esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Evaluation Metrics  
The classification performance was evaluated by measuring the sensitivity (\(Se\)), specificity (\(Sp\)), and area under the curve (\(AUC\)) of receiver operating characteristics (\(ROC\)). The sensitivity and specificity are the percentage of positive and negative results that are correctly identified. The metrics are defined as:  
\[Sensitivity(Se)=\frac{TP}{TP+FN} \tag{6}\]  
\[Specificity(Sp)=\frac{TN}{TN+FP} \tag{7}\]  
where \(TP\) and \(TN\) are _true positive_ and _true negative_ and likewise FP and FN are _false negative_ and _false positive_. The \(AUC\) was calculated by using receiver operating characteristic (\(ROC\)) analysis on the test set.
## 6 Discussion and Conclusions  
A 25.78-Gbit/s \(\times\) 4-ch AOC with an ultra-compact form-factor was proposed and fabricated. As a result of using a 0.5-mm pitch electrical connector, the proposed AOC size is 5.2 cm\({}^{3}\). In addition, high heat-dissipation structure and signal transmission lines and ground vias were designed and optimized to guarantee 25.78-Gbit/s error-free optical transmission over 100-m MMF link under all-channels (4-ch) operation and high temperature (\(T_{\rm c}\) = 70 \({}^{\circ}\)C). These results indicate that the proposed AOC is applicable to optical interconnects for short-reach optical communication specified in IEEE802.3 standards.  
Table 1 shows a bench mark of form factors for 4-ch AOCs. Our newly-proposed ultra-compact form-factor has the smallest size among the form-factors listed. And its transmission density is higher than those of the other standard form factors. In addition, total interface capacity when the proposed form factor is applied in a 19-inch/1-U rack (in two rows) is 7.2 Tbit/s. Therefore, it is concluded that the proposed AOC can provide high-speed and large-capacity rack-to-rack data communication systems in DCs and HPCs.  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the proposed AOC. They performed the experiments, and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
By designing the switching frequency to be greater than the resonant frequency of the resonant tank, the resonant tank will present an inductive load, and the active switches can operate at ZVS. However, the resonant converter requires additional rectifier and filter circuits to obtain a smooth DC output voltage, which not only increases the component count but also causes more conduction losses. This paper proposes an ac/dc converter to drive high-power LEDs, which is obtained by modifying two single-ended primary inductance converters (SEPIC) with interleaved operation. The proposed circuit achieves a high power factor and low THDi and the switch meets soft switching. The other content of this paper is organized as follows. Section 2 proposes the circuit topology and analyzes the steady-state operation mode in detail; Section 3 deduces the mathematical equations for designing circuit component parameters; Section 4 designs, manufactures and experimentally measures the 180 W prototype circuit. Finally, some conclusions are made in Section 5.
## 5 Two-Way Measurement Method in CCFD
### Key Errors of the Two-Way Measurement Method in CCFD
#### 5.3.1 Channel Time Delay  
Channel delay error is one of the significant error terms affecting the precision of inter-satellite measurements. Due to the presence of various nonlinear components in the equipment, along with the effects of aging and temperature drift, the channel delay of the transceiver equipment can vary over time. For a single measurement, since the measurement duration is short and the equipment state changes minimally, the channel delay can be assumed to be constant.  
Since the system employs the CCFD framework, allowing simultaneous transmission and reception of signals on the same frequency, an online calibration method can be used to perform real-time calibration of the channel delay. By introducing a self-calibration channel into the ranging equipment, a closed-loop circuit is formed between the transmission and reception channels. Through transmission-reception switching, three different delay combinations are obtained, and the transmission and reception equipment delays can be calculated, achieving a calibration precision better than 0.3 ns [66].
## 3 Results
### Image Reconstruction and VOI Delineation Results  
The comparison between Par-CT and Med-CT images for a representative patient of the validation dataset from the SOPHIA segmentation process, as can be seen in the respective Figure 2a,b, were highly influenced by the type of reconstruction, leading to differences in volumes of the estimated VOIs.
