esa-sceva/satcom-chunk-collection huggingface dataset:
## Data, Parameter Ranges, and a Framework for Clinical Application  
### Data  
Various relevant data sets exist, ranging from experimental studies of cancer cells to clinical studies of patients undergoing hormonal therapy. In this section, we summarize some of these data.  
Perhaps the most used data set for prostate cancer model validation at the patient level comes from a clinical trial at Vancouver Prostate Center. The study admitted patients who showed a rising serum PSA level after undergoing radiotherapy without evidence of distant metastasis or being previously subjected to hormonal therapy, with the exception of less than three months of neoadjuvant hormonal therapy [91]. Additionally, all patients exhibit high serum PSA levels (\(\geq 6\)\(\mu\)g/L) prior to the study. Another set of data comes from a clinical study [46], where three stage C and four stage D prostate cancer patients were treated with intermittent hormonal therapy for 21 to 47 months (two to four cycles). All patients show decreasing PSA levels during the course of the study. Moreover, there are various clinical studies that have been used in mathematical modeling. For instance, patients' data sets were used in the study by [PERSON] et al. [88], which can also be extracted directly from their paper. Another study [41] also utilized additional data sets from different clinical studies in the United States and Japan [97; 98; 99; 100]. Additionally, we would like to point out a clinical trial [20; 61] that was the first for prostate cancer that utilizes adaptive therapy to treat metastatic castrate-resistant prostate cancer. However, as this is ongoing, the data may not be readily available. For a review of clinical studies that focus more on the statistical correlation of different variables for prostate cancer, we refer the readers to the work by [PERSON] [77].
## 3 Results and Discussion
### Regeneration and Reuse of MPC  
Except for high adsorption capacity, the stability and reusability of adsorbents are also important. Therefore, regeneration and reuse of the MPC adsorbent was further investigated. The adsorption capacity of MB on MPC slightly decreases in successive cycles (Figure 5d). An Fe leaching test was performed to detect the Fe content in the aqueous solution by inductively coupled plasma (ICP). No Fe leaching was detected from the MPC in the aqueous phase after the completion of the MB adsorption, showing that the MPC has very high stability for MB adsorption. This observation could be interpreted by the strong chemical interaction between the Fe\({}_{3}\)O\({}_{4}\) and PC, as detected by the XPS results. Previous results have indicated that not all the adsorbed dye could be removed from the surface of the adsorbent during the regeneration and reusability tests [42]. Similarly, the complete removal of the adsorbed MB on MPC was hardly achieved due to the strong interaction between the MB and MPC in the regeneration process, thereby leading to a decrease of adsorption capacity for MB in successive cycles. However, the adsorption capacity of MB still remains at 80.7% after reuse in the third cycle, indicating the good reusability of the as-prepared adsorbent. Furthermore, the adsorbent still has high magnetic sensitivity after three cycles.
# Optimization of Nonspherical Gold Nanoparticles for Photothermal Therapy
## 3 Results and Discussion
Therefore, it is necessary to obtain the ranges of size parameters that keep the volume absorption coefficient greater than a threshold. Herein, a threshold of 90% of the maximum volume absorption coefficient was considered for each nanoparticle. Figure 5 shows the size parameters of gold nanoparticles, ensuring that the volume absorption coefficients are larger than 90% of the maximum coefficients, at an excitation wavelength of 800 nm. The range of length is much wider than that of the aspect ratio for each nanoparticle. Compared to the nanocylinder and nanorod, the nanospheroid exhibited a slightly wider range of length and a larger aspect ratio. The min-max and mean-std values of the aspect ratio and length of the gold nanoparticles, ensuring that the volume absorption coefficients are larger than 90% of the maximum coefficients, at four typical excitation wavelengths (800, 808, 820, and 1064 nm), are listed in Table 3. The results indicate that the nanoparticles have larger size parameters and wider ranges at the longer excitation wavelength. Figure 4: (**a**) Maximum volume absorption coefficients \(\alpha_{abs,max}\), (**b**) optimal aspect ratio \(R_{opt}\), and (**c**) optimal length \(L_{opt}\) of gold nanospheroid, nanocylinder, and nanorod at an excitation wavelength of 800 nm. The refractive index of tissue is varied from 1.35 to 1.7 in a step size of 0.05. Figure 5: Aspect ratio and length of the gold (**a**) nanospheroid, (**b**) nanocylinder, and (**c**) nanorod at an excitation wavelength of 800 nm, ensuring that the volume absorption coefficients are larger than 90% of the maximum coefficients. The refractive index of tissue is 1.44.
