esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experiment Results and Analysis
#### 4.3.3 Results on the TREC-6 Dataset  
We conducted a comparative experiment on the TREC-6 dataset to verify whether our proposed method is equally valid on short-text datasets. Under consistent training rules, the obtained results of the experiments are shown in Table 3. The quantitative consequence shows that APTrans still achieves the best results in short-text classification. The implementation data show that in several methods, such as BERT [10], short text has a great influence on the prediction effect of the model. After the introduction of the replacement token detection training strategy, ELECTRA [49] achieves a higher prediction level. BART [46] creates a new generative architecture by adding random noise, which also has excellent performance. This notion indicates that the random noise fluctuation of the text is also conducive to the improvement in the results. In addition, our APTrans can fully mine the text location information when the text is short and understand the dependency of these statements. Our model achieved a 0.6% improvement compared with the traditional baseline methods. It shows that our model can fully extract semantic information from a corpus and integrate location information, understand semantics, and be competent for short-text classification.
## 3 Results and Discussion
Beyond these depths, the cooling rate continued to decline, becoming insufficient to support the formation of martensite. This behavior aligns well with the experimentally observed hardness profile: a hardened layer of \(\sim\)1.5 mm with high hardness (>300 HV), followed by a steep decrease in hardness to base values (\(\sim\)150 HV) corresponding to the ferrite-pearlite microstructure in the unaffected core. In this way, the interplay between two critical conditions--(1) exceeding the austenitization temperature and (2) ensuring a high enough cooling rate--explains the formation of a relatively shallow hardened layer. This confirms that the sharp drop in hardness is not an anomaly but a direct consequence of the thermal field and transformation kinetics, further validating the numerical model. Microstructural analysis of 20 GL steel samples following EPH (Figure 11) enables a visual evaluation of how different treatment modes impact surface alterations in the material. Initially, the microstructure exhibits characteristics typical of as-cast low-carbon steel, featuring a coarse-grained ferrite-pearlite morphology (Figure 11a) and a corresponding low microhardness of approximately 150 HV. After treatment at 150 V (Figure 11b), the microstructure remains largely unchanged, with only isolated regions of dispersed pearlite observed, leading to a modest increase in microhardness up to 180 HV. This suggests minimal phase transformation, consistent with the temperature profile, which indicates that heating in the surface region was insufficient to induce austenitization. Figure 10: The value of microhardness of steel 20 GL by depth after different EPH modes. At 200 V (Figure 11c), a more noticeable alteration in the microstructure occurs, where the surface takes on a finer grain morphology. Zones of transitional bainite or incomplete martensitic transformation may also be present, correlating with a sharp increase in microhardness to 500 HV. The most substantial changes are observed at 250 V (Figure 11d), where the structure exhibits a typical martensitic morphology with an acicular texture and a maximum microhardness of approximately 800 HV. This indicates that at high voltage, the conditions necessary for complete austenitization and subsequent martensitic transformation are achieved in the surface layers, including a high temperature above 1100 \({}^{\circ}\)C and intensive cooling. Therefore, the presented micrographs corroborate that the effectiveness of EPH for 20 GL steel is directly contingent on achieving critical temperatures and cooling rates. The latter factor, particularly for cast steel, is decisive in the formation of a hardened structure.
# Micro-Actuators with Light and Magnetism Dual-Control for Maneuverable Movements
## 3 Results and Discussion
### The Motion Properties of the Actuators  
To validate the photothermal-actuation hypothesis, micro-actuators with right-angled triangular geometry (length is 10 mm, thickness is 500 \(\upmu\)m) were fabricated via cutting of the composite films, as shown in Figure 3a. The boat prepared by sample 3 was continuously irradiated by laser (100 mW, 532 nm) to achieve fast and smooth motion. The micro-actuators moved a distance of 40 mm at 7.8 s; the speed is about 5 mm s\({}^{-1}\). In addition, the light-driven motion of the micro-actuators at the oil surface was also tested, as shown in Figure 3b. Among the three samples, processing time increased progressively from sample 3 (shortest duration) to sample 2 (intermediate duration), with sample 1 requiring the longest completion time and consequently demonstrating the lowest processing speed. The generation of these different gradients is related to the photothermal conversion of the samples. In other words, the speed of the boat is positively correlated with the temperature of its conversion from light. Sample 3 absorbs the most heat under laser irradiation, and transfers the highest temperature to the water surface, thus forming a larger Marangoni force on the surface. This further confirms the results of our tests on the photothermal properties of the above material.  
