esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Phase According to Design Science Research
### Assessing Utility and Applicability
The stepwise backward elimination of statistically non-relevant predictors in the regression procedure revealed that besides the dominant container typology (D), a total of three predictors have significant impact on the response: (A) Number of containers, (B) Overall number panels to be modified, and (C) Number of entire walls to be modified. These results are visualized in the Pareto chart in Figure 17.  
The different regression equations in function of the dominant container typology are presented in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & **Number** & **Dominant** & **Overall Number** & **Number of** & **Panel Modification** \\  & **Containers** & **Container** & **Panels to be** & **Full Walls to** & **Effort (Estimated by** \\  & **Typicalology** & **Modified** & **be Modified * & **Experts) [d]** \\ \hline Project1 & 33 & BM \(16^{\prime}\) & 32 & 7 & 7 \\ Project2 & 25 & BM \(16^{\prime}\) & 32 & 4 & 7 \\ Project3 & 20 & BM \(20^{\prime}\) & 40 & 4 & 7.5 \\ Project4 & 102 & BM \(20^{\prime}\) & 40 & 10 & 13 \\ Project5 & 44 & BM \(20^{\prime}\) & 40 & 12 & 8 \\ Project6 & 55 & BM \(16^{\prime}\) & 32 & 7 & 9 \\ Project7 & 25 & BM \(16^{\prime}\) & 32 & 7 & 6 \\ Project8 & 30 & BM \(20^{\prime}\) & 40 & 10 & 7 \\ Project9 & 16 & BM \(16^{\prime}\) & 32 & 8 & 5.5 \\ Project10 & 2 & BM \(30^{\prime}\) & 7 & 1 & 2 \\ Project11 & 4 & BM \(30^{\prime}\) & 4 & 0 & 2 \\ Project12 & 42 & BM \(20^{\prime}\) & 40 & 8 & 8.5 \\ Project13 & 3 & BM \(16^{\prime}\) & 12 & 3 & 1.5 \\ Project14 & 8 & BM \(10^{\prime}\) & 20 & 3 & 2 \\ Project15 & 10 & BM \(16^{\prime}\) & 32 & 3 & 5 \\ \hline \hline \end{tabular}  
* Walls with more than three panels to be modified are considered as full walls.  
\end{table} 
Table 4: Data set for statistical analyses. By applying the models determined by the RSM approach, a more accurate effort can be calculated than before, as the comparison to the pilot project findings shows (see Table 3), (with: dominant container typology: BM 20'; number containers: 28; overall number panels to be modified: 60; number of entire walls to be modified: 11):  
\[y=\ -0.797+0.08429\times 28+0.1746\times 60-0.1589\times 11 \tag{1}\]  
\[y=10.29\ days\ \sim\ 2\ working\ weeks \tag{2}\]  
Thus, the utility according to DSR is demonstrated not only with regard to time savings for the processes of _assignment of physical containers to the drawing_ and _final creation of 2D plans and updating the repository_ (see Table 2), but also within the automated calculation of the bid estimation. However, these statistical analyses are based on expert knowledge when this study was carried out, and should be verified in the future by real historical project data.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 2 Multicarrier Modulations and FIR Filter
### Modulation Waveforms
#### 2.1.3 Gfdm  
The GFDM modulation waveform, as a new 5G multicarrier modulation technique, was proposed by [PERSON] et al. in 2009 [41]. Compared to OFDM, GFDM aiming to generalize traditional OFDM is based on separate block modulation, which makes it more flexible by configuring different subcarriers and symbols. To accommodate various types of services, GFDM can work with different prototype filters and different types of CPs. The design of the conforming prototype filter reduces the sidelobe interference, and the modulation process is converted from linear convolution to cyclic convolution through tail biting operation, thus shortening the length of CP. Similar to FBMC, GFDM is also robust against time-frequency offset but has a low PAPR. However, each subcarrier in GFDM modulation does not keep the orthogonality in the frequency domain and ICI is introduced even under ideal channel, which increases the complexity of the receiver algorithm and meanwhile raises BER.
# Lightweight Design and Evaluation of Square Reflector
## 2 Square Reflector Lightweight Design
### Mirror Structure Design  
At present, the materials that can be used as space optical components in space mirror blanks are as follows [7]: fused silica glass (fused silicon), glass ceramic (Zerodur), silicon carbide (SiC), beryllium mirror (Be), and ultra-low expansion glass ULE (ultra-low expansion). Their material parameters are shown in Table 1.  
