esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Temperature Characteristic Test  
The temperature characteristic of the sensor was tested for two different cases: (1) the sensor module only and (2) the sensor attached to the test piece described in the load test. In both cases, the sensor was placed in a temperature chamber (SU-641, Espec, Japan), and the temperature was first increased from 5 to 45 \({}^{\circ}\)C and then decreased to 5 \({}^{\circ}\)C again.  
The experimental results (Figure 7 show that the sensor output linearly increases when the temperature changes. This measurement was also conducted three times, and the results showed the same tendency. A representative experimental result is shown in Figure 7. In the measurement, no hysteresis was observed. The correlation coefficient was found to be 0.999 for both cases. As suggested in [29], UMFOPS does have a temperature-dependent characteristic. Therefore, the temperature characteristic observed in this experiment may be caused by the sensor structure. The sensitivity was found to be 2.36 nm/\({}^{\circ}\)C for the sensor alone and 2.61 nm/\({}^{\circ}\)C for the sensor with the test piece. The characteristics of the developed sensor are summarized Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Value** \\ \hline Dimension & 3 \(\times\) 2 \(\times\) 0.5 mm \\ Force range & \(\pm\) 25 N \\ Range of wavelength shift & \(-\)66.7 to 60.8 nm \\ Correlation coefficient of the output & 0.999 \\ Resolution & 0.235 \%FS \\ Temperature characteristic & 2.36 nm/\({}^{\circ}\)C or 0.94 nm/N \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of proposed fiber-optic force sensor.  
Figure 6: Load test result. A linear output characteristic is suggested with a correlation coefficient of 0.998.
## 4 Conclusions  
The paper introduces a novel passive control approach for mitigating the self-induced instabilities in decelerated swirling flows using an adjustable diaphragm. We performed extensive experimental investigations without and with a diaphragm placed at the cone outlet. Four diaphragm openings with the following shutter area ratios of 30%, 50%, 60% and 70% are selected. The experimental investigations are performed on a swirling flow test rig. The unsteady pressure signals are acquired on the cone wall to assess the performances of the new control method. The pressure recovery on the cone wall is obtained based on mean values while the dynamic behavior is determined from the unsteady part of the recorded signals. Next, both plunging and rotating components are discriminated based on the acquired unsteady pressure signals.  
The conclusions are summarized as follows:  
1. The pressure recovery on the cone wall is significantly improved using the diaphragm method regarding the swirling flow (e.g., the pressure recovery on the cone wall is more than twice of that on the L2 level, which is located in the middle of the cone).
2. The diaphragm method decreases the amplitude of the pressure fluctuation up to 45% while the frequency remains practically unchanged.
3. The bubble-type vortex flow configuration occurs when the diaphragm shutter area ratio of 70% (d = 0.088 m) is selected. This vortex configuration is less harmful than the swirling flow with the helical vortex rope.
4. The amplitude of the rotating component is significantly diminished when the shutter area of the diaphragm is increased. In contrast, the amplitude of the plunging component is slightly amplified when the diaphragm shutter area is increased.
5. The mitigation of this plunging component is the subject of another research study using a pulsating water jet control method [51].  
The dimensionless equivalent amplitudes obtained with this passive control method are compared against an axial water jet control method. The dimensionless equivalent amplitudes associated to the diaphragm method with the shutter area ratio of 50% are smaller than values with axial water method with 10% jet discharge. One can conclude that the novel proof of a concept examined in this paper brings valuable benefits if it fulfils a good balance between the dynamical behavior and the hydraulic losses. The diaphragm method is simple, robust, low-cost, is easy to implement on a real turbine, and is easily operated.  
This passive control method can be appropriately adjusted in terms of the operating conditions. We recommend that the diaphragm be operated with the shutter area ratios from 40% to 60% for a swirling flow configuration (as investigated in this paper) to strike a good balance between the dynamical behavior and the hydraulic losses. In addition, we recommend the diaphragm be switched on at the recommended operating conditions, while it should be retracted to the cone wall if the hydraulic turbine operates around the best efficiency point.