Figure 3: The motion properties of the actuators. (**a**) The forward motion of the boat. (**b**) The time comparison of the three samples for 40 mm forward motion under green laser of 532 nm, 100 mW.  
Figure 2: Photothermal properties of C/Fe\({}_{3}\)O\({}_{4}\)/PDMS film. (**a**) Optical photo and thermal image of sample 3 irradiated by laser on water surface. (**b**) Maximum temperature changes in three samples after 60 s of irradiation by 500 mW cm\({}^{-2}\). (**c**) Comparison of temperature changes in three samples after 60 s of irradiation at different laser powers. (**d**) Maximum temperature of sample 3 at different laser power.
## 8 Discussion
In [63], a laboratory demonstrator was developed to verify the optical inter-satellite linkages of the Kepler constellation, which links satellites in a GNSS constellation for optical range, time transfer, and data transmission. The recent initiatives to create orbits for a constellation of satellites linked by optical links were explored in [64], including progress on an orbit determination algorithm and a linking rule to schedule communication between constellation components in support of continuous and reliable synchronization capabilities. Considering this, future research could be focused on the application of laser inter-satellite links in the proposed method and the processing of signal sources mixing laser and high-frequency radio frequency ISL signals.
## 4 Architecture of Software Receiver
### Software Architecture
#### 4.1.6 Weight and Sum  
After a set of updated weights is obtained from adaptive MVDR beamforming function, the weight-and-sum function performs the combination of IF data as Equation (1). The results are sent to software correlator as composite IF data input. Assembly code using SIMD instructions implements the weight-and-sum operation by an architecture shown in Figure 10. By arranging the complex data format, one multiply-and-add (MADD) instruction processes the complex multiplication of two samples and outputs a product including I and Q components. In the end, all products are summed as composite IF data.
## 3 Comparison among the Routing Protocols and Open Issues
The advantage of hierarchical networks over flat networks is apparent, because in the former network data aggregation could be conducted at cluster head nodes. These nodes collect the sensed messages from its member nodes, and remove the redundant part, thus reducing the total messages towards the sink nodes. By this means, the network energy efficiency is improved. **Clustering Manner**. \"Proactive\" means that the clustering of the network is operating before the network operates. Because the clustering is carried out in the entire network and it needs a long time to maintain, it will create more energy cost than \"reactive\" clustering which is triggered on demand, such as the occurrence of some event. In some emergent cases, the performance of \"reactive\" routing is not time-sensitive enough. **Intra-cluster Topology**. In a cluster, the single hop topology can reduce the end-to-end delay to a certain degree, whereas a significant advantage of the multi-hop topology is energy-efficiency. Especially in DMSTRP [34], the topology of the spanning tree, which consists of the multi-hop structure, not only reduces the transmission energy through decreasing the average transmission distance, but also alleviates the collisions in clusters with a schedule scheme utilizing the tree structure. **Cluster Head Election**. According to the different objectives of each protocol, these protocols have different ways of electing the cluster heads. In ONCP, for instance, \"residual energy\" is chosen as the criteria to select cluster head to ensure that the cluster head has enough residual energy to process and deliver data packets. That makes the nodes energy-balanced to a certain degree. **Multi-Path Routing**. Multi-path routing means the traffic is delivered along several paths in order to balance the energy consumption of sensors along the single path. By this method, the data packets could still be delivered successfully in the case of path failure, thus ensuring the reliable delivery of packets. However, a deficiency is that much more overhead may be incurred owing to several sensor nodes must be selected as the next hops. In hierarchical routing protocols, some sensor nodes are grouped to efficiently relay the sensed data to the sink. The cluster-head plays the specialized role of performing data aggregation and sending it to the sink on behalf the nodes within its cluster. Thus, how to form the cluster is a more interesting and essential research issue concerning such protocols so that the energy consumption and various communication metrics such as latency are optimized. In addition, due to the number of sensor nodes is substantially increased in large-scale WSNs, the nodes nearby the sink will assume more data forwarding tasks so the energy of these nodes is depleted rapidly. That makes the hierarchical routing protocol design challenging.
