esa-sceva/satcom-chunk-collection huggingface dataset:
# Soft Semi-Supervised Deep Learning-Based Clustering
## 3 Proposed Soft Constrained Deep Clustering (SC-DEC)
Unlike the constraints referred to as \"must-link\" and \"cannot-link\", the constraints in our formulation are soft and not obligated. They can be viewed as a reward for correctly clustering a point, which is suitable for the poorly labeled knowledge available in the real world. The soft constraint term of the objective function governs the consistency between the learned representation and clustering with the side information provided. One should note that the Adam optimizer [56] was used to jointly optimize our objective in (2) with respect to its parameters, the cluster centers \(\mu_{j}\), and the DNN parameters. The following algorithm (Algorithm 1) depicts the steps designed and implemented to solve the clustering task of the proposed SC-DEC approach. ```
1:Input: Dataset \(X\); number of clusters \(K\); update interval \(T\); stopping threshold \(tol\%\); maximum number of training iterations maxIter; \(\gamma\): the constraint term weight; fuzzifier \(m\); AE pretrain epochs; \(\beta\): number of pairwise constraints; data batch size. 2:Output: Cluster centers \(\{\mu_{i}\}_{i=1}^{k}\); deep mapping weights \(\theta\); cluster label assignments \(\{y_{i}\}_{i=1}^{n}\). 3:
4:\(\mathbf{STEP1}\): Pretrain the deep network (AE) on input \(X\) and according to the input hyper-parameters to obtain initial deep mapping weights \(\theta\) and the data in latent space \(\mathbf{Z}\). 5:\(\mathbf{STEP2}\): Initialize the values of cluster centers \(\{\mu_{i}\}_{i=1}^{k}\) and cluster assignments \(\{y_{i}\}_{i=1}^{n}\) by running k-means in \(\mathbf{Z}\) from step1. 6:\(\mathbf{STEP3}\): Begin model fitting:
7:\(\rightarrow\) Create pairwise constraints set \(\mathbb{C}\) according to the input parameters \(\mathbf{X}\), \(\beta\). 8:\(\rightarrow\)for\(\text{iter}\in\{0,1,\ldots,maxIter\}\)do
9:\(\rightarrow\)if update_interval \(T\) is reached then
10:\(\rightarrow\) Compute embedded points on all dataset \(X\) using \(f_{\theta}:X\rightarrow\mathbf{Z}\). 11:\(\rightarrow\) Compute soft assignments \(q\) and target distribution \(p\) using \(z_{i}\) and \(q_{\bar{q}\bar{y}}\). \(p_{\bar{q}}\) formulas in Equations (3) and (4). 12:\(\rightarrow\) Update label assignments \(\{y_{i}\}_{i=1}^{n}\). 13:\(\rightarrow\) Compute cluster performance metrics. 14:\(\rightarrow\) Save old label assignments. 15:\(\rightarrow\) Compute change in label assignment and stop training if it is \(<tol\%\). 16:\(\rightarrow\)endif
17:\(\rightarrow\) Begin custom training on sequential data batches \(S\subset X\). 18:\(\rightarrow\) Compute constraint term per batch using Equation (2) and according to \(\mathbb{C}\) and to the input hyper-parameters \(m\), \(\gamma\). 19:\(\rightarrow\) Add the constraint term to the custom loss in Equation (2). 20:\(\rightarrow\) Train the model by minimizing Equation (2) using the selected optimizer. 21:\(\rightarrow\) Update cluster centers \(\left\{\mu_{j}\right\}_{j=1}^{K}\) and deep mapping weights \(\theta\) on S via the respective update equations. 22:endfor
```  
**Algorithm 1** Soft Constrained Deep Clustering
## 3 Results and Discussion
### Na-Dominant Clinoptilolite
#### 3.1.1 XC Functionals
Consequently, they stand out as the only two functionals in the comparison that conform to the prescribed range for the \(b\) lattice parameter, followed by BEEF-vdW. Speaking of which, the latter displayed the strongest deviation, 1.04%, of all the functionals, in addition to more computational time than the B97-D3. BEEF-vdW could be worth exploring in the follow-up work on the surface models of clinoptilolites since, as discussed earlier, this functional is mainly dedicated to surface modeling. The GGAs, B97-D3 and revPBE-D3, showed better convergence behavior than the metaGGA, TPSS-D3. On the one hand, the TPSS-D3 showed a faster convergence behavior than the PBE-D3 but slower convergence than the B97-D3 and the revPBE-D3, with the former being the best of the functionals after the PBE-D3(BJ)). On the other hand, TPSS-D3 had \(a\) and \(b\) lattice parameters beyond the designated range, with \(\beta\) positioned at the lower boundary of the range. Lastly, the optB86b-vdW failed to show any convergence with the system under consideration. Our results indicate that B97-D3, followed by revPBE-D3, showed reasonable deviations in all unit cell parameters, as well as moderate computational time. It is worth noting that starting from atomic positions of dehydrated clinoptilolite is not going to affect the overall conclusion, and we recommend using the B97-D3 and revPBE-D3 for microporous materials of this type due to their notable computational performance. Therefore, it was worth conducting a comparison