (0.016 mm/y) \(<\) 1 M NaOH (0.013 mm/y). [PERSON] et al. [55] further showed that the incorporation of aluminum, but not manganese, into the FeCoNiCr alloy (DC, sulfate-chloride-citrate bath) reduced corrosion current density and shifted the corrosion potential in a more positive direction. However, both elements increased the microhardness of the coating by approximately 50-60 HV. [PERSON] et al. [38,56] obtained FeCoNiCrMn alloys from organic baths on copper (PD, DMSO-AN) and aluminum (PD, DMF-AN and DMSO-AN). The alloys exhibited low corrosion current densities, lower than those of the substrates; however, defects in the coating films produced in the DMSO-AN bath resulted in no effective protection of the aluminum substrate. Notably, an unusually low corrosion current density (on the order of 10\({}^{-1}\) uA/cm\({}^{2}\)) was recorded for the AlFeCrNiTi alloy deposited galvanostatically from an aqueous bath [46]. The high corrosion resistance was attributed to the combination of elements with a strong tendency to form compact passive films (mainly Al, Ti, and Cr), along with a uniform, homogeneous microstructure and minimal coating defects.
## 3 SENDI: System for Detecting and Forecasting Natural Disasters Based on IoT
### Fault Tolerance Scheme
Thus, given a node \(s\), its new threshold is defined as:  
\[T^{\prime}(s)=T(s)\cdot\alpha+\frac{E}{E_{f}}\cdot(1-\alpha) \tag{5}\]  
where \(E\) is the current battery power of the node, \(E_{f}\) is the maximum capacity of the battery (allowing for differences in the batteries among the nodes), \(\alpha\) is the weight given to each part of the equation, where value 1 only uses the LEACH altered/implemented in the simulation, and value 0 only uses the information about energy. After the threshold has been calculated, the procedure  
Figure 5: Radio energy dissipation model [23]. continues in the same way, with \(s\) generating a random number and defining whether or not it is a leader based on a comparison between this value and the threshold. The number of rounds \(r\) in \(s\) is reset every time \(s\) becomes a leader. Apart from these differences, there is no longer a limitation of \((r*mod(n/k))\) rounds, thus any node can be a leader regardless of how many times the other nodes have been leaders or the number of rounds (allowing a node with a high battery level to become the leader on repeated occasions). In a same round, this process of selection can take place several times if the first election is not enough for all the nodes to have a defined leader. The maximum number of elections in a round can be stipulated and if a node does not have a leader defined after reach this limit, the node itself becomes his own leader regardless its battery level. The Algorithm 1 illustrates the operation for the leader election in each node. After each election attempt, if the node was not set as a leader, it waits a random time letting the other nodes in its neighborhood run the same algorithm. In case of a new leader does not appear, the election algorithm continues until it reaches the stipulated maximum limit, when the node is forced to become a leader. ```
1:\(Kopt\leftarrow\sqrt{\frac{n}{2\times n}}\times\frac{2}{0.765}\)
2:\(Popt\leftarrow\frac{Kopt}{n}\)
3:ThresholdLEACH\(\leftarrow\frac{Popt}{1-Popt\times(rand(\frac{1}{Popt}))}\)
4:MaxNumberElection\(\leftarrow\)GetMaxNumberOfElections\(()\)
5:ElectionCounter\(\gets\) 0
6:while \"Node without a leader\" do
7:ThresholdEnergy\(\leftarrow\frac{GetCurrentBatteryPowerLevel()}{GetFullBatteryPowerLevel()}\)
8:Threshold\(\leftarrow\)ThresholdLEACH\(\times\alpha+ThresholdEnergy\times(1-\alpha)\)
9:RandomNumber\(\leftarrow\)random\((0,1)\)
10:ElectionCounter\(\leftarrow\)ElectionCounter\(+1\)
11:if\(RandomNumber<\)Thresholdor\(ElectionCounter>MaxNumberElection\)then
12:SetNodeAsLeader\(()\)
13:else
14:wait\((random(0,1))\)
15:endif
16:endwhile
```  
**Algorithm 1** Leader Election Algorithm.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 3 Research Method
### Ice Load Parameters  
To calculate ice-crushing pressure \(p,[MPA]\), we first need to calculate related ice load parameters including indentation depth, contact area, normal force and load length. We calculate indentation depth directly by the Popov Method for all considered collision scenarios. To this end, we apply the contact geometry specific values for \(G\) and \(a\) (see Equation (2)) determined by [PERSON], et al. [9], [PERSON] [34] and [PERSON] [35]. We then calculate the ship-ice contact area \(A,\left[m^{2}\right]\), following Equation (2), then the related load length \(l,[m]\) and the total ice load \(F,[MN]\), following Equation (4). The load length is thereby specific for each contact geometry and can be derived as a function of the indentation depth. For example, for round contact geometry, load length \(l_{round},[m]\) is calculated following Equation (9) [9].  
\[l_{round}=\frac{5}{3}\cdot\sqrt{\frac{2\cdot R\cdot\zeta_{n}}{cos\beta^{\prime }}},[m] \tag{9}\]  
Based on the calculated parameters, we calculate ice crushing pressure \(p\) as a function of the contact area for each considered collision scenario, following  
\[p=\frac{F}{l\cdot h},[MPa] \tag{10}\]
## 6 Strengths, Limitations, and Future Research Directions
### Future Research Directions
#### 6.3.5 Explainability and Interpretability  
Deep learning models are often considered as black boxes because they do not provide insight into the decision-making process. This may prevent the usage of the models in certain areas such as justice and healthcare [30], among others. Future research should focus on making these models more interpretable and explainable. For example, by designing transformer architectures that provide visual explanations of their decisions or by developing methods for extracting features that are easily interpretable.