esa-sceva/satcom-chunk-collection huggingface dataset:
# Brain Tumor Detection with Deep Learning Methods' Classifier Optimization Using Medical Images
## 4 Experimental Results
### Results of CNN Models before-after Parameter Optimization
Grid search creates a grid by specifying a range of parameters and their possible values. It then trains the model with each combination on this grid and evaluates the performance of each combination using a specific performance metric. It repeats this process until it finds the parameter combination with the best performance. The parameters to be used for each CNN architecture used during the experiment were searched by grid search. As a result of the experiments carried out here, the learning rate values of 0.0001, 0.001, 0.01 and 0.1 for the Adam and SGD functions seen in Table 5 were evaluated. In addition, five was used as a tolerance value for the evaluation loss values obtained in the training rounds. In this case, if the evaluation loss did not decrease in five consecutive rounds, the model was considered to have memorized the training data and training was stopped. As a result of optimization, it was observed that the ResNet architecture reached an accuracy rate of 100%. While reaching this accuracy rate, many factors affecting the model, such as the number of learnings, number of rounds and parameter function, were found after many trials. Figure 19 shows the accuracy rates after parameter optimization. Figure 20 shows the learning graph of the ResNet architecture in each round after optimization. At the end of the 30 th round, the model shows 100% learning success. More trials here will not change the model accuracy. Figure 21 also shows the confusion matrix of the ResNet architecture since it gives the best results. Here, since the correct activation function and number of layers are chosen, the learning ability of the model is maximized. In addition, adjusting the learning rate and setting the hyperparameters (number of epochs, number of Mini-Natches) affect the model to learn faster and more effectively. So, when the performance metrics in Table 6 are analyzed, it is seen that all the results are accurate. Figure 19: Accuracy rates of CNN algorithms after parameter optimization.
## 2 Literature Review
### Systems Engineering
#### 2.2.2 Parametric Method
As part of the FCC application process, applicants must provide orbital data for their planned missions. However, in the context of the ITU, any files related to the orbital debris mitigation (ODM) requirements are not included in the ITU's regulatory procedures for non-geostationary satellites, except for those containing orbital data [34]. At the international level, users can access orbital data through ITU filings available on its website, but they cannot access the mass data of satellites, which is typically obtained from NASA's Debris Assessment Software (DAS). In this paper, the mass value for the Starlink version 1 satellite is derived from [35], but this cannot be regarded as primary data from the manufacturer. The FCC-mandated orbital debris mitigation (ODM) filings starting in 2024 [31]. However, SpaceX filed for approval for its modification of 1665 V1 satellites in 2018 without requiring an ODM submission for version 1 at that time [36]. This explains why the primary data are available in terms of orbital information but not mass. Now that the FCC requires all satellite operators to submit an ODM plan, users will be able to refer to the FCC for both mass and orbital primary data. However, this approach has a limitation, as FCC filings only cover U.S.-manufactured or U.S.-launched satellites within their jurisdiction, excluding non-U.S. satellites. On a global scale, ITU handles the filings, but only orbital data can be considered primary, while mass data must be sourced from other references, as we have done in this paper. ITU does not currently have a policy requiring the submission of ODM plans, which would provide mass data as primary information. Therefore, in this paper, we rely on the FCC data, and the discussed method can act as a foundational research with use of primary data after being mandated. If ITU were to adopt policies promoting sustainable practices by mandating the submission of an orbital debris mitigation (ODM) plan in the form of NASA Debris Assessment Software (DAS) reports, this approach could be extended to satellites launched globally.
