esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Perturbation Consistency Framework in Semi-Supervised Medical Image Segmentation
## 4 Experimental Results and Analysis
### Comparison with Other Models  
Table 1 presents the average performance of the four segmentation results on the ACDC dataset with label proportions of 3%, 7%, 20%, and 40%. To ensure a fair comparison, all experiments were conducted under identical settings. The most successful results are highlighted in bold font, indicating that correct segmentations were achieved for the right ventricle (RV), myocardium (Myo), and left ventricle (LV). To ensure a fair comparison, the same data enhancements were applied (i.e., random rotation, random cropping, and horizontal flipping). It was observed that the proposed method outperformed the current SSL-based method for different labeling ratios. This validates the superior performance of the proposed method in terms of segmentation accuracy and labeling efficiency. For instance, compared to the second-best ARCO, our HCLM on the ACDC dataset demonstrated an improvement of [0.9%], 0.8%], 1.4%], 1.5%] at [3%, 7%, 20%, 40%) label ratios of Dice index. In particular, our method achieved a Dice index of [89.2%, 90.6%, 91.5%, 92.6%) on the ACDC dataset. These results demonstrate that our method can be applied to diverse clinical scenarios and labeling ratios. The segmentation performance on the ACDC dataset under different labeling ratios for each model can be visualized in Figure 4. The segmentation performance of our model for varying labeling ratios can be observed in Figure 5.  
Given the focus of our work on semi-supervised learning, fully supervised methods were not included in the comparison. Fully supervised methods rely on large amounts of labeled data, and their primary goal differs from that of semi-supervised methods. While fully supervised models achieve impressive performance when abundant labeled data are available, they are less applicable in scenarios where labeled data are scarce--a key challenge that semi-supervised approaches aim to address. Thus, comparing these two fundamentally different paradigms may not provide the most relevant insights into the specific advantages of semi-supervised methods. To more effectively illustrate the segmentation efficacy of our model, we selected the method exhibiting the highest Dice index within the labeling scales of 3% and 7%, and calculated the HD95 and ASD metrics. As shown in Table 2, our model demonstrates the most consistent performance. To clearly highlight the advantages of our model, we plotted box plots of DICE, HD95, and ASD metrics for different models under the 3% and 7% labeling rates. These box plots provide a visual comparison of the performance distribution among various models. The box plots for the 3% labeling rate are shown in Figure 6, and those for the 7% labeling rate are shown in Figure 7.
## 4 Analysis
### Efficiency Analysis
#### 4.1.2 Verification Time  
The adversary may launch a memory verification attack against the attestation by generating the checksum accurately [11]. This means that the adversary could copy the original memory contents into the empty memory and carry out a check of whether the memory access is one of them. If the memory access touches an altered location, the adversary will load the correct copy instead.  
Nonetheless, the inserted if statements in the verification procedure will increase the detection probability. The slowdown is related to the number of memory accesses of the verification procedure. Generally speaking, if the verifier expects to detect this slowdown, the computing time for the checksum should be significantly larger than the worst case network delay in order to make it distinguishable from the network delay. Then, we have:  
\[n_{ex}\cdot t_{ex}\gg n\cdot d_{\max} \tag{38}\]  
where \(n_{ex}\) is the number of iterations in the verification procedure, \(t_{ex}\) is the increased time of executing an if statement, \(n\) is the number of relay nodes in the path and \(d_{\max}\) is the maximum delay in a single-hop.  
Then, we have:  
\[n_{ex}\cdot t_{ex}=n\cdot d_{\max}\cdot\alpha \tag{39}\]  
where \(d_{\max}\) is the end-to-end delay in the network and \(\alpha\) is the scale factor.  
According to our proposed attestation scheme, we estimate the one-hop transmission delay \(\bar{d}\) on the path in the network. Then, we only need to ensure that:  
\[n^{\prime}_{ex}\cdot t_{ex}+n\cdot\bar{d}\gg n\cdot d_{\max} \tag{40}\]  
where \(n^{\prime}_{ex}\) is the number of iterations in DRMA. Then, we have:  
\[n^{\prime}_{ex}=n_{ex}-\frac{n\cdot\bar{d}}{t_{ex}} \tag{41}\]  
To summarize, in the case where the checksum computing time is significantly distinguished from the network delay, the cost of our proposed attestation scheme is less than the ones without considering network delay. When the transmission path is long, the average network delay increases, leading to a low overhead of the proposed scheme.