It can be seen from Table 1 that the ultra-low expansion glass ULE has a low thermal expansion coefficient, and its absolute thermal expansion coefficient changes very little within 5 \({}^{\circ}\)C\(\sim\)35 \({}^{\circ}\)C. The material belongs to titanium dioxide-silicate glass, and its biggest feature is that it is easy to melt, which provides more possibilities for the lightweight structure of optical components [8; 9; 10].  
The basis for the design of the basic structure of the mirror is to ensure rigidity and surface accuracy, reduce weight, and have stress relief capabilities. According to the characteristics of the ULE glass material, which is easy to fuse, the mirror is designed to consist of three parts: the top mirror, middle mirror, and bottom mirror, which are combined by fusing, as shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **Coefficient of Linear Expansion** & **Modulus of Elasticity** \\  & **g/cm\({}^{3}\)** & **\(\mathbf{\alpha}\times\mathbf{10}^{-6}\)/K** & **Gpa** \\ \hline Fused silicon & 2.2 & 0.55 & 67 \\ Zerodur & 2.53 & 0.05 & 91 \\ Be & 1.87 & 11.3 & 287 \\ Sic & 3.12 & 2.24 & 400 \\ ULE & 2.21 & 0.03 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of mirror material parameters.  
According to the surface structure of the optical element, the top mirror surface is an off-axis aspheric surface, which can be used as an interlayer for lightweight structural design, and the purpose of the bottom layer is to form a closed structure and improve the rigidity of the optical element.
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
#### 3.2.3 Microwave Radiometer  
SST and OSW measurements derived from microwave radiometers can be used to study OSC [112; 132]. Compared to TIR radiometers, the coarse spatial resolution of microwave radiometers reduces the performance of sequence image analysis and MCC techniques for OSC estimation [133]. To resolve this issue, the Surface Quasi-Geostrophic (SQG) theory and multifractal analysis have been employed to retrieve OSC from a single SST microwave image [133; 134]. To this end, the OSC can be reconstructed by converting the SST map to OWH using a transfer function obtained by the multifractal analysis or SQG approach [135]. In the multifractal analysis, the fact that SST multifractal structuresare closely related to OSC is considered, and reasonable stream function approximation is allowed [133; 134]. However, this technique cannot provide information on OSC orientation. In contrast, the SQG approach can reconstruct the 3D OSC field and is more commonly used by researchers [134]. For example, [PERSON] and [PERSON] [132] assessed the OSC, reconstructed from SST, using the SQG method at the global scale. The results indicated that the retrieved OSC could be improved by providing information on the energy spectrum of altimetry data.
## 3 EDFA's Space Qualification and Test
### Thermal Vacuum Tests
The trend that optical power follows with temperature can be identified and characterized depending on the initial temperature before laser pump; hence, depending on the operation time, the lasercom-terminal transmitted power can be tuned this way through the EDFA settings in order to satisfy the link-budget requirements. LEO-ground downlinks occur during passes shorter than 10 min, and typically shorter than 6 min [3], with an average duration of 5 min [5]. As the TVT measurement in Figure 7 shows, the effective output power can be guaranteed to be over 2 W during the whole LEO-ground lasercom link by starting to transmit at 2.2 W, which is allowed by the amplifier con  
Figure 12: EDFA’s module full functional verification during 24 h thermal vacuum test with cold and hot cycles. can be confirmed that this EDFA is suitable for LEO-ground communications, which is the main application of this amplifier. However, the 6U-CubeSOTA version (see Figure 1, right) can be applied to intersatellite links from LEO to the geosynchronous orbit (GEO) as well in order to transmit data to the ground through a medium Earth orbit (MEO) or GEO relay in the middle. The main advantage of this application is the much higher link availability because of the longer time under direct line of sight, which on average is 57 min between LEO and GEO [5], as opposed to the 5 min of LEO-ground. It has been concluded that the CubeSat's EDFA can support this application as well if the starting output power is set to 2.5 W by increasing the pump-laser current, which is the maximum achievable power, reaching a minimum optical power around 2 W after 1 h. It must be noted that this is the worst case, and in the other cases, where initial temperature is lower and/or operation time is shorter, optical power will be well above 2 W during the full link. Since there is enough time to cool down the EDFA between one LEO-GEO contact and the following one, in practice, this amplifier can guarantee continuous operation in this scenario, satisfying the most important advantage of LEO-GEO, i.e., its superior link availability.