## 3 Experiments and Analysis
### Comparison of Fusion Criteria
#### 3.2.3 Combination of Low and High Frequency Fusion Criteria
Table 6 shows that many objective indexes of the proposed technique 5 are close to or better than other methods, for example, the indexes of AG, FD, RCE, and IFQI are 1.3042, 1.4708, 0.1290, and 0.2027, respectively, which demonstrate that the proposed method can better integrate the overlapping area information of adjacent strips. The larger value of AG and FD reflects the contour of reef, subtle features of geographic texture, and clarity of the fused image. Moreover, the image edge index \(Q^{AB/F}\) and EFQI reflect that the edge details of the fused image generated by the proposed method carry more information. Furthermore, the greater similarity indexes (MI, SSIM) demonstrate that the fused image processed by the proposed method fully combines the respective feature information of the multi-source image. Therefore, the evaluation results of multiple indexes objectively demonstrate that the proposed fusion criteria of SMLF energy and IDPCNN (NSML) is more suitable for the sonar image fusion processing of adjacent strips.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 3 Results and Discussion
### Effects of MGWP on Physical and Mechanical Properties of Mortar
#### 3.2.2 Compressive Strength  
The compressive strength of the mortar samples was assessed at various curing ages of 3, 7, 28, 56, and 90 days. The obtained results are displayed in Figure 8. It can be observed that the compressive strength of all the mixes increases with curing age. Notably, the mortar containing 10% MGWP replacement demonstrates the highest compressive strength among all the mixes, while the mortar with 30% replacement exhibits the lowest compressive strength. The enhancement in compressive strength can be ascribed to the elevated CaO content in MWP and SiO\({}_{2}\) content in GWP, resulting in the formation of a more substantial amount of CSH gel. Moreover, the significantly higher specific surface area of MGWP, nearly five times that of cement, promotes increased fineness, fostering a stronger bond between reactants and leading to higher compressive strength. However, further addition of MGWP may lead to a dilution effect in the mix, potentially compromising the overall strength performance.
## 4 Experiments
### Semantic Segmentation
#### 4.1.2 Evaluation of Different Skip Connection Configurations  
Table 2 evaluates the two models trained with the MixSkip procedure with each possible skip-connection configuration. We denote each skip connection according to the corresponding output stride. For example, \"/16\" denotes the coarsest skip connection, with feature resolution \(16\times\) smaller compared to that of the input image. Each row of the table evaluates the same model but with different skip-connection configurations: \(\checkmark\) denotes that the corresponding skip connection is used by the model, while \(\checkmark\) denotes that it is set to zero. The results show that the coarsest skip connection brings small improvements or even reduces the accuracy compared to the model without skips. The biggest contribution comes from the other two feature resolutions. Nevertheless, both models work best when  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **w/o Skips** & **w/ Skips** \\ \hline RN-18 & 44.1 & 75.0 \\ RN-18-MixSkip & 72.6 & 74.9 \\ \hline DN-121 & 18.0 & 77.4 \\ DN-121-MixSkip & 75.5 & 78.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation (mIoU) of semantic segmentation models with and without skip connections on Cityscapes val. Models are trained with regular or mix-skip procedure.  
all skip connections are turned on. This suggests that the synergy of skips is better than selecting only those that contribute the most.
## 3 Results and Discussion
### The Effect of Different Treatments on Soil Property and Soil Crystal Structure
#### 3.2.3 The Effect of Different Treatments on the Soil Crystal Structure  
The effect of different treatments on soil crystal structure is shown in Figure 5. It can be seen from the figure that Cd in soil crystal mainly exists in the forms of Cd(OH)NO\({}_{3}\) and Cd(OH)\({}_{2}\). Pb mainly exists in the forms of Pb\({}_{5}\)O\({}_{4}\) and Pb\({}_{3}\)(CO\({}_{3}\))\({}_{2}\)(OH)\({}_{2}\). This indicates that Cd and Pb are fixed in the soil in different mineral phases. However, the mineral phase is relatively less. It is speculated that, this phenomenon may be because only a handful of PTM is stabilized in the form of crystals in soil. The content of Pb\({}_{5}\)O\({}_{4}\) mineral phase treated by (BC + Fe) and (BC + FeSO\({}_{4}\)) at the number 1 is higher than others. This indicates that the majority of Pb\({}_{5}\)O\({}_{4}\) is fixed in the form of soil crystals, and the soil treated by (BC + Fe) and (BC + FeSO\({}_{4}\)) has more crystal of Pb\({}_{5}\)O\({}_{4}\). The differences of crystal structures after different treatments are not distinct when compared with the crystal structure of the control sample. This shows that there is no significant change in soil crystal structure by adding different stabilizers. The content of Cd(OH)NO\({}_{3}\), Pb\({}_{3}\)(CO\({}_{3}\))\({}_{2}\)(OH)\({}_{2}\) and Cd(OH)\({}_{2}\) mineral phase treated by (BC + FeCl\({}_{3}\)), (BC + FeSO\({}_{4}\)) and (BC + Fe) are higher than that treated by BC at the number 2, 3, and 4, which indicates that it is feasible to apply iron-based materials to the stabilization of Cd and Pb. In addition, mineral phase of Cd(OH)NO\({}_{3}\) treated by (BC + FeSO\({}_{4}\)) at the number 2 is higher than (BC + Fe), which indicates that the treatment of (BC + FeSO\({}_{4}\)) has a better stabilizing effect on Cd in soil than (BC + Fe). In summary, the treatment of (BC + FeSO\({}_{4}\)) has a better promotion of conversion of Cd and Pb into mineral phases, which means that Cd and Pb eventually exist in the soil in a form of low bioavailability.  
