esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Minimum Expected Delay Based Routing Protocol
### The Expected Time of Meeting and Departing the Sink Node  
Let \(O(x_{o},y_{o})\) be the coordination of the sink node, then the communication range of the sink node is a circular region within the circle C \((x^{2}+y^{2}=r^{2})\). Let P(\(x_{i_{o}},y_{i})\) be the current location of node i, \(v_{i}\) be the speed, and \(\theta_{i}\)\((0\leq\theta_{i}\leq 2\pi)\) be the moving direction. The process of calculating the expected time of node \(i\) meeting and departing the sink node, denoted as \(T_{i}^{S}\) and \(T_{i}^{E}\) respectively, can be categorized into the following three cases:  
(1) If the node \(i\) comes within the communication range of the sink node, then \(T_{i}^{S}=t\), here \(t\) is the current time. The time that they will depart can be calculated out by Equation (1), that is, \(T_{i}^{E}=T_{io}^{E}\).  
(2) If the current moving path of the node \(i\), _i.e._, the ray L determined by P(\(x_{i}\),\(y_{i}\)) and \(\theta_{i}\), does not intersect the circle C (the communication range of the sink node), then \(T_{i}^{S}=\infty\), \(T_{i}^{E}=\)0 since the node \(i\) will never meet the sink node in the near future.  
(3) If the above two cases cannot be held, then the ray L intersects with the communication range of the sink node. That means the node \(i\) is moving toward the sink node and will meet it with considerableprobability. Let \(\mathrm{I}_{1}\) and \(\mathrm{I}_{2}\) be the two intersection points of the ray \(\mathrm{L}\) and the circle \(\mathrm{C}\). Here, we ignore the instance that \(\mathrm{L}\) tangents to \(\mathrm{C}\) for the communication time between the two nodes is too short. Then:  
\[T_{i}^{S}=t+P\mathrm{I}_{1}\big{/}v_{i} \tag{2}\]  
\[T_{i}^{E}=t+P\mathrm{I}_{2}\big{/}v_{i} \tag{3}\]  
where \(\mathrm{PI}_{1}\) and \(\mathrm{PI}_{2}\) are the distance from \(\mathrm{P}\) to \(\mathrm{I}_{1}\) and \(\mathrm{I}_{2}\) respectively, and \(\mathrm{PI}_{1}<\mathrm{PI}_{2}\).
## Results
### Unilateral Impairment Group  
In the subjects with unilateral upper extremity impairment, handling foods (level of dependence, 2.6), using a computer (2.9), cleaning (3.4), and self-exercise (3.4) showed high dependency. Regarding the external robotic arm, handling foods (75.0%, important or very important), using a computer (75.0%), hairdressing (70.8%), and using a smartphone (66.7%) were considered objectively important and subjectively necessary functions. However, moving close items (75.0%, necessary or highly necessary) and dressing (62.5%) were the most highly necessary functions, whereas their importance was relatively rated with lower priority. Assistance in brushing teeth (37.5%, not necessary or of little necessity), switch control (37.5%), making phone calls (33.3%), and eating (33.3%) showed low necessity for external robotic arms. Importance and necessity showed generally higher ratings regarding the upper limb exoskeleton. Handling foods (87.5%, important or very important), self-exercise (87.5%), transfer (87.5%), and using a smartphone (83.3%) were considered objectively important functions. Regarding necessity, handling foods (75.0%, necessary or highly necessary), self-exercise (66.7%), moving close items (75.0%), dressing (66.7%), washing face (66.7%), hairdressing (66.7%), and using a smartphone (66.7%) were rated high. Brushing teeth (66.7%) and eating (62.5%) were relatively rated high in necessity as compared with their low rank in importance. Switch control (37.5%, not necessary or of little necessity), making phone calls (37.5%), and wheelchair control (33.3%) were functions that were considered not necessary for the exoskeleton. The detailed data of the UIG are shown in Table 2, and Figures 1 and 2. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data** & **Unilateral Impairment Group** & **Bilateral Impairment Group** & **Total** \\ \hline No. of subjects & 24 & 24 & 48 \\ Chronic stroke & 24 & 5 & \\ Cervical spinal cord injury & & 18 & \\ Duchenne muscular dystrophy & & 1 & \\ Amyotrophic lateral sclerosis & & 22.2 & 42.6 \\ Male-Female (\(n\)) & 20:4 & 22.2 & 42.6 \\ Average age (years) & 61.2 \(\pm\) 5.3 & 24.8 \(\pm\) 17.0 & 42.6 \(\pm\) 22.2 \\ Average duration since onset (months) & 109.5 \(\pm\) 51.8 & 90.1 \(\pm\) 56.7 & 99.7 \(\pm\) 54.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic data of survey participants.