### Progressively Residual Learning Problems for Inverse Halftoning  
Progressively residual learning (PRL) [23; 25] can be an alternative for solving sudden changes in brightness, as mentioned in the previous subsection. In PRL, the base layer whose brightness changes smoothly is first recovered; subsequently, the remaining detail layer is predicted.  
\[\mathbf{x}^{(d)}=f_{\theta}^{PRL,r}(\mathbf{x}^{(b)},\mathbf{x}^{(i)})\ \approx\mathbf{x}^{o}-\mathbf{x}^{(b)},\ \text{where}\ \mathbf{x}^{(b)}=f_{\theta}^{PRL,b}\Big{(}\mathbf{x}^{i}\Big{)} \tag{3}\]  
\(\mathbf{x}^{(b)}\) and \(\mathbf{x}^{(d)}\) indicate the predicted base and detail layers, respectively. For inverse halftoning, the input halftoned image \(\mathbf{x}^{i}\) cannot be used as the base layer. However, in PRL, the input halftoned image \(\mathbf{x}^{i}\) is first converted into the base layer \(\mathbf{x}^{(b)}\) through the pretrained DCNN \(f_{\theta}^{PRL,b}\). The generated base layer resembles a low-pass-filtered image, and it can be considered as an approximation of the original image. If the detail layer, which is defined as \(\mathbf{x}^{o}-\mathbf{x}^{(b)}\), is used as the residual layer, then a narrow brightness range can be guaranteed. This implies that residual learning, \(f_{\theta}^{PRL,r}\), is possible. The additive model of Equation (2) can be used reasonably with PRL for inverse halftoning. For reference, the input halftoned image \(\mathbf{x}^{(i)}\) can be used with the base layer \(\mathbf{x}^{(b)}\), as shown in Equation (3), to estimate the detail layer, thereby compensating for information loss in the predicted base layer.  
However, PRL [23; 25] applied to inverse halftoning has not been able to present a new deep learning model from the viewpoint of creating base and detail layers. In PRL, \(f_{\theta}^{PRL,b}\) is trained to generate the base layer. However, the output images of \(f_{\theta}^{PRL,b}\) cannot be regarded as the base layer. Instead, the output images correspond to the final reconstructed images because they appear similar to the original images. Moreover, the predicted base layers appear better visually than the reconstructed images using traditional inverse halftoning methods based on dictionary learning [19] and look-up tables [13]. If the image quality of the base layers decreases to the level of Gaussian blurring of the original images, then conventional PRL cannot yield satisfactory results. In summary, the PRL hitherto developed for inverse halftoning merely applies inverse halftoning twice in succession.
# Luminescence Tuning of Fluorinated Bistolanes via Electronic or Aggregated-Structure Control
## 1 Introduction  
Organic luminescent materials can be broadly classified into two groups: solution-state and solid-state luminophores. Solution-state luminophores, such as rhodamine and fluorescein, are applicable in biomedical imaging and optical probes [1; 2; 3; 4], whereas solid-state luminophores are typically employed in lighting devices, light-emitting diodes, and electroluminescence devices [5; 6; 7; 8; 9]. To realize practical applications, significant efforts have been devoted to the discovery of novel luminescent materials, and as a result, various kinds of luminescent molecules have been developed [10; 11]. However, there have been few systematic studies on the relationship between molecular structure and luminescent behavior, even though luminescence tuning is a key feature for practical light-emitting applications.  
Over the past few decades, numerous fluorine-containing organic materials have been developed because chemical reactivity and material properties are dramatically influenced by the unique characteristics of fluorine, e.g., the largest electronegativity of all elements, the second smallest atomnext to hydrogen, and the strong dissociation energy of C-F bonds [12; 13]. Our group has extensively studied efficient synthetic protocols for organofluorine molecules containing CF3 groups [14; 15] and CF2 CF2 fragments [16; 17]. More recently, we have also developed a significant interest in functional materials, e.g., liquid-crystalline (LC) and photoluminescence (PL) materials, based on fluorinated \(\pi\)-conjugated molecules such as pentafluorophenyl-containing toluene [18] and bistolane molecules [19; 20; 21; 22; 23]. Intriguingly, bistolanes **1 aA** and **1 bA** (Figure 1a) exhibited blue-light PL in solution as well as in crystalline (Cr) phases [23]. Remarkably, **1 aA** and **1 bA** in the LC phases under thermal conditions also displayed PL behavior, with the PL intensity and color reversibly changing during the thermal Cr \(\rightleftarrows\) LC phase transition.  