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 5 Experiments  
### Experimental Setup  
To collect a series of candidate images, a video camera was utilized to monitor the burning fire for a period of time. Normal heptane was used as fuel for the fire. A cauldron was used as the burner, which was about 700 mm in diameter and 22 mm deep. The fire-to-camera distance was about 3 m. There were 100 candidate images, with dimensions of 388 \(\times\) 320. Among them, the 1 st-60 th images exhibited the stable burning of fire, as shown in Figure 4a-c; the 61 st-93 rd images showed the initial extinguishing of fire, as shown in Figure 4d-f; and the 94 th-100 th images contained the final extinguishing of fire, as shown in Figure 4g-i. It was observed that some images with initial extinguishing still contained some information about the unstable burning of fire, which were similar to those with stable burning. On the contrary, the images with final extinguishing were quite different from those with stable burning, which should be detected easily. However, in order to better evaluate  
Figure 3: The flow diagram of fire image detection based on the PSO-SVM model.  
the algorithm performance, the difficulty of detection should be increased. Thus, in our experiments, the first 60 candidate images were regarded as fire images and the rest were regarded as non-fire images. Moreover, according to the different experimental requirements, the candidate images could be divided into various training sample sets and testing sample sets correspondingly.  
At first, the candidate images were converted into grayscale images. In the process of feature vector construction, selecting the Tchebichef moment invariants of proper orders is very important, as this has an effect on the accuracy of detection results. Considering the detection performance and running time of the experimental confirmation, the feature vector \(\boldsymbol{V}_{i}\) used in this paper is shown as follows:  
\[\boldsymbol{V}_{i}=\left[\widetilde{T}_{21},\,\widetilde{T}_{22},\, \widetilde{T}_{30},\,\widetilde{T}_{40},\,\widetilde{T}_{41},\,\widetilde{T}_ {50}\right] \tag{37}\]  
During the optimization process, the initialization parameters of PSO, such as the size of population, maximum number of iterations, inertia weight, and acceleration coefficients, were set as follows: \(s=20\), \(t_{\max}=100\), \(\omega=1\), \(c_{1}=c_{2}=1.49618\). For the cross-validation, the K-fold method was performed with a \(K\) value of 3. The related parameters of \(\tau\) and \(C\) for the RBF kernel function were varied in the arbitrarily fixed range of \([0.1,100]\). The above initialization parameters are given in Table 1. In addition, all of the experiments were implemented by Matlab 2013b and performed on a Windows 7 Ultimate operation system under the 2.4 GHz Intel Core i5 processor with 8 G RAM.  
Figure 4: Candidate images: (**a–c**) stable burning of fire; (**d–f**) initial extinguishing of fire; (**g–i**) final extinguishing of fire.
## 3 Solution Fundamentals
### Holonic Control Architecture and Artificial Agents
AMS is responsible for giving a unique agent ID (AID) for every agent under its platform. The AID is mainly used as the communication address for an agent. Meanwhile, the DF is the yellow pages of JADE. This means that DF knows the services which can be provided by all the agent under a JADE runtime. Therefore, it can announce those services to obtain the service oriented communication policies [16]. JADE applies a standard communication protocol which is founded by the Foundation for Intelligent Physical Agent (FIPA) specifications. FIPA is an IEEE Computer Society standard which supports the creation of an agent-based network and guarantees interoperability with the other available standard technologies [17]. JADE agents use FIPA-Agent Communication Language (FIPA-ACL) in order to exchange the ACL-messages either inside or outside its operating platform. Furthermore, JADE provides a Graphical User Interface (GUI) to help the agent programmer to develop, deploy, and debug a MAS [18]. Figure 3: (a) PROSA (Product-Resource-Order-Staff-Architecture) model; (b) JADE (JAVA Agent Development Environment) framework.
## 3 The Proposed Model
### Process Involved in a WCA  
The weight clustering technique defines the \(CH\) and utilized cluster infrastructure utilizing three measures: node degree (\(ND_{i}\)), (\(RES_{i}\)), and distance (\(DIS_{i}\)). For all the nodes, the weight, \(P_{i}\), was computed as:  
\[P_{i}=w_{1}*RES_{i}+w_{2}*DIS_{i}+w_{3}*ND_{i} \tag{4}\]  
However, \(w_{1}\), \(w_{2}\), and \(w_{3}\) signify the coefficient of model state, as follows:  
\[w_{1}+w_{2}+w_{3}=1 \tag{5}\]  
The \(SN(x)\) for transmitting \(k\) bits of data to the receiver at distance, \(d\), is calculated as follows:  
\[RES=E-\left(E_{T}(k,d)+E_{R(k)}\right) \tag{6}\]  
where \(E\) and \(E_{T}\) demonstrate the present energy level of the SN and the energy spent on data distributing, respectively.  