## 2 Principle and Preparation  
The proposed MZI sensor is composed of a cascade structure of SMF1-MMF1-SHDCF-MMF2-SMF2. A beam of light is coupled into MMF1 through SMF1, and then expands to two cores of SHDCF, and the two beam lights in the SHDCF are coupled to SMF2 through MMF2. The schematic diagram is shown in Figure 1a. The MF is filled into the air hole of the SHDCF by capillary action, as shown in Figure 1b and the cross-section diagram of the SHDCF is shown in Figure 1c. The SMF used in the experiment is commercial SMF with a cladding diameter of 125 \(\upmu\)m and core diameter of 8.2 \(\upmu\)m, and the cladding diameter and core diameter of MMF are 125 \(\upmu\)m and 105 \(\upmu\)m, respectively. The structural parameters of the SHDCF can be referred to in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Core Diameter** & **Cladding Diameter** & **Core Refractive Index** & **Cladding Refractive Index** \\ \hline Value & 9 \(\upmu\)m & 125 \(\upmu\)m & 1.4682 & 1.4627 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters table of SHDCF. The fabrication process of the proposed sensor is shown in Figure 2. First, a segment of SMF and a segment of MMF are cleaved and welded together by an optical fiber cleaver (Fitel, S325) and fusion splicer (Fitel, S179), as shown in Figure 2I. Second, The SMF-MMF section is obtained by cutting with an optical fiber cleaver, and the SHDCF with a flat end face is vertically inserted into the MF for capillary effect filling, as shown in Figure 2 II. Third, SMF-MMF and SHDCF are fused together without collapse. Finally, another MMF-SMF section is fused to the right side of the SHDCF. In this structure, the output transmission spectrum can be described as follows [22]:  
\[I_{out}(\lambda)=I_{1}+I_{2}+2\sqrt{I_{1}I_{2}}\cos(\Delta\varphi) \tag{1}\]  
where \(I_{1}\) and \(I_{2}\) are the light intensity of core 1 and core 2, respectively; \(\Delta\varphi\) is the phase difference between the guide lights in core 1 and core 2, which can be calculated by the following formula:  
\[\Delta\varphi=\frac{2\pi L\Delta n_{eff}}{\lambda} \tag{2}\]  
where \(\lambda\) is the wavelength of operation, \(L\) is the length of the SHDCF, and the \(\Delta n_{eff}\) is the RI difference between core 2 and core 1. Hence, the calculation formula of interference valley wavelength \(\lambda_{k}\) is as follows:  
\[\lambda_{k}=\frac{2\Delta n_{eff}L}{2k+1} \tag{3}\]  
Figure 1: (**a**) Schematic diagram of the sensing structure; (**b**) the effect drawing of magnetic fluid filling in the air hole of SHDCF; (**c**) the cross-section diagram of SHDCF. Figure 2: Fabrication process of the optical fiber magnetic field sensor, (**I**–**IV**) are the preparation steps of optical fiber sensing structure. where \(k\) is an integer. The RI of the MF in air hole (\(n_{MF}\)) will change with the external magnetic field, which changes the reflection condition of the core in the air hole, and finally affects the effective RI of the core in air hole with reference to the research results of [PERSON] et al.
## 2 Materials and Methods  
The Black Sea, with an average salinity ranging from 17 to 18 g/L, exhibits a pronounced stratified structure characterized by distinct biogeochemical layers. The hydrographic pattern consists of low-salinity surface waters from river inflows overlaying deeper, high-salinity waters of Mediterranean origin. A well-defined and persistent pycnocline exists between these layers, limiting vertical mixing to 100-150 m depths. The oxic layer extends to approximately 50 m, coinciding with the euphotic zone and the boundary of the pelagic habitat. This layer supports robust biological processes, with high oxygen concentrations around 300 uM, subject to seasonal variations in nutrients and organic matter from rivers and coastal areas. The mean thermohaline state of the Black Sea is influenced by the balance of water and salinity (surface dilution and salinification by Mediterranean inflow), while air-sea heat exchange drives seasonal variability. In the coastal areas, the oxycline, marking a distinct oxygen gradient, begins at depths of 70-100 m. Below this, the suboxic layer is found at depths of 100-130 m, characterized by reduced oxygen levels (<10 m) and increasing hydrogen sulfide concentrations. Further down, the anoxic layer starts at depths of 150-200 m, devoid of oxygen except for sulfate-reducing bacteria sustained by hydrogen sulfide [47; 48; 49].  