# New Results on Neutrosophic Extended Triplet Groups Equipped with a Partial Order
## 3 Partially Ordered NETGs
Then  
\[neut(b)=c,\ \{anti(b)\}=\{a\},\ b^{-1}=a;\]8  
It follows by (1) that  
\[neut(b)=c,\ \{anti(b)\}=\{a\},\ b^{-1}=a;\]9  
By (3) and Lemma 4, we have  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]0  
and so  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]1  
that is, \(c*a^{-1}\in P\). Thus, \(a\leq c\). Therefore, \(\leq\) is a partial order on \(G\). To see that it is compatible, let \(x\leq y\). Then \(y*x^{-1}\in P\) and it follows by (1) and (4) that, for every \(a\in G\),  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]2  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]3  
which shows that  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]4  
It follows that \(\leq\) is compatible. Finally, note that \(\forall\ a\in G\),  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]5  
so \(P\) is the associated positive cone. Suppose now that \((G,\leq)\) is a chain, then for every \(a\in G\), we have either  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]6  
It follows by Lemma 3 that  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]7  
Thus \(G=P\cup P^{-1}\). Conversely, if \(G=P\cup P^{-1}\), then for all \(a,\ b\in G\), we have  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]8that is,  
\[neut(c)=c,\ \{anti(c)\}=\{c\},\ c^{-1}=c;\]9  
Hence, we have either \(b\leq a\) or \(a\leq b\). Therefore, \((G,\leq)\) is a chain. By the following example, we clarify the above proposition as:  
**Example 2**. : _Let \(G=\{a,\ b,\ c\}\). Define multiplication \(*\) on \(G\) as shown in Table 2, where \(a,b,c\) to label the elements in the po-NETG and the multiplication \(*\) among these elements._  
_It is easy to verify that \((G,*)\) is a WCNETG and \((G,*)\) satisfies neutrosophic cancellation law, in which_  
\[neut(1)=1,\ \{anti(1)\}=\{a,\ b,\ c,\ 1\},\ 1^{-1}=1.\]0  
\[neut(1)=1,\ \{anti(1)\}=\{a,\ b,\ c,\ 1\},\ 1^{-1}=1.\]1  
\[neut(1)=1,\ \{anti(1)\}=\{a,\ b,\ c,\ 1\},\ 1^{-1}=1.\]2  
\[neut(1)=1,\ \{anti(1)\}=\{a,\ b,\ c,\ 1\},\ 1^{-1}=1.\]3  
_Let \(P=\{a\}\), then \(P\) satisfies all conditions mentioned in Proposition 9. Define the relation \(\leq\) on \(G\) by \(x\leq y\Leftrightarrow y*x^{-1}\in P\), then \(\leq\) is a partial order on \(G\) and \((G,\leq)\) is a antichain. Obviously, \(P\) is the positive cone of \(G\) with respect to this partial order \(\leq\)._  
**Proposition 10**. : _Let \((G,*)\) be a po-WCNETG. Then \(\forall\ x\in G,\ \forall y\in G,\ x\leq y\) implies \(y*x^{-1}\in P_{G}\)._  
Proof. : Let \(\forall\ x\in G,\ \forall y\in G\). If \(x\leq y\), then \(neut(x)=x*x^{-1}\leq y*x^{-1}\), hence, by Proposition 4 and Lemma 1, we have \(neut(y*x^{-1})=neut(x^{-1})*neut(y)=neut(x)*neut(y)\leq(y*x^{-1})*neut(y)= neut(y)*(y*x^{-1})=(neut(y)*y)*x^{-1}=y*x^{-1}\). Thus, \(y*x^{-1}\in P_{G}\).
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 2 Related Work  
### Data Augmentation  
Data augmentation is a commonly used method in computer vision to expand a dataset, which can improve the robustness of the model to images in different environments. Color transformations are usually achieved by changing the brightness, contrast, and saturation of an image, and for geometric transformations, datasets are usually expanded using operations such as image flipping, panning, and rotation [21]. In addition to the commonly used individual image data enhancement methods, researchers have proposed methods to use multiple images together for data enhancement such as MixUp, CutMix, and AugMix [22]. MixUp mixes any two random images based on the mixing factor and mixes the labels in the same way. AugMix augments the images with a randomized augmentation magnitude, which is then merged to generate a new image. However, the existing data enhancement methods are less effective in small target detection tasks; to solve this problem researchers have proposed new data enhancement methods such as oversampling, copy-pasting, and mosaic data enhancement.