\[E_{T}(k,d)=kE_{e}+KE_{a}d^{2} \tag{7}\]  
where \(E_{e}\) stands for the electron energy, \(E_{a}\) implies the energy has been utilized in implication, and \(E_{R(k)}\) defines the energy transmitted on the received data.  
\[E_{R(k)}=kE_{e} \tag{8}\]  
In addition, the mean value of the distance between neighboring nodes, which exist as single-hop neighbors, can be calculated as follows:  
\[DIS=\frac{\sum_{j=1}^{NB_{i}}dist(i,nb_{j})}{NB_{i}} \tag{9}\]  
Although \(dist(i,nb_{j})\) describes distance of the SN from the neighboring \(j\)th SN, eventually, the NDEG implies the quantity of neighboring nodes, which have a transmitting radius, as follows:  
\[NDEG=|N(x)| \tag{10}\]At this point, \(N(x)=\left\{\frac{n_{y}}{dist(x,y)}<trans_{range}\right\}x\
eq y\), and \(dist(x,y)\) defines the distance between two nodes, \(n_{x}\) and \(n_{y}\), and \(trans_{range}\) stands for the transmission range of the nodes.
## 2 Materials and Methods
### Analysis
The total number of nodes deployed is \(n=50\), uniformly randomly distributed in a quadrangular area between the coordinates (0, 0) and (500, 500), representing an area of 500 \(\times\) 500 m, see Figure 1.
* The coordinator node is located in the lower left corner, denoted by the ID zero (0). Thus, each transmission from a CH to it represents, at least, a transmission of 100 m and therefore consumes a large amount of energy. This situation can be considered as a scenario of high energy consumption compared to the case where the base station is within the monitoring area, for example, in the center. * All nodes have the same amount of energy available at the start of the simulation. This assumption is focused on considering the network's operation from the beginning when all nodes have new batteries with the same energy level. * The length of a traffic packet is 2 kbits, which comprises the node identifier, a type field that specifies whether it is an event or payload packet. Since the nodes only send information regarding the particular sensors for the application, we consider this sufficient for most practical measurements. * The length of a control packet is 1 kbit with the same fields as a data packet but with a shorter payload. * Physical Layer Parameters: Sensitivity threshold receiver, \(-94\) dBm and transmission power, 4.5 dBm. * Network Layer Parameters: the network has static nodes with a maximum data rate of 250 kbps. * The generation of events is carried out considering a Poisson process based on the rate of arrivals and the events' average duration. As an approach to the study of sensor network systems, we consider that the use of exponential distributions offers an initial insight into these systems' performance.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Gaps in Underwater SLAM Research
#### 9.1.5 Limited Communication for Cooperative SLAM  
Despite the promise of multi-robot SLAM in large-scale or complex underwater missions, effective collaboration remains constrained by the low bandwidth and high latency of underwater communication channels, predominantly acoustic. Transmitting raw sensor data or high-frequency map updates across multiple AUVs is often infeasible, leading to incomplete or inconsistent shared maps. Innovative communication strategies--such as exchanging compressed feature representations or selectively transmitting critical map segments--are needed to overcome these limitations. Developing robust protocols for multi-agent coordination, fault-tolerance, and bandwidth adaptation will be essential for enabling efficient, collaborative SLAM in real-world marine applications.
## 3 Results and Discussion  
### Thermal Deformation Characteristics  
The effect of a large ambient temperature gradient on the thermal deformation of the seal was analyzed by considering the thermophysical properties of helium under low-temperature conditions. The cross-sectional temperature distribution of the face seal and the deformation of the gas film are presented in Figures 3 and 4, respectively, and were calculated under the sealing pressure of 5 MPa and the sealing temperature of 100 K and 350 K, respectively. Owing to the fixed outlet-ambient-temperature of 350 K, the seal worked under a large ambient-temperature gradient condition or an isothermal condition.  
Figure 2: The density and viscosity of helium under different operating conditions [44].  
