esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved FFIP Method Based on Mathematical Logic and SysML
## 6 Case Study
### Further Analysis
On the other hand, since A8 and A10 are related to the green hydraulic pump, and A9 and A11 are related to the blue hydraulic pump, the failure of these two hydraulic pumps also plays a key role in the whole system. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Failure 10** & **Failure 11** & **Failure 12** & **Failure 13** & **Failure 14** & **Failure 15** & **Failure 16** & **Failure 17** \\ \hline \multirow{4}{*}{BSCU} & Command & \(\times\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) \\ \cline{2-9}  & Monitor & \(\times\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) \\ \cline{2-9}  & SystemModel\_SelCmd & \(Z_{2}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) \\ \cline{2-9}  & AutoBraking\_PressureOut & \(Z_{2}\) & \(\times\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) \\ \hline \multirow{4}{*}{Green Pump} & Green Pump & \(Z_{0}\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(Z_{0}\) \\ \cline{2-9}  & Green Pump Isolation Valve & \(Z_{2}\) & \(Z_{2}\) & \(\times\) & \(Z_{2}\) & \(Z_{2}\) & \(Z_{2}\) & \(Z_{2}\) & \(Z_{2}\) \\ \hline \multirow{4}{*}{SchCP} & Sector Valve & \(Z_{2}\) & \(Z_{1}\) & \(Z_{0}\) & \(Z_{2}\) & \(Z_{1}\) & \(Z_{2}\) & \(Z_{2}\) & \(Z_{2}\) \\ \cline{2-9}  & CMD/AS Meter Valve & \(Z_{2}\) & \(Z_{1}\) & \(Z_{2}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) \\ \hline \multirow{2}{*}{Blue Pump} & \(Z_{2}\) & \(Z_{2}\) & \(Z_{2}\) & \(Z_{0}\) & \(\times\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) \\ \hline \multirow{2}{*}{Blue Pump} & \(Z_{2}\) & \(Z_{2}\) & \(Z_{0}\) & \(Z_{2}\) & \(Z_{2}\) & \(\times\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) \\ \hline \multirow{2}{*}{Algebraic} & Accumulator Valve & \(Z_{0}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{2}\) & \(Z_{1}\) & \(\times\) & \(Z_{0}\) & \(\times\) \\ \hline \multirow{2}{*}{AS Meter Valve} & \(Z_{2}\) & \(Z_{1}\) & \(Z_{0}\) & \(Z_{2}\) & \(Z_{1}\) & \(Z_{1}\) & \(\times\) & \(Z_{1}\) \\ \hline \multirow{2}{*}{Manual Meter Valve} & \(Z_{1}\) & \(Z_{0}\) & \(Z_{2}\) & \(Z_{1}\) & \(Z_{1}\) & \(Z_{1}\) & \(Z_{1}\) \\ \hline \multirow{2}{*}{Accumulator Pump} & \(Z_{1}\) & \(Z_{0}\) & \(Z_{0}\) & \(Z_{2}\) & \(Z_{1}\) & \(Z_{1}\) & \(Z_{0}\) & \(\times\) \\ \hline \multirow{2}{*}{System} & \(Z_{2}\) & \(Z_{1}\) & \(Z_{0}\) & \(Z_{2}\) & \(Z_{1}\) & \(Z_{1}\) & \(Z_{1}\) & \(Z_{1}\) \\ \hline \end{tabular} Where: \(\times\) indicates the failure location, \(Z_{0}\) indicates normal function, \(Z_{1}\) indicates degradation function, and \(Z_{2}\) indicates failure functional.  
\end{table} 
Table 6: Component States under double fault simulation. Figure 9: Fault tree of brake system. Therefore, the key failure parts are the hydraulic pumps and the selector valve. Thus, in the process of equipment operation, more attention should be paid to the maintenance and repair of hydraulic pumps and the selector valve, to ensure the safety of the system.