To determine the optimal conditions for maintaining cultures, we adapted the ISO 14669 (1999) [33] requirements to align with the specific conditions typical of the Black Sea environment, as follows:  
* Salinity: 11-18 PSU, reflecting the salinity of the Black Sea based on data from NIMRD;
* Temperature: 20 \({}^{\circ}\)C \(\pm\) 3 \({}^{\circ}\)C;
* Lighting: Up to 2200 lux;
* Oxygen Saturation: Greater than 80%;
* pH: 8 \(\pm\) 0.5.
### Determination of Equivalent Carbon Emission  
The environmental impact assessment of any project is generally represented by the estimation of the carbon emissions caused by activities involved in that project. The term equivalent carbon emission is defined as the equivalent emission of all GHG involved in any activity with the carbon dioxide emission. The global warming potential (GWP) to CO\({}_{2}\) is assigned as one, whereas the GWP of other GHG is more than one. The equivalent CO\({}_{2}\) emission of the other GHG is the product of the GWP of that gas and the emission amount. Equation (6) shows an empirical relationship for estimating the equivalent carbon emission.  
\[{\rm{CO}}_{\rm{2,ori}}=({\rm{Q}}_{\rm{ci}}+{\rm{Q}}_{\rm{sh}})\;{\rm{F}}_{\rm{RC}} \tag{6}\]  
where EF\({}_{\rm{RC}}\) is termed as the emission factor of the reinforced concrete, which represents the carbon emission contributed by concrete and reinforcing steel. Since higher grade concrete uses a larger amount of cement, and the lower grade concrete requires low cement content; therefore, the emission factor of concrete, EF\({}_{\rm{C}}\) is varied with the concrete grades. The manufacturer usually provides the equivalent carbon emission of reinforcing steel bars. Generally, CO\({}_{\rm{2,Eq}}\) caused by reinforcing steel, is about 2.5 to 3 kg per kilogram of the steel. Therefore, if emission factors, EF\({}_{\rm{C}}\) and EF\({}_{\rm{s}}\), are defined as Kg CO\({}_{\rm{2,Eq}}\)/kg, then the coefficient EF\({}_{\rm{RC}}\) is determined based on the content of steel and concrete in the unit volume (m\({}^{3}\)) of reinforced concrete. The emission caused by reinforced concrete containing cement replacement materials is called CO\({}_{\rm{2,alt}}\), and it is calculated using Equation (7), where Q\({}_{\rm{ca}}\) is the total material quantity of concrete ingredients, including the amount of replacement materials such as fly ash. Q\({}_{\rm{sh}}\) is the total quantity of steel used in the reinforced concrete structure. If EF\({}_{\rm{RC}}\) is defined in kgCO\({}_{\rm{2,Eq}}\)/kg, then all numbers are also described in (kg).  
\[{\rm{CO}}_{\rm{2,alt}}=({\rm{Q}}_{\rm{ca}}+{\rm{Q}}_{\rm{sh}})\;{\rm{EF}}_{\rm{RC}} \tag{7}\]  
\[{\rm{EF}}_{\rm{RC}}={\rm{EF}}{\rm{ac}}+{\rm{EF}}_{\rm{s}} \tag{8}\]The emission factor of alternative reinforced concrete (EF\({}_{\text{RC}}\)) is shown in Equation (8), where it is the total emission factor of alternative concrete (EF\({}_{\text{ac}}\)) and emission factor of steel (EF\({}_{\text{s}}\)) in kilograms of carbon dioxide per kilogram of material (kgCO\({}_{2}\)/kg).
# Automated Car Damage Assessment Using Computer Vision: Insurance Company Use Case
## 4 Materials and Methods
### Cost Analysis
#### 4.3.3 Evaluation of the Public Dataset  
In Table 3, we list several datasets that have been created to carry out vehicle damage assessment, most of which are of a private nature. However, recently public datasets have been published, such as [4], which covers six types of damage (dents, scratches, cracks, glass breakage, punctures, and glass breakage) includes around 4000 images, collected using sources such as flickr5 and Shutterstock6, focusing on obtaining high-quality and diverse images. In [45], a dataset with 14,054 images was created, where 9 views of a vehicle are annotated as back, back left, back right, left, right, front, front left, front right, and zoomed in. Additionally, the authors added 3818 examples of three types of damage: scratches, cracks, and dents. However, as the scratch category damage tends to be more frequent, there is an imbalance in the dataset, which is solved by the authors through the application of data augmentation techniques, applying rotation, saturation, contrast, and brightness transformations.  