of the two DFs for all 84 configurations, given their similar behavior. Regarding the revPBE-D3, as displayed in Table 4, the \(a\) lattice vectors aligned well with the previously discussed range. However, the \(b\) lattice parameter for both samples, in most configurations, fell outside the expected range, varying from 17.71 A to 17.76 A, and the same was true for the angle values. On the other hand, with the B97-D3, the values of the most stable configurations fell within the expected range for all lattice vectors, as well as the \(\beta\) angle. This is in addition to the reasonably inexpensive computational time. Calibrating those experiments to 0 K by accounting for thermal expansion corrections or considering the zero-point anharmonic expansion might alter the values of the above-mentioned experiments (by very small percentages). However, it is unlikely to impact the overall conclusion, as mentioned elsewhere [42; 43; 44]. In an extensive benchmarking of a total of 200 DFs [56], notably, it was recommended that B97-D3 be the default choice from the category of local GGAs, being the best overall functional, followed by revPBE-D3, regardless of the application. The authors also recommended the use of dispersion correction with local GGAs, as using the latter alone cannot adequately capture the non-covalent interactions. The robust refitted B97-D is based on [PERSON]'s power series approach from the B97 functional introduced in 1997, which is specifically parameterized to incorporate damped semi-classical dispersion corrections between atom pairs.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## Appendix A The YOLOv3 Architecture  
Unlike earlier works on object detection that used classifiers or locators to detect objects, YOLOv3 [8] uses a window that moves through the image and locates objects in these fragments. In YOLOv3, a single neural network predicts the objects' locations and assesses the classes' probabilities. It is done directly from the picture with only one data pass. The YOLOv3 network divides the photo into regions and predicts the object frames and the category probabilities for each of them.  
The network consists of 53 fully convolutional layers and 53 others, making a total of 106 layers. Each convolutional layer is followed by the batch normalization and ReLU normalization layers. The detection takes place in three layers: 82, 94, and 104. A picture subsampling is performed in convolution layers only. Such an approach prevents the loss of low-level features that are often excluded by pooling layers. This feature improves the detection of small objects and is particularly important in our project.  
The resolution of the images used for network training and prediction is not specified for YOLOv3. They are automatically adjusted to the size of the network. The only requirement is that the image's width and height are divisible by 32.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 6 Discussion and Recommendations
### Predictive Modeling of Long-Term Soil Resistivity Variations  
Long-term variations in soil resistivity significantly affect the performance and reliability of grounding systems. These variations are primarily influenced by seasonal changes in temperature and moisture content, which alter the conductivity of the soil over time. To address this, a simple empirical predictive model is introduced to estimate the temporal evolution of soil resistivity, allowing for better planning and optimization of grounding system maintenance and performance forecasting. The model is expressed as follows:  
\[\rho(t)=\rho_{0}\times(1+\alpha T+\beta M) \tag{22}\]  
where the \(\rho(t)\) is the predicted soil resistivity at time \(t\), \(\rho_{0}\) is the baseline soil resistivity under dry reference conditions, \(T\) is the deviation in soil temperature from the baseline, \(M\) is the deviation in soil moisture content from the baseline, \(\alpha\) and \(\beta\) are soil-type-specific coefficients that quantify the sensitivity of resistivity to temperature and moisture changes, respectively. This model allows for proactive adjustment of grounding system parameters based on environmental forecasts or sensor data. For example, in tropical climates, soil resistivity may increase significantly during dry seasons due to evaporation and reduced conductivity, while during the rainy season, increased moisture levels reduce resistivity and improve grounding effectiveness. By estimating future resistivity values, system designers can determine when to implement supplementary electrodes, chemical treatments, or system recalibrations.  
In future work, this model can be enhanced using real-time monitoring tools (e.g., IoT-based soil sensors) and integrated into smart grounding system architectures for continuous adaptation to environmental conditions.