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
Based on the analysis of experimental data for different steels of the same structural class, it was shown that this parameter can be interpreted as a random variable with a normal distribution with constant parameters (mean value and variance) for certain steel grades. Refinement of the parameters of the Palmer-Heald model enables predicting the crack type from AE source emissivity with a certain probability. Though such a model does not clarify the nature and hazard class of AE source, its application makes it possible to estimate statistically the number of AE hits emitted by AE source, and together with AE hits amplitude distribution allows predicting detectability and assessing POD. This paper represents an approach to assessing the probability of fatigue crack detection by AE testing. For this aim, we offer an a priori model of the AE testing procedure, which includes a model of the AE source, allowing to predict the emissivity and the amplitudes of AE signals, as well as a model of the waveguide, allowing to predict the signals' waveform. The procedure for AE data processing is also simulated. A kind of concentration criterion is considered as a detection criterion, in which a defect is detected if the location cluster reaches a certain density. The main efforts of the authors are aimed at determining the probability of fulfilling the defect detection criterion for various AE test parameters, such as the distance between the sensors, the maximum load value, the steel grade, etc. The following assumptions are made in the frame of this study:  
* fatigue cracks originate and propagate under cyclic operating load and are detected during AE testing under step loading by tension, as recommended by the ASTM standard [23];
* during the AE data registration, the method of threshold detection is used, while the threshold value is refined based on the calculation of the probability of defect detection;
* as a method of location, linear location is considered, while the density of the cluster is determined as the number of AE events located at an interval of 0.05\(L\), where \(L\) is the distance between the AE sensors.
### Related Work  
Channel coding for 6G networks is one of the most explored research domains. Review articles on advanced channel coding schemes emphasize decoder performance analysis as a critical area for future research [12; 13]. In general, the literature on error-correcting codes for short block lengths focuses on code comparison, but lacks an explicit comparison of decoders. [PERSON] et al., in [14], have carried out a performance analysis of LDPC, Reed Muller, polar, and other short-length codes with ordered statistics decoding (OSD) for 5G NR. They have proposed OSD as a potential candidate universal decoder for 5G use cases. One significant development in code-agnostic decoding is guessing random additive noise decoding (GRAND), which recovers the original message by estimating the noise that distorted the transmitted data [15]. This method can be applied to different linear block codes, making it feasible for 6G applications. We extend our contribution in the direction of obtaining a desirable network performance satisfying the strict requirements of latency and reliability for 6G URLLC use cases by providing a comparative evaluation of existing works. Table 1 lists recent review articles on code-agnostic decoding techniques in the 6G scenario. Taking into account the limitations of these articles, we discuss and present a comparative analysis of recent universal decoding techniques scalable toward the 6G scenario.
## 2 Segmentation Model
### Design of REU-Net Model Structure  
The traditional U-Net is highly praised for its clear structure and excellent performance on small sample datasets. The original U-Net consists of 18 3 \(\times\) 3 convolution layers, 1 1 \(\times\) 1 convolution layer, 4 2 \(\times\) 2 downsampling layers, and 4 2 \(\times\) 2 upsampling layers, with ReLU used as the activation function. However, after several convolution operations on the left side of the model (the initial feature extraction part), there is a significant loss of feature elements, which hinders the complete and effective extraction of primary features. Additionally, noise handling is not optimized. Since this research aims to detect buildings effectively in remote sensing image datasets, we propose an optimized REU-Net model. The specific structure of the model is shown in Figure 2. The specific optimization of the network mainly includes the following two components.  
The ResBlock module [20] is introduced in the left encoding network part, as shown in Figure 1. When the number of channels in the previous and subsequent layers is the same, the feature map x is directly connected. When the channel numbers differ, a 1 \(\times\) 1 convolution with no bias and full padding is used for dimension adjustment. This results in a new W(x), which is then added to the convolutional feature map, effectively preserving the primary features.  
In the direct skip connection part of the network, the traditional direct connection is replaced with an Attention module. This module extracts edge features from the feature maps obtained at each layer and uses weighting methods to guide the feature maps of  
Figure 1: Structure of REU-Net.  
different depths from the encoding part. These guided feature maps are then combined with the upsampled feature maps, which have reduced dimensions, to recover specific pixel-level details, enabling selective and thorough feature extraction.  