In [26], a dataset is created for vehicle damage classification, dividing damage into seven types of damage: bumper dents, door dents, broken glass, broken headlights, broken taillights, broken tail lights, scratches, and breaks. These images were collected from the web and manually annotated.  
In [46], a dataset was created using images from an insurance claims database, where five types of damage are defined: scratches, major dents, minor dents, and cracks. In [47], a public dataset is provided using images extracted from Google Images using Selenium. The images are divided into three sets: vehicle identification, view identification, and damage level identification.  
Although some of the datasets named above are public and have a considerable number of images, we consider that it is necessary to adapt vehicle damage detection to a real appraisal. Therefore, we created a dataset under the supervision of experts in the field and focused on carrying out an image acquisition protocol based on the requirements of the insurance companies, which consists of taking six photos of each car representing all possible views: front view, rear view, front right side view, rear right side view, front left side view, front left side view, and rear left side view.  
In our datasets, damage segmentation labeling was discarded due to a lack of agreement between experts on the boundaries of the damage. Particularly, in the case of scratches and deformations, these boundaries may depend on subjective perceptions, making the labeling inconsistent. Furthermore, from the point of view of insurance companies, a rectangular box is sufficient for damage detection and also speeds up the labeling process by the experts.  
Finally, in order to be able to compare our work with future work, we performed tests on dataset 3 (Section 4.1.3) with all the models used in previous sections.
# Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC
## 2 Dynamic and Kinematic Modeling
### Static Force (Moment) \(\tau_{C}\)  
In the process of air motion, the static force on the vehicle is gravity, and the magnitude is \(G=mg\); in the process of underwater motion, the static forces include gravity \(f_{G}\) and buoyancy \(f_{B}\), where gravity is expressed as \(f_{G}=[0,0,-mg]^{T}\); buoyancy is expressed as \(f_{B}=[0,0,\rho g\Delta v]^{T}\), where \(\rho\) is the fluid density in the basin, and \(\Delta v\) is the amount of water discharged by the drone.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Physical Meaning** & **Name** & **Physical Meaning** \\ \hline \(\tau_{C}\) & Static force (moment) & \(f_{G}\) & Gravity \\ \hline \(\tau_{P}\) & Thrust (moment) & \(f_{B}\) & Buoyancy \\ \hline \(\tau^{\prime}\) & Hydrodynamic force (moment) & \(r_{B}\) & The center of buoyancy in the motion coordinate system \\ \hline \(\tau\) & Combined force (moment) & \(l\) & The distance of the motor to the origin \(o\) of the motion coordinate system \\ \hline \(\tau_{A}\) & Inertial hydrodynamic force (moment) & \(K_{Fat}\) & Thrust coefficient of air propeller \\ \hline \(\tau_{B}\) & Viscous hydrodynamic force (moment) & \(K_{Paw}\) & Thrust coefficient of air propeller under water \\ \hline \(n_{\text{i}}(i=1,\dots,6)\) & Speed of the \(i\)-th propeller & \(K_{Tau}\) & Air propeller torque coefficient \\ \hline \(m_{\text{\bar{u}}}(i=1,\dots,6)\) & Added mass & \(K_{Tau}\) & Torque coefficient of air propeller under water \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of related physical quantities.  
In the coordinate system \(E-\xi\eta\zeta\), the combined force on the drone is \([0,0,B-G]^{T}\), where \(G=mg\), \(B=\rho g\Delta v\), and the center of gravity of the drone coincides with the origin of the object coordinates. When the center of gravity of the vehicle does not coincide with its floating center, the center of gravity and the center of buoyancy in the motion coordinate system are given as \(r_{{}_{G}}=[0,0,0]^{T}\) and \(r_{B}=[0,0,z_{B}]^{T}\), respectively.  