## 2 Numerical Simulation Methods
### Numerical Validation  
In Figure 2, the schematic diagram is displayed. The impact experiment used a two-stage light gas gun to fire reactive fragments in order to validate the results of the numerical simulation. The reactive fragments used in this experiment were cylindrical reactive fragments with a diameter of 6 mm and a length of 4 mm. The reactive fragment impact velocity was 2.38 km/s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Material Name** & \(\mathbf{D_{1}}\) & \(\mathbf{D_{2}}\) & \(\mathbf{D_{3}}\) & \(\mathbf{D_{4}}\) & \(\mathbf{D_{5}}\) \\ \hline Q345S & 0.123 & 0.236  
It can be observed in Figure 3 that there is a high degree of agreement between the simulation results and the experimental results. It was confirmed that the numerical simulation's smooth particle length choice and model parameters were suitable. This shows that the simulation model parameters can be applied to additional simulation studies and that some reference can be made to the simulation outcomes.  
The experimental results for the spherical and cylindrical reactive fragments impacting three layers of Q345S or 304 SS plates are presented in Figures 4-6. The results showed that at a velocity of 2.4 km/s, the spherical reactive fragments had the ability to penetrate two layers of the Q345S plate and leave impact marks on the third layer of the steel plate (Figure 4). The cylindrical reactive fragments had the ability to penetrate two layers of the Q345S plate to the limit at an impact velocity of 2.38 km/s (Figure 5). The cylindrical reactive fragments had the ability to penetrate a single layer of the 304 SS plate at an impact velocity of 2.31 km/s and leave pits or small holes on the second layer of the 304 SS plate (Figure 6).  
Figure 3: Simulation and experimental results: (**a–d**) the simulated perforation result for the steel plate; (**e–h**) the experimental perforation result for the steel plate.  
Figure 2: Schematic diagram of the impact experiment.
# Experimental Study of 3D Concrete Printing Configurations Based on the Buildability Evaluation
## 4 Results and Discussion
### Leaning Angle  
The cone buckets with LA \(\alpha\) ranging from \(10^{\circ}\) to \(50^{\circ}\) were printed as shown in Figure 17a. It can be observed that the print failed and collapsed when LA was higher than \(40^{\circ}\). Thus, the max LA of this material is \(40^{\circ}\), and the leaning angle in the sketch design can not be higher than \(40^{\circ}\) for the material and the printer in this study. A blue vase, shown in Figure 17b, was then designed and printed successfully with a maximum LA of \(36^{\circ}\). Increasing the LA by optimizing the material formulation can aid the design of more complex geometry, by, for example, decreasing the setting time or viscosity, which would make extruding difficult, leading to low printability. An acceleration technique could be invented to speed up mortar setting, such as by dosing with accelerating admixtures [40] at  
Figure 16: A 2-step slump requirement calculation algorithm of a printing simulation in Excel.  
the nozzle tip. The layer thickness was fixed at 5 mm for all the printings. The result data would vary when using other layer thicknesses.  
Horizontal bridge-like geometry could be printed if the LA reached 90\({}^{\circ}\), which is common and achievable in plastic extrusion printing. However, it is difficult to reach 90\({}^{\circ}\) LA for cementitious material. In plastic printing, the process involves a rapid (nozzle moving speed at 100 mm/s) physical change from high-temperature liquid (-200 \({}^{\circ}\)C for ABS material) to low-temperature solid (room temperature) using a small amount of material (nozzle diameter 0.4 mm) and, therefore, easy to dissipate heat. Contrarily, the printing of cementitious materials involves a much slower chemical change and requires a larger nozzle size.  
Thus, the 3 DCP technique can not directly print horizontal overhang, such as a roof, but it could be possible with some specific printing strategies proposed by [PERSON] [4] and [PERSON] [5]. Supporting material, such as wax, can be introduced with another extruder to help achieve better buildability. Roofs were printed with manual support of sand and waxes by [PERSON] et al. [41]. The second nozzle can print waxes to support the printed concrete, and the waxes can be removed by heating, while the multi-nozzle techniques are common in plastic printing.