Figure 4: The effect of different treatments on soil cation exchange capacity (CEC). Note: different letters for the Figure 4 indicates that the differences are statistically significant (\(P<0.05\)).
# An Adaptive Contextual Relation Model for Improving Response Generation
## 4 Dialogue Generation Model
### Overview  
Notations. As in Section 3.1, the dialogue history, response, and future are still represented as \(H\), \(R\), and \(F\), while the corresponding hidden vectors obtained in the model are \(h\), \(r\), and \(f\). We use \(Rel=(w_{hr},w_{rf})\) to represent the annotated relevance labels, where \(w_{hr}\) is the relevance label of \(H\)-\(R\), \(w_{rf}\) is the relevance label of \(R\)-\(F\). In addition, the simulated response and future by the model are labeled by \(*\), and the variables without \(*\) are ground truth sentences. And we note the forward generator which predicts the response by history as \(G_{F}\), and the generator which predicts the future by history as \(G_{F2}\). The \(G_{B}\) is the generator predicting the response by using future information.  
Model overview. This paper proposes a response generation model with adaptive context relations, which is composed of three components. The first component, Bidirectional Response Generation, predicts two hidden representations of the response with history and simulated future respectively, which represent the contribution of two sentences to the response. The second component, Bidirectional Relevance Learning, obtains the relevance strength of \([H,R]\) and \([R,F]\) with the annotated relevance labels as constraints, which represent the contextual relation of different dialogue relations. The third component Adaptive Response Generation adaptively fuses two representations of response by introducing the response-history and response-future relevance strength to obtain the final response. The goal of the model is to predict an appropriate response \(R^{*}\) under the conditions of given dialogue history \(H\), simulated future \(F^{*}\), and context relevance labels (\(Rel\)). Thus, the objective is:  
\[\arg\max_{\theta}p(R^{*}|H,F^{*},Rel,\theta) \tag{1}\]  
where \(\theta\) is the parameters of our model. The model framework is shown in Figure 1. BART-base [30] is used as the backbone of the encoder-decoder generators \(G_{F}\), \(G_{F2}\) and \(G_{B}\).
# Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video
###### Abstract  
Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means that the quality of the video depends on the player's ability to predict network throughput accurately, which can be challenging in the unpredictable environment of mobile networks. To improve the prediction accuracy, we grouped the throughput trace into clusters taking into account the similarity of their mean and variance. Once we distinguished the similar trace fragments, we built a separate LSTM predictive model for each cluster. For the experiment, we used traffic captured from 5G networks generated by individual user equipment (UE) in fixed and mobile scenarios. Our results show that the prior grouping of the network traces improved the prediction compared to the global model operating on the whole trace.  