###### Abstract  
A wheelset bearing is one of the main components of the train bogie frame. The early fault detection of the wheelset bearing is quite important to ensure the safety of the train. Among numerous diagnostic methods, envelope analysis is one of the most effective approaches in the detection of bearing faults which has been amply applied, but its validity greatly depends on the informative frequency band (IFB) determined. For the wheelset bearing faulty signal, it is often difficult to identify the IFB and extract fault characteristics due to the influence of complex operating conditions. To address this problem, a novel method to select optimal IFB, called the Mkurtogram, is proposed for railway wheelset bearings fault diagnosis. It takes the multipoint kurtosis (Mkurt) of unbiased autocorrelation (AC) of the squared envelope signal generated from sub-bands as assessment indicator for the first time. The fundamental concept which inspires this proposed method is to make full use of regular periodicity of AC of squared envelope signal. In the AC domain, the impulsiveness and periodicity, two distinctive signatures of the repetitive transients, have achieved a united representation by Mkurt. A simulated signal with multiple interferences and two experimental signals collected from wheelset bearings are applied to verify its performances and advantages. The results indicate that the proposed method is more effective to extract the wheelset bearings fault feature under complex interferences. It can not only decrease the influence of large impulse interference and the discrete harmonics interference, but also effectively overcome the influence of amplitude fluctuation caused by variable working conditions. Moreover, based on the periodic directivity of Mkurt, the proposed method also can be applied to the compound faults diagnosis of the wheelset bearing.  
railway wheelset bearing; fault diagnosis; unbiased autocorrelation function; multipoint kurtosis; envelope analysis +
Footnote †: journal: Applied Sciences
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 4 Experimental Results and Discussion
### Performance Comparison with Other Single Optimization SVR Methods  
To verify the MMPSO model's performance in optimizing the SVR hyperparameters, we select HCLDMSPSO-SVR, HCLPSO-SVR, PSO-LSSVM, and BPNN models for comparison. We set the MMPSO-SVR, HCLDMSPSO-SVR, HCLPSO-SVR, and PSO-LSSVM models to the maximum iteration limit of 1000, with a population size of 50. We selected the HCLDMSPSO [48] and HCLPSO [46] models as representative SOTA swarm-intelligence algorithms. We built a BPNN with an input layer consisting of five nodes. The hidden layer consisted of 13 nodes, and the output layer consisted of 8 nodes. The objective function was set to the minimum fitness value, and the best optimization result was preserved during the training process. To analyze the hyperparameter influence for an SVR small-sample human body size estimation, we compared the single MMPSO-SVR optimization model enhanced with the population's diversity to avoid local optima in subsequent iterations, thus improving overall accuracy.  
Figure 8: Pattern making and virtual simulation using body dimensions predicted by the GRFN model: (**A**) GRFN model, (**B**) 2D pattern making, (**C**) pant pattern, and (**D**) 3D simulation try-on. (Notes: In subfigure (**B**), the blue curve represents the back pattern piece of the pant; the red curve represents the frontal pattern piece of the pant; the black dot curve indicates an auxiliary line.)  
Based on our quantitative analysis, we compared each model's prediction results, as shown in Table 8. By assessing the \(R^{2}\) statistics, we concluded that the degree of fit for a single MMPSO-SVR model was the highest in all parts, and the \(e_{RMSE}\) and \(e_{MAE}\) measures were the smallest of all prediction methods. This again clearly demonstrates the effectiveness of our proposed single unmodified MMPSO-SVR global optimization model. Compared with other single optimization SVR hyperparameter methods, our method demonstrates superior efficacy by evading local optima and attaining the global optimum.