## 1 Introduction
The synthesis and detailed analysis performed in this paper through the application of a systematic literature review (SLR) enabled us to identify a varying set of GRM-P&A that enable supporting companies to properly address their daily management decision-making processes. Moreover, it was possible to identify a set of main GRM paradigms that were encompassed in a proposed collaborative management (CM) framework, in the I4.0 context. The proposed framework is, thus, intended to enable solving more or less complex GRM problems, namely in EME or A/VE, or in the context of cyber-physical production systems (CPPS) [10; 17], which play a very important role nowadays in the I4.0 era. This proposed framework integrates dynamic, distributed, integrated, intelligent, predictive, parallel, and real-time-based approaches, for fulfilling the requirements underlying the resolution of the GRM problems that may occur nowadays in different kind of manufacturing environments. These manufacturing environments may vary from more classical or centralized manufacturing environments up to fully distributed and decentralized ones. Moreover, the proposed CM framework is a novel contribution, as, as to our knowledge, there is not yet any such kind of contribution available in the literature. Thus, some more specific ones are made, regarding the resolution of some kind of problem occurring in a more or less concrete manufacturing environment or application scenario are usually being explored, and/or based on a reduced combination of management paradigms and underlying approaches. Instead, by considering our proposed CM framework, different kind of management P&A can be combined, along with varying types of underlying methods/algorithms, and corresponding problem-solving tools or platforms, for solving a GRM problem. Therefore, different combinations of appropriate methods and techniques, varying from applying pure mathematical optimization methods to the use of diverse types of metaheuristics, among other AI approaches, e.g., machine learning or multi-agent systems (MAS), just to mention a few, may be applied for solving the GRM problems, among others [5; 7; 18; 19; 21]. This paper is organized as follows. In Section 2, GRM-P&A, along with some applications from the literature, is summarized. The SLR methodology used is summarily described in Section 3. In Section 4, the main literature review results reached are presented and analyzed, based on the main decision-making paradigms identified. These are further correlated, based on the information made available through the main set of papers that were deeply analyzed, and that make use of two or more GRM paradigms identified through the SLR carried out. In Section 5, the proposed CM framework is presented and summarily discussed in order to highlight its importance in the I4.0 context. Section 6 briefly presents the conclusions and some insights about future work.
## 3 Causality Inference Using Imess
### Transfer Entropy  
Transfer Entropy (TE) is a measure of causality for time series [19]. It can be intuitively interpreted as the current degree of uncertainty of \(X\) that is solved jointly by the variables \(X\) and \(Y\) and exceeds the current degree of uncertainty, which can be solved by \(X\)'s own past. Equation (3) gives the relationship between TE and Shannon entropy \(H\), here \(H=-\sum\limits_{x}p(x)\log p(x)\).  
Figure 4: Examples of anomaly detection using SPOT, biSPOT, DSPOT, and biDSPOT.  
\[T_{Y\to X}(t)=H(X_{t}|\mathbf{X}_{t-1}^{k})-H(X_{t}|\mathbf{X}_{t-1}^{k},\mathbf{ Y}_{t-1}^{l}) \tag{3}\]  
\(T_{Y\to X}\) denotes transfer entropy from \(Y\) to \(X\), \(H(\cdot)\) denotes Shannon information entropy, the subscript \(t\) denotes time, \(k\) and \(l\) denotes the embedding history length of \(X\) and \(Y\), that is \(\mathbf{X}_{t-1}^{k}=[X_{t-1},X_{t-2}...,X_{t-k}]\), \(\mathbf{Y}_{t-1}^{l}\) has the same meaning. According to the calculation formula of Shannon entropy, Equation (4) gives the calculation of TE:  
\[\begin{split}& T_{Y\to X}(t)=\sum\limits_{x_{t},x_{t-1}^{k},y_{t-1} ^{l}}p(x_{t},\mathbf{x}_{t-1}^{k},\mathbf{y}_{t-1}^{l})\log\frac{p(x_{t}| \mathbf{x}_{t-1}^{k},\mathbf{y}_{t-1}^{l})}{p(x_{t}|\mathbf{x}_{t-1}^{l})}\\ &=\sum\limits_{\mathbf{x}_{t-1}^{k},\mathbf{y}_{t-1}^{l}}p( \mathbf{x}_{t-1}^{k},\mathbf{y}_{t-1}^{l})\sum\limits_{x_{t}}p(x_{t}|\mathbf{ x}_{t-1}^{k},\mathbf{y}_{t-1}^{l})\log\frac{p(x_{t}|\mathbf{x}_{t-1}^{k}, \mathbf{y}_{t-1}^{l})}{p(x_{t}|\mathbf{x}_{t-1}^{l})}\end{split} \tag{4}\]  
Generally, \(k\) is set as 1, \(l\) can be freely selected in practical applications, different \(l\) denotes different time lags of causality.