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Footnote †: journal: Applied  
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Footnote †: journal: Applied
# Ultrasound for Meat Processing: Effects of Salt Reduction and Storage on Meat Quality Parameters
## 3 Results and Discussion
### Shear Force Measurements  
Consumers' responses have shown that texture is the most important palatability factor when determining meat quality, and a variety of results have been reported on the potential effects of ultrasound for meat tenderization [1; 13; 38]. The general understanding is that US treatment can effectively improve meat tenderness by the mechanical rupture of the myofibrillar protein structure, leading to faster proteolysis, as well as by acting on the thermal properties of collagen [10]. In the results presented in Figure 4, neither the storage temperature (\(p\) = 0.855), the US treatment (\(p\) = 0.638) nor the salt (\(p\) = 0.125) alone or in combination, significantly influenced the average hardness values. With the conditions used in this study, we observed a greater effect caused by the storage time (\(p\) < 0.001), with an increase in the hardness of the meat between day 0 and day 60, more pronounced for the NS samples. A non-significant tendency towards a decrease in the toughness of the muscle due to US was similarly observed in beef [36]. In general, effects of US on meat tenderness vary greatly depending on the US system used, power, temperature and meat cut; some studies showed significant effect of ultrasound to improve tenderness [25; 39; 40], while others seem to indicate that ultrasound has no effect on shear force of meat [32; 41]. Studies on beef cured with NaCl and exposure times of 30 and 120 min with ultrasound (150 and 300 W, 20 kHz) reported changes in the water retention capacity and the tenderness of the meat compared to traditional curing (\(p\) < 0.05) [42]. Despite the same US frequency used, differences in the chosen processing time as well as a lower ultrasonic power, could explain why no differences were observed in our study. It has to be addressed that in this experiment, good care was taken to ensure that the samples were obtained from the same part of the _Longissimus dorsi_ muscle; as a very long muscle, thus with a lot of variability, different outcomes in the effect of the treatment could be observed based on the section of the muscle used.
## 3 Results
### Regional Analysis of Coordinate Differences
Directional clustering is particularly evident in eastern coastal regions such as Gyeongbuk and Gangwon, which may suggest systematic differences in model implementation or reference frame realization between the two GNSS processing strategies. Each subplot corresponds to a regional group and represents the directional spread of \(\Delta\)N, \(\Delta\)E residuals.
## 4 Results and Analysis  
### An Analysis of the Spatial Pattern of PLES  
Based on the spatial categorization system of the PLES land types, the spatial distribution maps of Zengcheng District in 2010, 2015, and 2020 were produced (Figures 3 and 4). In 2010, urban living land was primarily concentrated in the central urban area. Over time, it expanded eastward, southeast, and southwestward, with the main development areas focusing on Licheng Street, Xintang Town, Shitan Town, and Zhucun Street.  
This pattern of development can be attributed to the Zengcheng City Area Main Functional Area Planning, which designated strict controls for the northern ecological-agriculture and eco-tourism zone, the central urban living area, and the southern industrial development zone. Additionally, stringent ecological restoration measures for towns and villages across the district played a significant role in shaping this spatial evolution.  
Over time, agricultural land in Zengcheng District has become increasingly fragmented, particularly in Zhongxin Town, Shitan Town, and Yongning Street, due to accelerated urbanization and industrialization. After 2015, agricultural land in Xintang Town and Xiancun Town experienced a significant decline, influenced by the implementation of rural revitalization strategies and land improvement projects. Between 2010 and 2020, efforts in forest conservation and greening resulted in the transformation of portions of agricultural land into ecological land, thereby enhancing the quality of the ecological environment.  
Zengcheng District is characterized by a substantial proportion of ecological land, which plays a crucial role in maintaining environmental functions. However, this ecological significance has been increasingly compromised due to the encroachment of land for production and residential purposes. Between 2010 and 2015, forests and grasslands in Licheng Street, Xintang Town, and Shitan Town were significantly reduced, with some agricultural land being converted into forested areas. Following 2015, the decline in grasslands continued, although ecological protection measures were implemented, leading to an increase in the proportion of ecological land from 16.87% in 2010 to 18.56% in 2020. These gains were particularly concentrated in Xiaolou, Zhongxin, and Paitan Towns, where ecological restoration efforts successfully enhanced the local environment and promoted the coordinated development of production, living, and ecological spaces (PLESs).  