# Loop-Type Field Probe to Measure Human Body Exposure to 5G Millimeter-Wave Base Stations
## 3 Probe Fabrication and Measurement
### Expansion to Probe Array  
Owing to its small size, the single probe can be easily arrayed to measure PD at multiple points in the free space. Figure 8 shows the array probe in which a total of five single probes are assembled. Each probe is connected to the interface board through a right-angle PCB. As shown in Figure 8, the right-angle PCBs place the probes such that they have the same distance between them to transmit the output voltage of the RMS power detector to the interface board. The interface board displays PDs measured at each probe, in which PD is calculated using (2). The interface board also displays PD after averaging all measured PDs. As shown in Figure 8, all the loop antennas are inside the circular area that has 4 cm\({}^{2}\). According to [8], the PD limitation over 6 GHz should be compared with a PD that is averaged over 4 cm\({}^{2}\) area. For this, the PD should be repeatedly measured at multiple points to average all measured PDs. No other measurements, however, are required for the proposed array probe. Instead, a single measurement is enough to obtain the averaged PD.  
Figure 7: Power density measurement results of fabricated single probe.  
Figure 9 shows the averaged PD for the same horn antenna with the slot array that radiates the accepted power of \(-16\) dBm. For comparison, it also shows the simulated PD. The simulated PD was obtained by averaging PDs that were simulated at the same positions where each probe composing the array probe is located. As before, the proposed probe measures PD close to the simulated one with a maximum error of about 1.3 dB.  
Figure 10 shows the measured frequency-dependent characteristics of the proposed probe array. We analyzed the changes in the probe output value when the proposed probe array receives frequency components other than 28 GHz. The proposed loop-type antenna is a non-resonant antenna that receives all frequency bands without impedance matching. Instead, the main factor causing the change is the probe output in the frequency response of the LNA gain of the processing circuit. The LNA used in the processing circuit has a flat gain in the mmWave band, so the change with frequencies at the same distance is insignificant [15]. However, in the near-field region close to the reference source, differences in the PD can occur due to the change in the length of the wavelength.  
Figure 8: Probe assembled into array type.  
Figure 9: Power density measurement results of the probe array.