## 3 System Implementation and Performance Evaluation
### In-Memory Cache and In-Disk Cache
Because each block of in-memory cache has timeliness, Memcached checking the information is timely will automatically replace the cache by an LRU cache-replacement policy when the system is full or the cache has been stored for more than one month. With the implementation of the LRU policy for the in-disk cache, the history list has executed this policy in this program. This list records the location and the last access time to this cache. When a cache hits, the program updates the list once. Checking this list, the program can find some cached data that had not been accessed for a long time and will delete that instantly. The user, through the interface of this program, can enter the purge x command to delete cached data which had not been accessed during a certain x days. Figure 13: The meminfo file in Linux. The search results are simultaneously stored in-memory and in-disk, but the in-memory cache has the highest access priority. When the in-memory cache hits, the result will not be retrieved by the in-disk cache and big data tool. When the in-memory cache misses, the result will be retrieved through the in-disk cache and will be automatically written back into the in-memory cache. When both in-memory and in-disk caches have missed, the result will be retrieved by big data tools and written to the in-memory and in-disk caches. ```
Input: \(D_{i}\)+ \(H_{m},H_{mr}\). Output: result. 1 while\(H_{m\!\!\!/\!\!\!/g}=H_{m}\)do
2 call Algorithm 7;
3 while\(H_{m\!\!\!/\!\!\!/g}<H_{m}\)do
4 if(find\(D_{i}\)+ in \(H_{m}\), \(\forall\)\(D_{i}\)+) is true, then update memory_hit_ratio and write information to result;
5 if(find\(D_{i}\)+ in \(H_{m}\), \(\forall\)\(D_{i}\)+) is false, then update memory_miss_ratio and call Algorithm 7;
6 returnresult. ```
**Algorithm 6**********Visit current data (hit or miss) in the memory cache
```
Input: \(D_{i}\)+ \(H_{d},H_{dr}\). Output: result. 1 while\(H_{d\!\!\!/\!\!\!/g}=H_{d}\)do
2 call Algorithm 1;
3 while\(H_{d\!\!\!/\!\!\!/g}<H_{d}\)do
4 if(find\(D_{i}\)+ in \(H_{d}\), \(\forall\)\(D_{i}\)+) is true, then update disk_hit_ratio and write information to result;
5 if(find\(D_{i}\)+ in \(H_{d}\), \(\forall\)\(D_{i}\)+) is false, then update disk_miss_ratio, call Algorithm 1, and result: = execute (assign(\(u,e\)));
6 returnresult. ```  
**Algorithm 7********Visit current data (hit or miss) in the disk cache
## 2 Materials and Methods
### Main Concepts and Definitions  
Let a capillary-porous material of mass \(M\) contain a dry matter of mass \(M_{dry}\) and water of mass \(M_{water}\):  
\[M=M_{dry}+M_{water} \tag{1}\]  
The ratio of \(M_{dry}\) and \(M_{water}\) depends on the processes of interaction with the environment. Obviously \(M_{dry}/M+M_{water}/M=1\). According to the physical meaning, \(M_{dry}/M\)--the concentration of dry matter, \(M_{water}/M\)--the concentration of water in the capillary-porous material (by weight).  
Based on the fact that the test material with mass \(M\) contains water, it is logical to call the ratio \(M_{water}/M\)--the moisture content of the material, determined on a wet basis (w.b.). Accordingly, the ratio \(M_{dry}/M\) determines the dry matter concentration on the same basis (w.b.). Let us denote:  
\[C_{water}^{(w.b.)}=\frac{M_{water}}{M} \tag{2}\]  
\[C_{dry}^{(w.b.)}=\frac{M_{dry}}{M} \tag{3}\]In the literature, dry basis moisture content (or d.b.) \(C_{water}^{(d.b.)}\) is often used as dimensionless characteristics of the state of a capillary-porous material. By analogy with [19; 20; 21], we write:  
\[C_{water}^{(d.b.)}=\frac{M_{water}}{M_{dry}} \tag{4}\]  
Characteristics (2), (3) and (4) are interval variables:  
\[0<C_{water}^{(w.b.)}<1,\;0<C_{dry}^{(w.b.)}<1 \tag{5}\]  
\[0<C_{water}^{(d.b.)}<\infty \tag{6}\]  
The relation between \(C_{water}^{(w.b.)}\) and \(C_{water}^{(d.b.)}\) is found using (2) and (4). For this, taking into account Equation (1), we write the equality \(C_{water}^{(w.b.)}M=C_{water}^{(w.b.)}\big{(}M_{dry}+M_{water}\big{)}\) and divide both of its parts by \(M_{dry}\). Taking into account (4), we get:  
\[C_{water}^{(w.b.)}=\frac{C_{water}^{(d.b.)}}{1+C_{water}^{(d.b.)}} \tag{7}\]  
Equation (7) implies that \(C_{water}^{(w.b.)}\leq C_{water}^{(d.b.)}\).  