Previous studies by our research group on fluorinated bistolane-type luminophores have demonstrated that the PL behavior depends on the electron-density distribution or aggregated structure. To gain deeper insights into the PL behavior, in this study, we designed six fluorinated bistolanes, viz., **1 aB-D** and **1 bB-D**, by systematically modulating the electron-density distribution. Herein, we describe the molecular design and synthesis of these fluorinated bistolane-type luminophores as well as their LC and PL behaviors. Additionally, the effect of precisely modulating the electron-density distribution or aggregated structure on the PL behavior is discussed.
## 3 Methodology
### Multi-Agent Processing
#### 3.3.3 Multi-Agent Voting Mechanisms  
Since ancient Greece, debate has been used as a method to refine opinions and make informed decisions. Analogous to multi-person debates, multi-agent LLMs could achieve collaboration by forming LLM communities and debating each other [30]. To reduce the interference of stereotyping or pre-trained knowledge, we propose multi-agent voting mechanisms, that is, each agent (LLM) is set a priori as a participant with different preferences, and votes independently on whether the response of a single LLM is a hallucination after a debate occurs. For example, we have the following:  
\"You are a robot responsible for providing home services to users. When making decisions, your first criterion is to protect the user's physical safety. You are wary of unfamiliar objects and usually deal with them by moving them outdoors.\"  
Multiple voting is required when two or more agents disagree about the same event. For example, a user complains of a headache after breathing in cooking fumes. Single agents (LLMs) may generate content suggesting that headaches, in such cases, are often associated with carbon monoxide poisoning. Therefore, multiple agents have to vote on whether to contact emergency services.  
Furthermore, we incorporate chain-of-thought (CoT) reasoning into each agent before voting to provide more references for other agents. For example, consider a scenario where a gas leak is detected in the home, and the group needs to vote on whether to contact emergency services. The following is the CoT of agent 1 before voting:  
* Confirmed through multiple sensors that a gas leak has occurred and the user is at home.
* There is an immediate and serious danger to the safety of the user due to the risk of explosion or poisoning.
* Confirmed that the gas leak cannot be repaired and that the user cannot be evacuated safely.
* Data indicate a serious hazard, and failure to act could result in serious harm to the user.
* Vote in favor of contacting emergency services immediately.
# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
###### Abstract  
Few-shot object detection (FSOD) remains a critical challenge in computer vision, where the limited training data significantly hinder model performance. Existing methods suffer from poor robustness and accuracy, primarily due to scale sparsity and inadequate feature extraction. In this paper, we propose MDA-HAPP, a novel framework built on a transfer learning architecture and a two-stage object detection approach, specifically designed to address these issues. The key innovations of MDA-HAPP include 1. MultiScale-DynaAttention, a novel attention module that enhances feature extraction by integrating multi scale convolutions into channel attention and applying a dynamic pooling ratio to spatial attention, with residual connections to improve robustness; 2. hierarchical adaptive-pyramid pooling, designed based on a spatial pyramid pooling (SPP) structure, extracts multiscale features from intermediate layers and dynamically adjusts pooling strategies. These features are then fed into a dual-branch detection head for comprehensive results.The experimental results on the PASCAL VOC and COCO datasets show that MDA-HAPP achieves significant improvements across different K-shot settings. Specifically, the model demonstrates an up to 9.8% gain in AP75 on PASCAL VOC for K-shot values of 10 and an up to 3.7% improvement on COCO for K-shot values of 30. These results confirm its superior performance in FSOD and highlight its potential for real-world applications.  
Keywords:transfer learning; few-shot object detection; multiscale dynamic attention; hierarchical adaptive-pyramid pooling +
Footnote †: journal: Applied Sciences
## 7 Analysis of Grouting Effect
### Monitoring of Surface Subsidence  
Seven subsidence monitoring points were set on the surface, named YDB-01-YDB-07, respectively. In order to reflect the deformation characteristics of the surrounding rock under excavation conditions as much as possible, monitoring was carried out in time after the excavation. The first measurement was taken after the excavation of Part A-1 on 6 August 2020, with a duration of 37d. During the period from 6 August to 20 August, the cumulative subsidence value of each monitoring point on the surface showed an increasing trend, of which the maximum value was YDB-3, as 3.56 mm. During the period from 21 August to 5 September, the cumulative subsidence value of each monitoring point fluctuated repeatedly. After 5 September, the surface subsidence became stable, while the cumulative subsidence value did not exceed the alarm value by more than 60 mm (Figure 9).  