# Spare Parts Made by Additive Manufacturing to Improve Preventive Maintenance
## 3 Results: Case Study
### Selected Lists
#### 3.2.1 Categorisation Criteria  
Based on the parts mentioned above and selected from the stock of the car manufacturing company, a categorisation was made following multiple criteria and priorities, for these parts to be printed and to check their usefulness for preventive maintenance. The methodology followed to categorise the spare parts was based on the following criteria:  
* Structure of the part or assembly: it is important to know if we are dealing with an assembled piece or a unique one, in order to determine if the 3D printed assembled part is operational or not. There are many types of assemblies where the parts cannot be printed quickly due to the variety of materials, the process to be used or the parts that cannot be printed. The distinction was made between the individual part and the assembled part;
* Large: Vsp \(>500\times 500\times 500\) mm\({}^{3}\);
- Medium: \(50\times 50\times 50\) mm\({}^{3}\) \(<\) Vsp \(<500\times 500\times 500\) mm\({}^{3}\);
- Small: Vsp \(<50\times 50\times 50\) mm\({}^{3}\).
* Material: this criterion considers whether it is a single material or a multi-material. It also takes into account whether it is metal, ceramic or polymeric. It is necessary to know whether it is possible to print with the material used in the usual part or, if not, to identify the replacement material, for example, by checking its mechanical properties;
* Rigidity (class): this criterion is used qualitatively and differentiates between rigid and flexible;
* AM suitability (printability): this is a qualitative result criterion that is used to categorise the spare parts. A distinction was made between very low, low, medium, and high suitability;
* Functionality is based on where the piece is used and its role in the equipment; generally, this refers to the surface condition in which the roughness is present in almost all the types of spare parts in the inventory. Precision parts required special attention because they demand high tight tolerances and a tight fit to ensure precision operation whence the importance of roughness \"Ra value\". This roughness value has to be equal to a precise value where the function is performed properly as in the case of mechanical construction parts such as gears and shafts. Additionally, the hardness was taken into consideration in this criterion because it is a critical property that should be present in the printed part. The hardness value of the AM spare parts should be the same as the value of the conventional part or in the same range. Figure 6 shows the flowchart of the different lists and categories of spare parts.
# Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining
## 3 Multi-Objective Optimization of Maximal Frequent Itemset Mining  
Frequent itemset mining aims to find all frequent itemsets that meet the threshold requirements. According to Property 1, if all maximal frequent itemsets can be found, then all their nonempty subsets are frequent itemsets, and all frequent itemsets are found. Therefore, the designed mining algorithm proposes an evolutional computation method of multi-objective optimization (MOO) to search for a maximal frequent itemset from the high-dimensional transaction sets by using the maximal frequent itemset as the search objective and high-dimensional transaction sets as the objects.
## 6 Conclusions  
In conclusion, we present a theoretical and experimental study on the SBS threshold for optical pulse in the presence of MI in long optical fiber. The effect of MI on the SBS gain and threshold is analyzed based on the coupled-wave equation. An analytic expression is obtained to calculate the SBS threshold in presence of MI. The model explains the mechanism of how MI affects the SBS threshold for optical pulse. The effects of repetition rate and ASE noise level on the SBS threshold are analyzed. The theory is confirmed by experimental results. The study can be helpful to the design of interferometric fiber sensing  
Figure 8: The evolution of the experimental average SBS threshold versus ASE noise levels with different repetition rates. The pulse duration of the optical pulses is 200 ns.  
Figure 7: The experimental setup to measure SBS threshold with different repetition rates and ASE noise levels.  
systems and optical power transmission systems. Though the paper focuses on how SBS initiates from noise, the results can also offer guidance to the design of systems that utilize SBS with a seed, such as slow light system and Brillouin optical time domain analyzer.  