Using (7), we find  
\[C_{water}^{(d.b.)}=\frac{C_{water}^{(w.b.)}}{1-C_{water}^{(w.b.)}} \tag{8}\]  
Figure 1 illustrates the relationship between \(C_{water}^{(w.b.)}\) and \(C_{water}^{(d.b.)}\). Note that if the moisture content of the material is low (\(\leq\)0.2), then \(C_{water}^{(w.b.)}\approx C_{water}^{(d.b.)}\).  
Relations (5) and (6), as well as Figure 1 suggest that it is easier to use \(C_{water}^{(w.b.)}\) from Equation (2) as an indicator of moisture in a theoretical study.
## 3 Research Methodology and Model Development
### Study Area and Scope  
As previously mentioned, based on the traffic analysis in the city of Zagreb, a suitable corridor for DLA implementation was selected. This corridor is located in the mid-western part of the city and forms part of the major traffic flow connecting the east with the west of Zagreb. The macro situation of the selected corridor is presented in Figure 1.  
The corridor is approximately 500 m long and, based on its traffic and structural characteristics, it functions as a roadway with the main direction of movement east-west and vice versa. It consists of three traffic lanes, each between 3 and 3.5 m wide--one lane is designated for eastbound traffic, while the remaining two are designated for west-bound traffic.  
The corridor experiences high demand with pronounced congestion at peak hours, which the existing infrastructure and traffic control cannot manage efficiently. It includes four critical signalized intersections:  
1. [label=()]
2. Vukovar Street-Trakoscanska Street-Ozaljska Street;
3. Vukovar Street-Nova Cesta Street;
4. Vukovar Street-Florjana Andraseca Street;
5. signalized pedestrian crossing at Cernicka Street.  
These are presented in Figure 2.  
On the selected corridor, the existing traffic cannot cope with the ability to satisfy and provide optimal traffic flow during morning and afternoon peak hours. Even though the corridor itself consists of four signalized intersections, the DLA was observed and tested on the section from intersections (b) to (c) with the inclusion of a signalized pedestrian crossing (d). To develop a reliable and calibrated simulation model, relevant traffic data needed to be collected and analyzed, which is described in the next chapter.  
Figure 1: The macro location of the selected corridor.
## 3 Results and Discussion
### Water and Sediment Composition
#### 3.3.2 Sedimentary Composition  
The lake sediments were characterized by a predominance of the sand fraction (\(>\)75% in general terms: data not shown, [34]) and low contents of inorganic colloids such as Fe oxyhydroxoxides (Table 2). The organic content C was very high in the superficial part of the lake sediments (0-15 cm), with mean values ranging between 8.6% in Louro and 24.6% in Bodeira. These values decreased with depth, but with alternating organic C-rich and -poor layers (Table 2; see also [18]). The pH was close to neutral, and the redox conditions were anoxic (Eh \(<\) 200 mV; [35]) in all cores (Table 2). The TS content was correlated with the TOC at depth (correlation TOC/TS, rs = 0.739, \(p\)\(<\) 0.001, n = 41), indicating that most of the TS is organic S. Moreover, the relationship between TOC and pyrite S (Figure 2) revealed a clear difference between the Xuno and Bodeira lakes and the Louro and Vixan lakes. For the first pair, the superficial and deep samples always had freshwater characteristics, while the sediment samples from the second pair were more characteristic of marine environments. These results are consistent with the water composition, demonstrating that the Xuno and Bodeira lakes were never salt lagoons.