## 2 Data and Methodology
### Profiles with Warm Cloud Layers
Figure 2b provides the ratios of profiles with warm cloud layers to all cloud profiles, indicating the warm cloud fraction. For the annual average, the majority of warm clouds appear between 60\({}^{\circ}\) N and 60\({}^{\circ}\) S. At higher latitudes, the cloud cover contribution from warm clouds is nearly negligible. The regions with high values, exceeding 50%, are mostly within subtropical oceans. In particular, the warm cloud fractions approach 70% at the northeastern Pacific and northern Atlantic, and they even approach 90% at southeastern Pacific Ocean, southern Atlantic Ocean, and central Indian Ocean. Most of the above regions are well known for their semipermanent subtropical marine stratocumulus sheets, suggesting that stratocumulus significantly contribute to the warm clouds there [25]. It is evident that, although these several regions have the maximal occurrences of warm clouds throughout the globe, the total cloud cover over there is just moderate as compared with the distribution of total cloud cover shown in Figure 2a. This is probably due to the absence of mixed-phase or ice clouds in these regions, and warm clouds thus become the only component of total cloud cover. In other regions,  
the fractions of warm clouds are very low. Warm cloud fractions generally decrease westward within each ocean, exhibiting a notable east-west gradient. Furthermore, Figure 2c shows the ratios of profiles with only warm cloud layers to those with warm cloud layers. The regions with high quantities are almost consistent with those of warm cloud fraction. Their spatial correlation is high, with a correlation coefficient of about 0.76. In the five regions, the maximum quantities exceed 90%. However, it is noteworthy that the quantities are not very low in other regions, mostly above 50%, which implies ubiquitous occurrences of the overlapping of low-level warm clouds and upper non-warm clouds on the global scale. In the zone between 60\({}^{\circ}\) N and 80\({}^{\circ}\) N, although the profiles with warm cloud layers are few, this fractional ratios are a bit notable. There are exceptionally low values over the narrow regions adjacent to west coast of continents. These should be attributed to the deficiency of CPR for detecting clouds at very low levels, which are mostly coastal status and they occur very frequently in these regions [24; 28; 29]. Figure 2: Ratio of cloud profiles among total profiles (**a**), ratio of profiles with warm cloud layers among cloud profiles (**b**), and ratio of profiles with only warm cloud layers among those with warm cloud layers (**c**), in each \(2.5^{\circ}\times 2.5^{\circ}\) grid over global oceans.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 2 Petroleum Products
### Mechanism and Influential Factors for Microbial Biodegradation
#### 2.3.2 Environmental Factors  
Environmental factors play a crucial role in the remediation of contaminated sites. These factors, along with the microbial and physicochemical characteristics of the pollutant, determine the success of any bioremediation process. Factors such as the type of soil and the organic content of the soil influence the adsorption potential of the pollutants to the adsorbent [19]. In the sorption processes, absorption is termed as a similar process whereby a contaminant enters the substantial mass of the soil matrix. However, for both adsorption and absorption, there is a reduction in the amount of the pollutant. In this case, most microorganisms and the rate at which the chemical is metabolized is proportionately reduced [6]. Differences in penetrability of the unsaturated and saturated zones of the aquifer matrix may then influence the trajectory pattern of the fluids and contaminant passage in groundwater. As a result, the matrix's potential to distribute gases such as oxygen, methane, and carbon dioxide is reduced in fine-grained sediments, causing soils to become more soaked in water. This could have an impact on the speed and form of biodegradation that occurs.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 4 Comparative Analysis and Technical Difficulties
### Technical Difficulties and Solutions for Linewidth Characterization
#### 4.2.1 Coherent Envelope Demodulation Based on an Iterative Algorithm  
Coherent envelope demodulation based on an iterative algorithm divides the short-fiber delay self-heterodyne power spectrum into three parts, as shown in Figure 12a: Lorentz line spectrum \(S_{1}\), periodic modulation function spectrum \(S_{2}\), and delta function spike spectrum \(S_{3}\).  
\[S(f,\Delta f)=S_{1}S_{2}+S_{3} \tag{28}\]  
\[S_{1}=\frac{P_{0}^{2}}{4\pi}\frac{\Delta f}{\Delta f^{2}+\left(f-f_{0}\right) ^{2}} \tag{29}\]  
\[S_{2}=1-\exp(-2\pi\Delta f\tau_{d})\{\cos[2\pi(f-f_{0})\tau_{d}]+2\pi\Delta f \tau_{d}since[2\pi(f-f_{0})\tau_{d}]\} \tag{30}\]  
\[S_{3}=\frac{P_{0}^{2}}{2\pi}\exp(-2\pi\Delta f\tau_{d})\delta(f-f_{0}) \tag{31}\]where \(P_{0}\) is the output power of the measured laser, \(f\) is the frequency of the beat frequency signal, \(f_{0}\) is the frequency shift, and \(\Delta f\) is the measured laser linewidth. \(S\) (\(f\), \(\Delta f\)) is the result of the Lorentz linetype spectrum \(S_{1}\) being modulated by a periodic function \(S_{2}\), where the period and amplitude of the \(S_{2}\) function are \(1/\tau_{0}=c/nL\) and \(\exp(-2\pi\Delta f_{0})\), respectively; these two parameters are determined by the delay length and the tested linewidth. Using a step-by-step iterative algorithm to estimate the linewidth and achieve coherent envelope demodulation, the process is shown in Figure 13:  
The algorithm loops continuously until the estimated linewidth \(\Delta v_{est}\) converges. After multiple iterations and constant revisions, \(\Delta v_{est}\) is used to obtain more accurate estimates.  