Conceptualization, X.H.; methodology, X.H.; software, X.H. and Y.L.; validation, X.H. and Q.H.; format analysis, X. H; investigation, X.H., Q.H.; resource, X.H.; data curation, X.H.; writing--original draft preparation, X.H; writing--review and editing, X.H.; supervision, Z.M.; project administration, X.H. and Y.L.; funding acquisition, X.H and Z.M. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Science Foundation of China (No. 12204542) and the Science project of National University of Defense Technology (No. ZK19-08).  
Not applicable.  
Further data are available from the corresponding author upon reasonable request.  
The authors would like to acknowledge the support of the National Natural Science Foundation of China (No. 12204542) and the Science project of National University of Defense Technology (No. ZK19-08).  
The authors declare no conflict of interest.
## 4 Analysis and Requirements
### Reducing Communication Load and Latency  
The presented interviews reveal a conflict between the demands of the firefighters (less communication) and of the incident commanders (as much information as possible). With the currently employed technologies it seems hard to solve this problem. However, monitoring the team's activities and actions could be an approach to avoid this dilemma. In fact, using an approach that acquires information about the actions of the teams inside a building that is then transferred over a robust communication link would deliver the required information to the commander. Within the research communities, various approaches exist to facilitate the monitoring of activities. However, from the requirements of the work environment of the firefighters, these are reduced to a small number of options. Video based systems are not possible due to the limited vision. Thermal camera based systems would suffice but their current physical dimension, weight and price still restricts the integration into firefighter equipment or communication repeaters. Attaching acceleration sensors to firefighter's bodies or clothing could be an approach but must be considered carefully due to the required certification tests.
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## 3 Analysis of the Coverage Probability and Average Rate
### Coverage Probability  
In the context of this paper, the coverage probability is formally defined as the probability that the typical user can achieve a target SINR threshold. Mathematically, this is \(\mathcal{P}=\mathbb{E}\left[1\left(SINR>\tau\right)\right]=\mathbb{P}\left( SINR>\tau\right)\), where \(\tau\) is the target SINR threshold.  
We denote \(\mathcal{P}_{M,in}\)  
**Theorem 2**.: _The average rate for a randomly chosen CoMP user is:_  
\[\mathcal{R}_{\mathrm{C}}{=}\int_{0}^{\infty}\mathcal{P}_{\mathrm{C}}\left(e^{t }-1\right)dt \tag{21}\]  
Proof.: The average rate for a randomly chosen CoMP user is formally defined as:  
\[\mathcal{R}_{\mathrm{C}}\stackrel{{\Delta}}{{=}}\mathbb{E}_{ \mathrm{X}_{\mathrm{C}}}\left[\mathbb{E}_{SINR}\left[\ln\left(1+SINR\left( \mathcal{B}=\{MBS_{0},FBS_{0}\}\right)\right)\right]\right].\]  
Because \(\mathbb{E}\left[\tau\right]=\int_{0}^{\infty}\mathbb{P}\left(\tau>t\right)dt\) for \(\tau>0\), the \(\mathcal{R}_{\mathrm{C}}\) can be rewritten as:  
\[\mathcal{R}_{\mathrm{C}}{=}\int_{0}^{\infty}\mathbb{P}\left(\ln\left(1+SINR \left(\mathcal{B}\right)\right)>t\right)dt{=}\int_{0}^{\infty}\mathbb{P} \left(SINR\left(\mathcal{B}\right)>e^{t}-1\right)dt.\]  
Using the definition of \(\mathcal{P}_{\mathrm{C}}\), we obtain the result in Equation (21).
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Integrated Germanium Photodiodes  
Integrated bulk germanium photodiodes (PD) are used as the main optical-to-electrical converters in silicon photonic systems [58; 59; 60; 61]. The photodiode relies on a _p-i-n_ junction within the bulk germanium to collect photo-induced free carriers. Shown in Figure 3 is a typical structure of an integrated germanium PD [58; 62]. A reverse-bias voltage is applied to the PD through the tungsten contacts to create a strong electric field in the germanium region. Light incident from the silicon waveguide, normal to the structure in Figure 3, with photon energy \(>\sim\)0.8 eV will get converted to free carriers as photo-current. TID, SEE, and DD radiation effects investigations have been conducted to determine if this standard operating working principle of the PD is, in any way, altered.
# Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp
## 5 Conclusions  
This research proposes an algorithmic architecture for improving the precision of garbage classification and localization by integrating computer vision and machine learning  
Figure 9: Each subplot in (**a–j**) depicts the robot grasp pose calculating from camera coordinate to robot base coordinate (Translation and Rotation).  
techniques. The proposed approach leverages RGB-D data captured by the RealSenseD415 camera and employs the Yolactedge model to predict the garbage class and mask from RGB images. By utilizing the predicted mask, the algorithm effectively separates the garbage instances' point cloud from the overall scene point cloud. The spatial position and orientation of the garbage instances are determined by computing the OBB of the segmented point cloud using the DBSCAN clustering method, which eliminates interfering points and provides valuable information for robotic grasp applications. To facilitate the training process, the TACO dataset is pre-processed, and a custom-collected garbage dataset is generated. While the TACO dataset only consists of garbage image data, the fine-tuned Yolactedge model proves to be useful for garbage mask segmentation in the custom dataset, enabling the segmentation of point clouds and the calculation of garbage instance OBBs. This approach significantly reduces the laborious task of data annotation. The results indicate that the proposed approach can run at a maximum speed of 150 FPS and maximum _mPA_ of 37%. Experimental evaluations on custom-collected datasets demonstrate the effectiveness of the proposed approach in achieving accurate garbage detection and spatial localization. The results highlight the potential of the algorithmic architecture in revolutionizing waste management processes, enabling intelligent garbage sorting, and facilitating robotic grasp applications.  
Future work involves redefining garbage categories based on recycling standards to optimize detection performance and exploring the integration of 3D spatial localization with mobile robot garbage pickup or conveyor belt sorting systems. In summary, this research highlights the application of computer vision, machine learning, and intelligent image processing in garbage detection and localization, with implications for improving waste management and recycling processes.  
Conceptualization, Z.L.; Methodology, Z.L.; Software, T.C.; Formal analysis, T.C.; Writing--original draft, Z.L. and T.C.; Writing--review & editing, Z.C. ([PERSON]) and Z.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The authors declare no conflict of interest.
## 2 The Preliminary Background of the Proposed Control Method
### Fractional-Order PID (FOPID)  
The fractional-order PID (FOPID) controller is an improved version of the PID controller that integrates fractional-order calculus. The FOPID controller employs fractional orders instead of integer values for the derivative and integral terms to improve performance and flexibility in controlling complicated systems. The three major components of the PID controller are the proportional (\(K_{P}\)), integral (\(K_{I}\)), and derivative (\(K_{D}\)) terms. The fractional orders of the integral (\(\lambda\)) and derivative (\(\mu\)), in addition to the gains for the integral and derivative terms, are two additional parameters in the FOPID controller. The FOPID controller's architecture is shown in Figure 4. The fractional order (\(\lambda\) and \(\mu\)), which allows non-integer values between 0 and 1, provides more control flexibility. These fractional orders control the amount that the integral and derivative terms weigh and contribute, allowing the system response to be improved with greater specificity.  
The standard FOPID controller is called \(Pl^{\lambda}D^{\mu}\), where \(\lambda\) and \(\mu\) can be any real number and stand for integrator and differentiator orders, respectively. The FOPID controller's transfer function is defined as  
\[C(s)=K_{P}+K_{I}\frac{1}{S^{\lambda}}+K_{D}S^{\mu} \tag{24}\]  
The goal is to derive the optimal values of \(K_{P}\), \(K_{I}\), and \(K_{D}\).
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 5 Fiber-Optic Hydrogen Sensors
### Wavelength Modulated Fiber-Optic Sensors
Experiments were performed with H\({}_{2}\) concentrations varying from 20 to 70% of the sample gas. The experimental results show that the Bragg wavelength increases with increasing hydrogen concentration, and the wavelength drift is large. [PERSON] et al. [86] used a Pd slot with a semicircular cross-section and placed an optical fiber written with a Bragg grating inside the Pd slot, which was coaxial with the Pd slot, and the fiber was fastened to the Pd slot. When the sensor was placed in a hydrogen environment, the Pd tank expanded. The expansion of the Pd tank coupled to the fiber, causing a change in the grating constant. Since the Pd slot expansion had a cumulative effect along the axial direction, it could play an amplifying role in the strain. To ensure the stability of the sensor, the researchers concluded that the thickness of the fabricated Pd slot should not be less than 300 \(\upmu\)m.