According to Figure 3, it was obvious that the gas film in the groove area had the lowest temperature and the temperature decreased along the gas flow, except for the groove area. A temperature difference existed between the inlet and outlet of the seal, which were 28.6 K and 27.9 K for the temperature gradient condition and the isothermal condition, respectively. Correspondingly, the temperature of the seal rings was distributed similarly to the gas film, but the temperature difference of the stationary seal ring was larger than the rotating seal ring, owing to their different coefficients of heat conduction, which were 4.95 K and 0.27 K for the condition of the large temperature gradient, and 6.05 K and 0.23 K for the isothermal condition. Additionally, a significant temperature rise for the entire gas film occurred, which was 221.6 K and 144.5 K, respectively, for the two temperature-gradient conditions. Therefore, the ambient temperature gradient resulted in a larger temperature difference of the gas film between the inlet and outlet of the seal and also a larger temperature rise of the entire gas film.  
On the basis of the temperature distributions, the gas film appeared a divergent deformation under both conditions with a temperature gradient and without a temperature gradient, which are presented in Figure 4. The film thickness at the outlet of the seal was larger than the inlet, which increased by 0.94 \(\upmu\)m and 1.04 \(\upmu\)m for the temperature gradient  
Figure 4: The distribution of the film thickness. (The yellow part is in microgrooves, the blue part is in the seal dam). (**a**) Temperature gradient condition; (**b**) Isothermal condition. (\(P_{\text{in}}\) = 5.0 MPa, \(P_{\text{out}}\) = 0.1 MPa, \(n\) = 50,000 rpm, \(h_{0}\) = 3 \(\upmu\)m).  
Figure 3: Cross-sectional temperature distributions of the spiral-grooved face seal (The color legend only applies to the gas film). (**a**) Temperature gradient condition; (**b**) Isothermal condition. (\(P_{\text{in}}\) = 5.0 MPa, \(P_{\text{out}}\) = 0.1 MPa, \(n\) = 50,000 rpm, \(h_{0}\) = 3 \(\upmu\)m).  
condition and isothermal condition. Therefore, although the ambient-temperature gradient tended to obtain larger a temperature difference and temperature rise of the gas film, it contributed to decreasing the extent of the thermal deformation of the gas film.
# A Path Recommendation Method Considering Individual Driving Preferences
## 2 Factors Influencing Individual Preferences
### Path Properties
#### 2.1.2 Dynamic Properties  
The traffic state attributes include the dynamic and potentially time-varying characteristics of a path that influence the selection of a path by a driver. These attributes include path travel time, as well as maximum and average path density [21; 22].  
1. Path Travel Time  
Traditionally, path travel time and path length serve as optimization objectives in relevant research. Path travel time is a crucial indicator that reflects the overall travel conditions and results from the interplay of various traffic factors.
2. Maximum and Average Path Density  
In addition to considering path travel time, drivers also take into account the traffic state along a path when making path choices. A favorable traffic state is more likely to be selected by drivers. Moreover, the path density, which characterizes the traffic state, can impact the comfort level of travelers. Drivers prefer paths with smaller maximum and average path densities. The average path density, denoted as \(K_{L}\), can be calculated as follows:  
\[K_{L}=\frac{\sum_{i=1}^{n}\left(lannum_{l_{i}}\cdot len_{l_{i}}\cdot K_{l_{i}} \right)}{\sum_{i=1}^{n}\left(lannum_{l_{i}}\cdot len_{l_{i}}\right)}, \tag{8}\]  
where \(K_{l_{i}}\) is density of \(l_{i}\) at a specific time window, which is calculated as follows:  
\[K_{l_{i}}=\frac{T_{pass}}{lannum_{l_{i}}\cdot len_{l_{i}}\cdot\Delta_{t}}, \tag{9}\]  
where \(T_{pass}\) is total time cost of all cars passed \(l_{i}\) during a time window of \(\Delta_{t}\) (5 min).  
The maximum path density, denoted as \(K_{L}^{max}\), is calculated as follows:  
\[K_{L}^{max}=\underset{i\in[1,n]}{max}K_{l_{i}}. \tag{10}\]
## 3 Grommet Mechanical Properties and Inverse Method
### Inverse Method for Bonding Force of Grommet  
To observe the bonding force of the grommet, the interference fit analysis and bonding force analysis were conducted continuously. The cable model was forcibly moved 100 mm in the axial direction according to the end time of the analysis. The data extracted from the FEA results were set as the amount of cable movement and force generated at the contact node. Figure 12(a) shows the analytical results when interaction is not considered, that is, when the scale factor value is not considered. In this case, a contact force of around 1 N was generated. Figure 12(b) shows the analytical results in which the scale factor of the linear section is set to 0, 0.25, 0.5, 0.75, and 1 in the FEA.  