The method effectively avoids the effects of spectrum broadening and phase noise fluctuations caused by \(1/f\) noise, resulting in more accurate measurement results. However, this algorithm requires multiple iterations and still needs to be improved in terms of  
Figure 12: (**a**) Composition of the beat frequency power spectrum [98], (**b**) coherent envelope spectra of different fiber lengths [99], (**c**) schematic diagram of the valley of the envelope spectrum submerged by background noise [99], (**d**) contrast difference between the second peak and the second valley of the envelope spectrum (CDSPST) [98].  
Figure 13: Iterative algorithm flowchart based on coherent envelope spectrum demodulation.  
computational speed. In addition, the shorter the delay fiber is, the more pronounced the coherent envelope spectrum, as shown in Figure 12b. If the delay fiber is too short, it will cause the \"valley\" of the coherent envelope spectrum to be submerged by background noise, as shown in Figure 12c, requiring a more sensitive detector and a spectrum analyzer with lower background noise. It is necessary to choose an appropriate range of fiber lengths for linewidth characterization.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 2 Cross-Domain Fusion Constellation Design Method
### Basic Constellation Design
#### 2.2.2 Navigation Constellation Generation Method  
LEO navigation enhancement is to improve the accuracy, integrity and space-based monitoring capability of existing satellite navigation systems by using LEO constellation. The independent navigation mode is adopted in the navigation domain adopts, and the available navigation accuracy factor, geometric dilution of precision (GDOP) is taken as the main index. The mixed orbit designs of inclined orbit and polar orbit will be used [17]. Since only LEO satellites in the independent navigation mode are considered, the paper only considers LEO constellation, and the form of the weight matrix of GDOP can be described as follows [17].  
\[H^{\text{T}}\!\!=\!\left[\begin{array}{ccc}\frac{r_{\text{loo},1}-r_{\text {user}}}{\|r_{\text{loo},1}-r_{\text{user}}\|}&\frac{r_{\text{loo},2}-r_{ \text{user}}}{\|r_{\text{loo},2}-r_{\text{user}}\|}&\cdots&\frac{r_{\text{loo },n}-r_{\text{user}}}{\|r_{\text{loo},n}-r_{\text{user}}\|}\\ 1&1&\cdots&1\end{array}\right]_{4\times n}. \tag{4}\]In (4), \(n\) refers to the number of visible satellites in the LEO constellation, \(\mathrm{r_{leo}}\) and \(\mathrm{r_{user}}\), respectively, refer to the LEO satellite position vector and user position vector under the Earth Centered Earth Fixed coordinate system. Generally speaking, good navigation and positioning performance can be obtained when \(GDOP\leq 5\)[16]. The calculation of global GDOP is as follows [17].  
\[GDOP_{globe}=\frac{\sum\limits_{-\pi/2}^{\pi/2}\sum\limits_{-\pi}^{\pi}\sum \limits_{0}^{T}GDOP(lat,lon,Epoch)}{N_{lat}N_{lon}N_{t}}. \tag{5}\]  
\[k_{\min}\geq 4. \tag{6}\]  
The above constraint means that the navigation resources in CDFC should be at least four times the coverage; where \(T\) is the constellation design planning period, and \(N_{lon}\) and \(N_{lat}\) distribution are the division of longitude and latitude of the earth grid.