## 3 Results and Discussion
### Extent of Dust Accumulation and Soil Loss
#### 3.2.4 Implications of Dust Accumulation  
Aeolian dust is of critical importance due to its impact on climate change, the environment, and ecosystems [43; 44]. Dust storms and dust accumulation are major phenomena in arid regions, such as Saudi Arabia. Therefore, exploring the dust accumulation rates and fluxes is important to avoid its adverse impacts. Arid climate, atmospheric circulation, and human activities have been considered the major factors for dust emissions [45]. Dust storms and accumulation are significant concerns, impacting climate, environment, and ecosystem health. Our analysis of dust storms in Al-Baha demonstrated significant variation in dust accumulation across different sites and collection methods. The highest dust deposition was observed using the Marble Dust Collector (MDCO), followed by the Surface Dust Collector (SDC) and the Big Spring Number Eight (BSNE). In this study, soil loss related to wind erosion, wind surface erosion, and dust deposition followed a similar pattern. Notably, S11 exhibited the highest soil loss due to both wind erosion and dust deposition.  
The term \"wind erosion\" refers to the process by which soil particles are detached and lifted away by wind, while \"wind surface erosion\" specifically refers to the loss of soil material that occurs at the surface level due to wind action. The order of soil loss due to these processes was generally highest at S11, followed by S6, S1, and S17. This pattern underscores the impact of high wind speeds and soil susceptibility to erosion on dust accumulation and soil degradation. This pattern is consistent with the findings of [PERSON] et al. [46], where soil loss was also highest in areas with significant wind activity and susceptible soils. This highlights the combined influence of high wind speeds and soil vulnerability to erosion that contributes to increased dust accumulation and soil degradation, mirroring the observations reported in [46].
## 3 Proposed Methodology
### Overview of SA-MPCMOEA  
Algorithm 1 summarizes the main procedure of SA-MPCMOEA. In the evolutionary process, SA-MPCMOEA relies on the following three key mechanisms:  
1. Multi-preference search mode based on evolutionary demand: To coordinate the trade-off between constraint feasibility and objective optimization across different stages of evolution, three preference-based search modes are proposed and switched adaptively based on population feedback. Mode 1 emphasizes the rapid discovery of feasible solutions. Mode 2 simultaneously guides the population toward both the constrained Pareto front and the unconstrained Pareto front. Mode 3 focuses on accelerating convergence when sufficient feasible solutions exist. This mechanism enhances the algorithm's ability to balance feasibility and optimality across different evolution phases.
2. Dynamic assessment strategy: In each generation: Candidate solutions are first evaluated by the surrogate models in terms of both objective and constraint functions. Their prediction confidence is then estimated based on model errors. When the surrogate prediction is reliable, more solutions are evaluated using the surrogate models to save computational resources. Otherwise, the proportion of real evaluations is increased to prevent misleading search directions.This strategy achieves a dynamic balance between solution accuracy and computational efficiency, improving overall optimization performance.
3. Fill sampling strategy based on optimization requirements: During the population update phase: A hybrid sampling scheme is adopted, combining uncertainty maximization with potential promising solutions. The former enhances global model performance. The latter steers the population toward potentially optimal regions. Candidate solutions that are too close to the current population are excluded. This improves the diversity and spatial distribution of the population and avoids premature convergence.  
Figure 1: The overall workflow of SA-MPCMOEA.  
These three components work in synergy throughout different stages of surrogate-assisted evolution, enabling SA-MPCMOEA to demonstrate enhanced adaptability and convergence performance on constrained multimodal multi-objective optimization problems.  
```
0: Population \(P\); archive \(A\)
0:\(P\); \(A\); New samples \(O\)
1:\(C_{\text{pop}}\leftrightarrow P\)
2:\(w=1\); \(w_{\text{max}}=10\)
3: Train global model for objective functions and constraint functions.
4:Search-mode \(=\) condition\((P,A)\)
5:while\(w\leq w_{\text{max}}\)do
6:if Search-mode \(=\) 0 then
7: Parents \(\leftarrow\) Matingselection\(0(P,A)\)
8:elseif Search-mode \(=\) 1 then
9: Parents \(\leftarrow\) Matingselection\(1(P)\)
10:else
11: Parents \(\leftarrow\) Matingselection\(2(P,A)\)
12:endif
13: Off \(\leftarrow\) Reproduction(Parents)
14: Evaluate Off using surrogate models.
15:\(w_{\text{max}}=\) Changewmax
16: Update C\({}_{\text{pop}}\) with Off.
17:endwhile
18:\(X\leftarrow\) Sampling\((P,A)\)
19: Add \(X\) to \(P\).
```  
**Algorithm 1** Pseudo-code for the proposed algorithm