Then the static force matrix (for the airframe coordinate system) on the UAV in a fixed coordinate system can be obtained as  
\[\tau_{\rm C}=\left\{\begin{array}{c}\left[\begin{array}{c}\left(G-B\right) \sin\theta\\ -\left(G-B\right)\cos\theta\sin\varphi\\ -\left(G-B\right)\cos\theta\cos\varphi\\ -z_{B}B\cos\theta\sin\varphi\\ -z_{B}B\sin\theta\\ 0\end{array}\right],\ \ \left(z<0\right)\\ 0\end{array}\right. \tag{6}\]
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 3 Methods  
### Overview  
We propose a semantic-guided adaptive bias calibration approach for the zero-shot point cloud semantic segmentation problem. Our approach specifically addresses the problem that existing generative segmentation approaches [18,19] often misclassify between seen and unseen classes. As illustrated in Figure 2, we design a dual-branch network architecture. One branch is the primary segmentation branch, following existing work [19], which tends to produce segmentation results that can better distinguish different objects but often misclassifies between seen and unseen classes. The other branch is the visual-semantic fusion branch, which fuses the semantic embeddings with point cloud features through contrastive learning to identify potential features, especially for unseen classes. The outputs of the two branches are adaptively combined to calibrate the bias and produce accurate segmentation probabilities.  
To formulate our problem, we establish an object category set \(C\), which includes the seen \(C^{S}\) and unseen classes \(C^{U}\). Following the general setting of inductive generalized ZSL works, our training process takes all the object category embeddings for \(C=\{C^{S},C^{U}\}\) and only the point clouds of seen classes \(C^{S}\) with their ground truth labels to train the network. After training, the network will encounter point clouds from either seen or unseen classes.  
In the remainder of this section, we first introduce the network modules, including the primary segmentation branch (Section 3.2), the visual-semantic fusion branch (Section 3.3), and the adaptive bias calibration module (Section 3.4). Then, we describe the entire training strategy, which is a three-stage training process (Section 3.5).
## 4 Conclusions  
In conclusion, we have presented a fibered OF-CEAS system with an enhanced spectral resolution based on a U-shape cavity. In the experiment, an asymmetric ripple effect is observed, which seriously affects spectral measurements. A theoretical and experimental  
Figure 6: The cavity transmitted light signal of M2 (black). The simulated transmitted light of M2 (red) without etalon effect. The simulated results without etalon effect are based on Equation (3) and HITRAN 2020 (P = 1.0 atm, T = 25 \({}^{\circ}\)C). The bottom shows the residuals of the measured signal (black) and the ripple amplitude of M2.  
Figure 7: The absorption coefficient spectrum (black) and Voigt fitting of the absorptions of molecular H\({}_{2}\)O (red) based on HITRAN 2020 (P= 1.0 atm, T = 25 \({}^{\circ}\)C); the bottom shows the fit residual.  
study is presented for this unique phenomenon. The results show that this phenomenon is caused by the different losses of each cavity mirror (the larger the loss of the reflector, the smaller the ripple amplitude). This spectral fluctuation can be effectively eliminated by optically gluing a prism to the back of the end mirror. After system optimization, the OF-CEAS system with the minimum detectable coefficient \(\alpha_{\text{min}}=8.33\times 10^{-9}\) cm\({}^{-1}\) was obtained. These works have guiding significance for the performance improvement of the CEAS system.  
Considering the characteristics of the asymmetric ripple effect in U-cavity enhanced spectroscopy systems, several potential applications are proposed. Cavity ring-down systems for measuring the spectrum loss of mirrors require high-performance optical switches and photodetectors. After eliminating the ripple effect, the system can measure the spectrum loss of highly reflective mirrors by detecting the intensity of transmitted light from different mirrors. In addition, the laser wavelength can be calibrated by changing the thickness of the end mirror and measuring the ripple period of the enhanced spectral signal of the end mirror cavity. Unlike the traditional cavity-enhanced absorption spectroscopy system, the U-cavity-enhanced spectroscopy system has a more straightforward scheme and a more comprehensive application range.  
Conceptualization, Y.W. and Z.T.; methodology, Z.T.; software, Y.W.; validation, Y.W., S.G. and H.C.; formal analysis, Y.W.; investigation, H.C.; resources, Z.T.; data curation, S.G.; writing--original draft preparation, Y.W.; writing--review and editing, S.G. and Z.T.; visualization, Y.W.; supervision, Z.T.; project administration, Z.T.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 12004435), the Hunan Provincial Innovation Foundation for Postgraduate (No.CX2020001).  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors declare no conflict of interest.