Specifically, we replace the original direct skip connections with ESAM at two intermediate layers (e.g., between the second and third downsampling layers and the corresponding upsampling layers). This placement is chosen because of the following: (1) Shallow Layers: The early layers capture fine-grained edge features, where ESAM enhances contour extraction. (2) Intermediate Layers: Deeper layers encode semantic information, and ESAM here suppresses noise while preserving structural details.  
We experimented with inserting ESAM at different depths (1 EEAM, 2 EEAM, etc.), and found that using two modules optimally balances feature enhancement and computational overhead.
# Targeted Therapy in Metastatic Bladder Cancer: Present Status and Future Directions
## 8 Sampling and Trial Setup
Most mUC patients succumb from metastasis, thus biomarker assessment requires access to metastatic lesions. Samples from metastatic lesions can be obtained by taking needle biopsies, which is burdensome and puts patients at risk for complications. Liquid biopsies allow for the evaluation of tumor DNA that has shed into the circulation. Tumor DNA is continuously shed into the circulation and can be accessed by drawing blood. The analysis of circulating tumor DNA (ctDNA) using ultra-deep sequencing or digital droplet PCR allows for the early detection of metastatic relapse and monitoring the treatment response in a minimally invasive manner [75; 76]. Furthermore, actionable molecular alterations, such as FGFR3 hotspot mutations, have been detected in ctDNA [77; 78]. Nonetheless, it remains to be determined whether a patient's most burdensome tumor clones can be identified as such by evaluating the allele frequencies of ctDNA. Ideally, mutations specific to proliferating or metastasizing tumor clones are indeed represented by greater allele frequencies detected in ctDNA. This would potentially enable umbrella trials that loop back to renewed biomarker assessment and treatment allocation every time that a treatment relapse occurs.
## 5 Performance Evaluation and Discussion
### Experiment Results and Discussion  
#### 5.2.1 Convergence Performance of System  
This subsection illustrates the convergence performance for our work, where the appropriate value for each parameter has been selected for use in the remaining simulation based on applying different values for each parameter.  
First, the parameter of learning rate is optimized in Figure 4, in which different values are used with regard to the ratio of reward. It is seen from the plot that the performance convergence is observed to be faster with a value of 0.01 as well as the speed is increasing with the increase of learning rate value. Nevertheless, the convergence of performance speed for the large value (i.e., 0.1) drops, whereas a local optimum solution occurs. Therefore, we have decided that 0.01 would be the most suitable value for learning, since it is a strong indicator of how well the learner will be able to adapt their behavior in a given situation.  
Secondly, the DNNs' parameter is optimized in Figure 5, in which different numbers are used with regard to the ratio of reward. It is clear that our suggested model can reach convergence more quickly as the number of DNNs increases. Moreover, with only three DNNs, the ratio of reward can reach 0.96 after 2000 steps of learning. Nevertheless, we observed that our proposed model could not converge well when only a few DNNs are used (i.e., DNNs = 1).  
Figure 4: Convergence of performance over different learning rate values.  
Lastly, the parameter of batch size is optimized in Figure 6, in which different values are used with regard to the ratio of reward, where this parameter indicates the trained samples at each interval. The figure clearly demonstrates that the convergence with 32 is more rapid than with the other values. This is because the gradient descent direction tends to increase with decreasing mini-batch size values, leading to a more rapid updating of neural network weights. As a consequence, the batch size is determined to be 32, which appears to be optimal.
### Model Compression  
In our work, we introduce a novel compression technique that combines CP [9; 10], Tucker [11], and SVD [12] decomposition methods for compact low-rank representation of weight matrices of convolutional layers of a neural network, enabling a significant reduction in the number of parameters and the computational complexity of the models.  