The inverse method for the bonding force was used in the analysis results by applying the scale factor. For the parameters applied to the inverse method, this study considered both the scale factor of the nonlinear section and the scale factor of the linear section in the interference fit analysis, as well as the analysis in which the cable was removed. In addition, the inverse method and tests were performed for cases in which the inner diameter decreased by 8%, remained unchanged, and increased by 8%, by assuming that the scale factor is a function of the grommet inner diameter (\(d_{1}\)). When the reduced ratio of the grommet inner diameter is \(\eta\), the relationship between \(\eta\) and the inner diameter is defined by Equation (8).  
\[\mathbf{d}_{1}+(\mathbf{d}_{1}\times\eta\%) \tag{8}\]  
Figure 14 shows the inverse method, which was very similar to that in Figure 7. Table 4 shows the inverse method results. For the case of lower, unchanged, and higher diameters, the inverse method was repeated seven, six, and seven times, respectively.  
Table 4 shows the scale factor values derived through the inverse method. The parameter that impacted the bonding force depending on the change in inner diameter can be considered the scale factor (\(FS_{2}\)) of the linear section in the interference fit analysis. Before the analysis, this study predicted that the scale factor would increase as the physical tolerance decreased. However, the analysis results indicated the opposite relationship. It can be inferred that other scale factors do not greatly impact the bonding force. The quadratic Equation (9) shows the relationship with the scale factor.  
\[FS_{2}=0.001753\ \eta^{2}-0.0152\ \eta+0.4957 \tag{9}\]
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 2 Data and Methods
### Electric Field Detector  
Electric Field Detector (EFD) measures the electric potentials of four spherical sensors, located at the end of four 4.5 m long booms, relative to the spacecraft potential [35], and derives the electric field in the frequency bands detailed in Table 2[36]. Consistent with the SCM analysis, we focus on the ELF band. In this band, EFD provides electric field waveform data, sampled at 5 kHz and expressed \(\mathrm{inmV/m}\); PSD data, presented as the square root of the power spectral density (\(\mathrm{mV/m}\) Hz\({}^{-1/2}\)). PSD data are derived via STFT using 409.6 ms length windows. The frequency range extends from DC to \(f_{MAX}\sim 2200\) Hz (since ELF band for EFD also includes the Ultra-Low-Frequency band), with a frequency resolution of \(\sim\)2.44 Hz, resulting in a total of \(\sim\)1024 frequency bins.
## 4 Experiments and Results
### Ablation Studies  
Table 3 outlines ablation studies conducted on the nuScenes validation dataset to evaluate the effectiveness of the proposed multi-modal fusion network in this paper. This table outlines the single-modal object detection performance of two networks: MonoDIS and NeXtDet. It serves as a baseline for understanding how NeXtFusion, which builds upon the efficient NeXtDet architecture, makes use of additional sensor (radar) data to improve object detection performance. While both networks achieve reasonable mean average precision (mAP) scores, indicating their ability to identify objects, NeXtDet demonstrates a 16.61% improvement over MonoDIS. This suggests that NeXtDet's architecture is better suited for extracting relevant features obtained from the sensor data, resulting in an increased accuracy in object detection (mAP). However, the differences in other metrics are very subtle. While MonoDIS exhibits slightly lower errors in terms of mATE, mASE, and mAOE, these differences are negligible. Conversely, NeXtDet shows a slight improvement in mAVE and mAAE.  
The second half of the ablation study focuses on the proposed NeXtFusion network and the impact of two crucial methods responsible for associating radar detections with objects in the image plane, especially for multi-modal networks. The first is the naive method (NM), where each radar detection point is projected directly onto the image plane using the sensor calibration information. Therefore, if the projected radar point falls within the two-dimensional bounding box of the detected object inside an image, then it is associated with that object. NM is compared to the modified frustum association method (FAM) outlined in Section 3.5 of this paper and utilized in the design of the proposed NeXtFusion network. The results of this study demonstrate the potential benefits of sensor fusion. Compared to the camera-only results, NeXtFusion achieves significant improvements in several key metrics when utilizing FAM, demonstrating a substantial increase in the network's ability to correctly identify objects. Additionally, there are considerable reductions in errors related to object location, scale, and orientation, as observed in Table 3.