# Role of Piezoelectric Elements in Finding the Mechanical Properties of Solid Industrial Materials
## Using Piezoelectricity to Calculate Solid-Material Properties
### Tensile Strength  
The tensile strength of a material is its ability to bear tensile load before failure. As a mathematical concept, it is the ratio between the greatest tensile forces allowed (before destruction) to the cross section area of a specimen. Most of the users of PE in this type of test have used it as a sensor. Among the first who used PE as a pressure sensor in a tensile test was [113; 114] for a specimen with dimensions 1.5 cm \(\times\) 0.5 cm.  
Authors in [115] employed eight PEs (Physical Acoustics model S9225) with frequencies ranging from 0.1 MHz to 2 MHz to calculate the tensile strength of a rock of quasi-brittle material with size (span \(\times\) height) of approximately 800 mm \(\times\) 300 mm and 400 mm \(\times\) 150 mm for the granite and 900 mm \(\times\) 300 mm for the sandstone and 200 mm \(\times\) 75 mm for the granite and sandstone.  
A 150 kHz acoustic emission was also used by [116] as a detector to determine the strength of glass-fibre bundles or as a sensor for energy produced from a fibre fracture during the tension process, as shown in Figure 14.  
Figure 13: Specim shape and the position of bender element, trigger of (C\({}_{\mathrm{L}}\) and C\({}_{\mathrm{S}}\)) waves, and accelerometer of (C\({}_{L}\) and C\({}_{S}\)) waves.  
PE was used in a tensile setting, and the authors in [117] measured the tensile strength of a piezoelectric specimen made of PZT (150) kHz fibre-epoxy with dimensions of 25 mm \(\times\) 50 mm composites and explained the strong effect of matrix material on the tensile strength of PE.  
The authors in [118] placed two PEs as an emitter and receiver with an operating frequency range of 200 kHz to 750 kHz. The relationship between the tensile stress and acoustic wave velocity (acoustoelectric calibration test) was measured in a case of loading and unloading for seven-wire steel strands (T15.7), which had the following dimensions: core wire diameter of 5.4 mm, helical wire diameter of 5.22 mm, and strand diameter of 5.7 mm. The results of this research revealed an induced positive relationship between an increase in transit wave velocity (through this type of wire) and an increase in the stress percentage in it.  
The authors in [119] used a piezoelectric sensor with a frequency range of 1 kHz to 1.5 MHz to evaluate the tensile strength of epoxy-impregnated Kevlar(r) 49 composites (25 mm \(\times\) 1.4 mm \(\times\) 1.1 mm). PE was used as a load washer (load sensor with 33 kHz) to measure the tensile strength of sisal fibre cement composites (152.4 mm \(\times\) 25.4 \(\times\) 12 mm) [120].  
The progress in the areas of manufacturing enabled researchers to use piezoelectric actuators of high speed and frequency. The authors in [121,122] designed a high strain rate tensile test machine for DP780, DP980, and TRIP780 steels using a PE (Kistler 9071A) as a high speed load cell (20 m/s).
## 5 Results and Discussion
### Performance Evaluation Based on Review Rating Prediction
Additionally, for training data = 100%, the smallest RMSE of 0.114 is measured by the proposed spider Taylor-ChOA-based HAN, while the RMSE values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.335, 0.241, 0.201, and 0.133.
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 5 Effect of Heat Treatments in AM Processed Maraging Steels
### Strengthening Mechanisms' Models  
The strengthening mechanism for maraging steels includes solid solution strengthening, dislocation hardening, and precipitation strengthening. In addition, the effect of these mechanisms may be utilized to evaluate the Vickers hardness and yield strength of maraging steels according to Equation (1) [92; 93; 94].  
\[H_{v}=\frac{1}{3}\sigma_{y}=\frac{1}{3}\big{(}\sigma_{ss}+\sigma_{mart}++ \sigma_{p}\big{)} \tag{1}\]  
where \(\sigma_{mart}\) is the effective martensite strength, \(\sigma_{p}\) is the precipitation strengthening, and \(\sigma_{ss}\) is the contribution of solid solution strengthening.