One of the key strengths of the proposed method is its ability to achieve high compression ratios through the application of tensor decomposition techniques. By using a combination of different decomposition methods, rather than applying a single method uniformly across all layers, we achieve more effective compression with minimal performance degradation. This is because different layers of a neural network exhibit varying levels of compatibility with specific decomposition methods, depending on their structure and the nature of their learned representations. For example, CP decomposition may effectively compress certain layers with lower-rank characteristics, while Tucker or SVD decompositions may be better suited for layers with more complex dependencies.  
In addition to the core compression method, we advance the post-compression fine-tuning pipeline by adopting an iterative approach. Instead of performing fine-tuning a single time after compression, we interleave multiple iterations of compression and fine-tuning. At each step, tensor decompositions with progressively decreasing ranks are applied, followed by fine-tuning to allow the model to adapt incrementally to the compressed representations. This gradual adaptation helps preserve model performance, minimizing accuracy degradation even at high compression levels.  
We apply our method to the ResNet-18 and ResNet-152 [13] models and evaluate their image classification performance on the CIFAR-10 [14] and Tiny ImageNet [15] datasets. Additionally, we assess the computational efficiency of the compressed models by measuring the inference time required to process a single \(224\times 224\) image.  
Using our compression method, we were able to achieve a significant reduction in the number of model parameters without a significant drop in quality: We obtained three compressed versions of ResNet-18 with compression ratios \(\times 12.2\), \(\times 12.5\), and \(\times 14.5\) and one compressed version of ResNet-152 with compression ratio \(\times 2.5\). Evaluated on the CIFAR-10 and Tiny ImageNet datasets, the compressed versions of the model showed comparable test accuracy results next to the original, uncompressed models. Moreover, the increase in inference time caused by the inefficiency of modern GPUs in the multiplication of sequences from low-rank matrices turned out to be quite insignificant. These results show that our proposed method can be used as an efficient and useful method for compressing convolutional neural networks in real-life applications.
## 3 AI/ML in the Standards and Industry
### Data Analytics and AI/ML in 3 GPP
#### 3.1.2 European Telecommunications Standards Institute (ETSI)
The overarching goal of the ZSM committee is to design a reference architecture that supports self-monitoring, self-optimisation, and self-healing of future networks, with higher levels of autonomy attained as more data is collected [85]. The main drivers for this are the high complexity of future networks, the service-based architecture along with the adopted principles of SDN/NFV, the multi-vendor ecosystems, and the unprecedented business opportunities created by network slicing. The ZSM committee recognises that data collection and closed-loop operations throughout the network are the lifeblood of network automation, see Figure 6.  
Network automation and agility would allow MNOs to provide new services and business opportunities, which ultimately is beneficial to their customers, rather than managing the operation of their networks themselves. Figure 5: High-level functional architecture of the ENI system with an API broker and three assisted systems with different levels of AI adoption, ranging from no AI capability to AI functional blocks with closed-loop controls [79]. To accelerate the adoption of E2E automation in multi-vendor environments, a single, harmonised, and interoperable service and network management framework is needed, where different solutions provided by vendors, standardisation bodies, and open-source projects can consistently fit together and inter-operate [86]. To achieve this target, the ZSM committee does not intend to generate a new standard but rather integrate existing standards and solutions to a consistent architectural framework. The system architecture suggested by the ZSM committee is modular, scalable, and service based with open, intent-based interfaces to facilitate the adoption of various existing solutions and standards [87]. There are several other committees under the ETSI auspices with a focus on AI/ML-based networking solutions. For example, the Securing AI (SAI) group, formed in 2019, studies threats arising from the deployment of AI systems and aspires to provide mitigation schemes against cyber security attacks targeting AI systems. Another example is the Core Network and Inter-operability (INT) committee, which considers AI-aided actions to configure network parameters, e.g., routing policies. All these committees are likely to collaborate, develop synergies with standard developing organisations (SDOs) outside ETSI, and liaise with open-source projects, such as the Linux Acumos and the Open Network Automation Platform (ONAP) to avoid duplications and jointly realise the vision of digital industry transformation.