To analyze the evolution of PLES conflicts in Zengcheng District, we compare historical and contemporary spatial patterns. Using two photographs--one from 1989 (Figure 5) and another from the present day (Figure 6)--we illustrate the significant transformations in land use and the increasing intensification of conflicts between production, living, and ecological spaces. These visual aids provide valuable context for understanding the spatiotemporal evolution of these conflicts and the influence of urbanization on the district's land-use dynamics.  
Figure 5: Photo of Zengcheng District Today. Note: Sourced from Baidu and other online platforms.
# Symmetry of the Non-Analytic Solution of the Dirac Equation Inside the Proton of Hydrogen Atoms
## 2 Analytic Solution of the Dirac Equation Inside the Proton for the S-States of Hydrogen Atoms  
For hydrogen atoms, the Dirac equation for the f- and g-components of the Dirac bispinor has the form (in the natural units \(\hbar=\mathrm{m_{e}=c=1}\))--see, e.g., textbook [29]:  
\[\mathrm{df(r)/dr=(k-1)f(r)/r-h_{1}(r)g(r),} \tag{1}\]  
\[\mathrm{dg(r)/dr=h_{2}(r)f(r)-(k+1)g(r)/r,} \tag{2}\]  
where  
\[h_{1}(r)=W-1-V(r),h_{2}(r)=W+1-V(r). \tag{3}\]  
In Equation (3), \(W\) is the energy and V(r) is the potential energy of the atomic electron. In Equations (1) and (2)), \(k\) is the eigenvalue of the operator \(K=b(2 Ls+1)\) mentioned in the Introduction.  
For the S-states (on which we focus in the present paper), \(k=-1\). Then, from Equation (2) we find:  
\[f(r)=[dg(r)/dr]/h_{2}(r). \tag{4}\]  
On substituting Equation (4) in Equation (1), we obtain:  
\[r(d^{2}g/dr^{2})+[2+r(dV/dr)/h_{2}(r)](dg/dr)+rh_{1}(r)h_{2}(r)g=0. \tag{5}\]  
Now, we study this equation and its solution near the origin, i.e., near \(r=0\). After denoting  
\[V_{0}=-\mid V(0)\mid=const>0, \tag{6}\]  
Equations (4) and (5) can be simplified as follows:  
\[r[d^{2}g(r)/dr^{2})+2[dg(r)/dr]+hrg(r)=0, \tag{7}\]  
\[f(r)=[dg(r)/dr]/h_{20}, \tag{8}\]  
where  
\[h=(W+V_{0})^{2}-1=const, \tag{9}\]  
\[h_{20}=W+1+V_{0}=const. \tag{10}\]  
We start from the traditional practice of limiting the search for the solutions by the class of analytic functions, i.e., by functions that can be adequately represented by their expansion into the Laurent series. Namely, we seek the solutions in the form:  
\[g(r)=r^{d}\sum_{m=0}^{\infty}a_{m}r^{m} \tag{11}\]where \(a_{m}=const\) for any \(n\) and \(d=const\). On substituting Equation (11) into Equation (7) and requesting the mutual cancellation of the lowest powers of \(r\), we find:  
\[d(d+1)=0, \tag{12}\]  
so that \(d_{1}=0\) and \(d_{2}=-1\).  
For the solution with \(d_{1}=0\), corresponding to  
\[g_{1}(r)\approx a_{0}, \tag{13}\]  
from Equation (8) we get:  
\[f_{1}(r)\approx a_{1}/h_{20}. \tag{14}\]  
For the solution with \(d_{2}=-1\), corresponding to  
\[g_{2}(r)\approx a_{0}/r, \tag{15}\]  
from Equation (8) we obtain:  
\[f_{2}(r)\approx-a_{0}/(\ h_{20}r^{2}). \tag{16}\]  
The normalization integral is  
\[\int\limits_{0}^{\infty}dr\ r^{2}\Big{[}\hat{f}^{2}(r)+g^{2}(r)\Big{]}=1 \tag{17}\]  
It is easy to see that for the solution with \(d=-1\), the normalization integral diverges at \(r=0\), so that this solution should be rejected. For the solution with \(d=0\), the normalization integral converges at \(r=0\), so that this solution should be accepted.