Figure 8: Locations of deformation test points.  
Figure 7: Relation between grouting flow and time.  
Monitoring of Vault Botanggou tunnel is a long-span tunnel. Three monitoring points for vault subsidence are set at the vault, named YGD-01-YGD-03, respectively. The measurement of YGD-01 began on 6 August 2020. The measurement of monitoring points YGD-02 and YGD-03 began immediately after the excavation of part B-1 had been completed on 17 August. According to the analysis of the measurement data, the cumulative subsidence value of YGD-01-YGD-03 increased gradually, and remained basically stable until 10 September. YGD-01 had the largest cumulative subsidence value of 10.25 mm. From continuous monitoring, it can be seen that the cumulative subsidence value of the three monitoring points for vault subsidence does not exceed the alarm value of 60 mm (Figure 10).  
Monitoring of Horizontal Convergence A total of four horizontal convergence survey lines were set in this section, which are named YSL1-1, YSL1-2, YSL2-1, and YSL2-2, respectively. The YSL1-1 and YSL1-2 measurements started on 6 August 2020. The convergence value increased linearly within 3 days after excavation, and then slowly increased. It was basically stable until 6 September. The cumulative convergence value of YSL1-1 was the largest for 10.78 mm. The measurement of YSL2-1 and YSL2-2 started on 17 August. The convergence value increased linearly within 4 days of excavation and then slowly increased, while it remained basically stable until 10 September. The accumulated value of the four horizontal convergence measuring lines does not exceed the alarm value of 60 mm (Figure 11).  
Figure 11: Relation between horizontal convergence and time.  
Figure 9: Relation between surface subsidence and time.
# Underwater Acoustic Target Tracking: A Review
## 5 Classification Underwater Acoustic Target Tracking Algorithms
### Arithmetic Average  
Because of the complexity of the marine environment, the acquired measurements are usually corrupted seriously by noise, and the measuring error may result in large tracking error even employing filtering techniques. In [74], a novel tracking algorithm is proposed to address the problem of noise interference. The measuring error in bearings is removed by pre-processing progress using arithmetic average. The pre-processing technique efficiently reduces the variance of the noise present in the bearing measurements by taking an average of the present and the projected previous measurements since the natural noise is generally unbiased. To be clear, the example of 2-order pre-processor is shown in Figure 10.  
The detailed computation process can be seen in Equation (15), where \(Z(1)\), \(Z(2)\) and \(Z(3)\) denote the measurements acquired at the time instant 1, 2 and 3 respectively. The corresponding pre-processing result is represented by \(Z^{{}^{\prime}}(1)\), \(Z^{{}^{\prime}}(2)\) and \(Z^{{}^{\prime}}(3)\). \(Z^{1}p(1)\) and \(Z^{1}p(2)\) denote the 1-step predictor measurement of \(Z(1)\) and \(Z(2)\) respectively:  
\[\begin{array}{l}Z^{\prime}(1)=Z(1)\\ Z^{\prime}(2)=\frac{1}{2}\big{[}Z(2)+Z^{1}p(1)\big{]}\\ Z^{\prime}(3)=\frac{1}{2}\big{[}Z(3)+Z^{1}p(2)\big{]}\\ Z^{\prime}(4)=\frac{1}{2}\big{[}Z(4)+Z^{1}p(3)\big{]}\end{array} \tag{15}\]  
The 1-step predictor measurement \(Z^{1}p(1)\) and \(Z^{1}p(2)\) in Equation (15) can be computed by taking an average of one-step differences of previous \(N\) measurements as follows:  
Figure 10: The 1-order pre-processor.  
\[\begin{split} Z^{1}p(1)&=\frac{1}{N}\sum\limits_{i=2-N}^{ 1}[Z(i)-Z(i-1)]\\ Z^{1}p(2)&=\frac{1}{N}\sum\limits_{i=3-N}^{ 2}[Z(i)-Z(i-1)]\end{split} \tag{16}\]  
where the number of \(N\) can be chosen according to the real application. In Equation (16), the negative index measurements refer to the measurements stored before the start of the algorithm.