## 4 Evaluation Results and Discussion
### Performance across Session Durations
#### 4.4.4 Fourth Model Group  
Figure 9 compares the performance of the QoE models in the fourth group. We can see that these models perform generally well across the databases. In particular, the average PCC, SROCC, and RMSE values are respectively 0.88, 0.88, and 0.37 for the _LSTM_ model, 0.83, 0.84, and 0.43 for the _SQI_ model, and 0.81, 0.84, and 0.46 for the _KSQI_ model. In particular, the performance of the _LSTM_ model is generally the highest and most stable. In particular, the PCC, SROCC, and RMSE range respectively in [0.76, 0.96], [0.77, 0.96], and [0.27, 0.48] for the _LSTM_ model, in [0.66, 0.88], [0.69, 0.93], and [0.31, 0.59] for the _SQI_ model, in [0.70, 0.90], [0.78, 0.89], and [0.31, 0.69] for the _KSQI_ model.  
Except for the _W-SQoE-IV_uH264_ database, the lowest PCC and SROCC values are 0.82 and 0.77 while the highest RMSE value is 0.46. For the _W-SQoE-IV_uH264_ database using UHD TV, the obtained results are not very high but still acceptable (i.e., PCC = 0.76, SROCC = 0.81, and RMSE = 0.48). This result implies that, among the considered models, the _LSTM_ model is the best one to predict the QoE of streaming sessions with various viewing devices and different session durations. In addition, it is suggested that the use of segment-basis parameters and an _LSTM_ network is quite effective for reflecting the impacts of quality variations, stalling events, and memory-related effects. In addition, it is essential to consider temporal relations between impairment events in QoE models. However, there is still room for improvements of the _LSTM_ model and the others as well, especially in the case of UHD TV.  
For the _SQI_ and _KSQI_ models, their performances are acceptable, but not very high for most of the databases. In particular, the lowest performance in terms of PCC, SROCC, and RMSE is 0.66, 0.69, and 0.59 for the _SQI_ model, and 0.70, 0.78, and 0.69 for the _KSQI_  
Figure 9: Performance of the _LSTM_, _SQI_, and _KSQI_ models in the fourth group over the test databases. (a) PCC; (b) SROCC; (c) RMSE.  
model. This can be because these models are built based on some assumptions that are not completely valid for diverse session durations and for various patterns of quality variations and stalling events in the databases. For example, it is assumed that the influence of each impairment event is independent and additive. This means that temporal relations between events are not taken into account. In both the models, the impact of each stalling event only depends on its duration and the previous segment quality. While the _SQI_ model does not consider the impact of quality switches, the _KSQI_ model assumes that the impact of each quality switch is dependent on its switching amplitude and the current segment quality.
## 5 Results and Discussion
### Comparison of Visual Observation  
In addition to the mechanical tests, visual observation of the specimens was also carried out to note any visible changes in the two types of test specimens. It is understood that a three-month exposure is a very short period for the assessment of the specimens' deterioration. There was no efflorescence on the samples after three months of exposure in marine or ambient conditions. There was no crack development on the surface of the cylinder specimens. However, the core of the ambient exposed samples looked much lighter in colour compared to the marine exposed ones, as shown in Figure 10 below.  
This can indicate much denser packing of the hydration of binder materials, especially slag species, due to the heated environments under marine exposure. In addition, the fairness of ambient exposed specimens may be due to lower moisture content and evaporation. However, the sample under marine environments seems denser and less porous than the ambient exposed models.  
These short-term observations lead to the understanding that this novel SCGC, originally developed by the authors as a 40 MPa geopolymer concrete, offers substantial potential to be used under marine weather conditions without any strength degradation. Again, SCGC is found to provide a strength gain under marine environments with no potential salinity impacts on the structure under severe exposure conditions. The study confirms the sodium ions in the saline water and the activator favour the solidification of any unreacted species to form a denser geopolymer matrix. However, more work on long-term durability is warranted to confirm this behaviour of the SCGC mixes.  
The test results suggest the hybrid curing potential of the newly developed SCGC mix. In addition to the inherent benefits of self-compacting, flowable concrete, this mix can be cured under ambient conditions and saline conditions without loss of any design strength. This ensures that the mixture can be used for in situ marine structures or precast applications. Further exposure to marine weather will not initiate any degradation in the early days of curing or exposure.  
Figure 10: Ambient exposure vs. marine exposure after three months.