## 2 Materials and Methods
### Theoretical Model  
Experimental study based on the recreated temporary shelter measurement was mirrored by digital modelling. For this purpose a digital model (Figure 5) in transient system simulation (TRNSYS) software was developed.  
The temporary shelter's energy system was simulated based on the following assumptions:  
1. System simulations were made for a one-year period for Riga, Latvia climate conditions with 20 s calculation step;
2. Controllers sent on/off signals to heat pump depending on the air temperature in the tent and control precise temperature (for heating or cooling needs) in the tent according to timetable (see Table 3);
3. A comfortable climate for occupants' wellbeing and for the reduction of CO2 in the tent was provided by an air-to-air heat recovery device according to sensor signals, and ventilation was provided only during heating periods;
4. Parameters of the tent were adopted according to the manufacturer's specification (see Table 3);
5. The diesel generator was switched on in cases when additional electricity was required or if the storage battery could not be charged during the day;
6. The electrical load of the system was \(\sim\)2.3 kW;
7. A digital model observed heat emissions from occupants, lighting and electricity consumption from equipment according to Table 3.  
Figure 5: TRNSYS model for mobile energy unit.  
TRNSYS model includes all parts of mobile energy unit and allows performing dynamic energy simulations in various climates.
## 3 Materials and Methods
### Performance under Dark Conditions  
#### 3.4.1 Filtration Experiments under Dark Conditions  
The studied membranes were characterised based on intrinsic water permeability and salt rejections (MgSO\({}_{4}\)) using a transparent plastic dead-end cell (Figure 1, Amicon, Denvers, MA, USA, model 8200), of which the active area was 28.7 cm\({}^{2}\) (a diameter of 63.5 mm). In order to avoid the influence of light on the intrinsic membrane performance, the dead-end cell was carefully covered by aluminum foil. To compare the filtration performance of the same membrane before and after the modification, the filtration experiments were performed in two steps: first of all, the neat Desal51 HL membranes were evaluated and then, the performance of these TFC membranes after each modification (i.e., polydopamine modification, binding TiO\({}_{2}\) nanoparticles by polydopamine, and binding TiO\({}_{2}\) nanoparticles by self-assembly method), was tested.  
The vessel was filled with 200 mL of solution (pure water, MgSO\({}_{4}\) solution, or NaCl solution) and tested at ambient temperature. Since the maximum pressure limitation of the plastic dead-end cell was 5 bar, the transmembrane pressure was set at 4 bar. The permeate was collected when the membrane system reached a steady state. The pure water flux (\(J_{w}\)) was obtained by dividing the permeate volume by the membrane area and time. The pure water permeability (A) was calculated by dividing the pure water flux (\(J_{w}\)) by the transmembrane pressure (\(\Delta P\)), \(A=J_{w}/\Delta P\). After the pure water flux measurement had been taken, the rejections of MgSO\({}_{4}\) and NaCl were individually tested. The concentration of the feed solution was 2000 ppm in all cases. The pH of the salt solution was close to neutral (6.7-7.0). In order to reduce concentration polarization, the stirring speed was set at 700 rpm, in which condition the conductivity along the entire depth of solution was identical [28]. The salt rejections (\(R\)) were determined by measuring the conductivity of the permeate solution (\(c_{p}\)) and the concentrated solution (\(c_{c}\)), \(R=1-c_{p}/c_{c}\).  
After testing the filtration performance of the neat TFC membranes, the membranes were washed thoroughly with distilled water and dried under compressed air for 5 min. Following this, each modification procedure (i.e., polydopamine coating, binding TiO\({}_{2}\) by polydopamine, and binding  
Figure 1: Transparent plastic dead-end cell (Amicon, Denvers, MA, USA, model 8200).  
TiO\({}_{2}\) by self-assembly) was carried out inside the plastic dead-end modules, following the procedure described in Section 3.2. Since the diameter of the plastic dead-end cell was 63.5 cm, the volume of each solution used in the modification process was 14.35 mL. After further rinsing of the membranes for 30 min, the pure water permeability and the salt rejections of the modified membranes were tested again. Triplicates were performed for each modification procedure. Average values are reported.