# Multimedia Vision for the Visually Impaired through 2D Multiarray Braille Display
## 3 Proposed eBook Reader Application for 2D Braille Display
### Parsing Method of Multimedia Contents for the VI
#### 3.3.2 EPUB Formats  
Using Epublib, an open library that supports EPUB v2.0 and v3.0, a book in the EPUB format was extracted on the Android OS. As shown in Figure 10, Epublib is designed to facilitate extraction of the title, subtitles, and main contents of the EPUB book using tags [38]. Through the Epublib library that is converted into the Android format, parsing is performed on the Braille OS by following the usual EPUB parsing method using NCX [39]. In contrast to the open source of DAISY v3.0, the method using Epublib can extract all contents of the EPUB based on the HTML document without any compatibility problem with the application. Furthermore, because it has been developed for a long time, this open source has been well optimized and debugged. The extraction method is also very simple, and the contents can be extracted in the HTML format. Furthermore, EPUB and DAISY are based on the style of a HTML document. Therefore, to extract text contents from books in these formats, a process of extracting text from HTML is required: using the Jsoup open library, tags of corresponding contents are parsed, and then, all contents are extracted [40]. After extracting all the contents, they are first classified into text, audio (MP3), and images, and then allocated to the respective player or viewer.
## 5 Field Application and Monitoring
### Field Monitoring Results  
The results of field monitoring are presented in Figure 19. The largest values of BSRS (Bolt Stress Release System) for the depths of 8.5 m and 2.5 m in the roof strata are 44 mm and 36 mm, respectively. The values for the depths of 7.5 m and 1.5 m are no more than 54 mm and 51 mm, respectively. Initially, the general BSRS value changes rapidly in the first 10 days after the roadway excavation but then gradually stabilizes. The bearing loads of the bolts and cables are evenly distributed. Additionally, the difference between the loads on the bolts and cables is minimal. The combined effect of the bolt and cable support, along with the overall load-bearing structure, strengthens the control of the coal roadway.  
The subsidence of the roof and convergence of the two sides are both within acceptable limits, with maximum values of 79 mm and 191 mm, respectively, indicating minimal deformation during the two months of roadway excavation. The subsidence of the top plate generally corresponds to the downward movement of the neutral axis, which is effectively supported. The convergence of the two coal sides has two main stages of development. The first stage occurs within the first 15 days after excavation and then stabilizes. The second stage occurs between 25 and 40 days. Furthermore, the supporting structure remains stable and does not require any repairs during the roadway excavation, resulting in cost savings and reduced manpower requirements for specialized coal miners. Overall, the deformation of the surrounding rock is much smaller compared to the initial five supporting schemes, confirming the effectiveness of the supporting scheme with the core of the HSCTS (High-Strength Composite Truss Structure).  
Figure 18: Optimization of support scheme.  
Figure 19: Field application results. (**a**) surrounding rock deformation; (**b**) the BSRS; (**c**) cables loads; (**d**) bolts loads; (**e**) field support effect.
# Large Field-Size Elliptic Curve Processor for Area-Constrained Applications
## 1 Introduction
### Research Gap  
Various architectural techniques such as pipelining, parallelism, and different flavor of finite field multipliers, i.e., digit-serial, digit-parallel, and bit-parallel, etc., have been utilized to optimize the performance of ECPM operation in [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. However, due to their higher area complexity, these architectures are not suitable for applications related to secure robotics communication, WSNs, automotive vehicles, RFID, etc. Therefore, the objective of this work is to provide a low-complexity hardware accelerator over a large ECC field size.
## 5 Probability of Data Error in IEEE 802.15.4
### Chip Error Rate to Data Symbol Error Rate
#### 5.1.2 Non-Coherent Detection  
In the non-coherent case, we can only identify the phase transitions during the chip intervals. For a 32-chip long sequence, there will be 31 phase transitions, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Symbol** & **Data Bits** & **Phase Transitions** \\ \hline \(S_{0}\) & 0000 & ++ - - - - ++ - ++ - ++ - ++ -  
Where \(+\) means a \(\pi/2\) increase in the phase and \(-\) means a \(\pi/2\) decrease in the phase. We can think of the phase transition sequence of a data symbol in Table 4 as a vector in Hamming space and calculate the Hamming distances between these vectors. The recurrences of Hamming distances from a data symbol to the other data symbols in the set are given in Table 5.  
The union upper bound is obtained as:  
\[P_{d}\leq\sum_{i=1}^{N}r_{i}P(h_{i},P_{c}) =2P(13,P_{c})+2P(14,P_{c})+3P(15,P_{c})+3P(16,P_{c})+2P(17,P_{c})\] \[+2P(18,P_{c})+P(31,P_{c}) \tag{39}\]  
where \(h_{i}\) and \(r_{i}\) are given in Table 5. \(i\) is the index for columns, and \(N=7\) is the number of columns in the table.  
Again, the computational complexity of the upper bound is \(O(1)\).
## 5 Experimental Testing (Setup)
#### Necessary Steps for Coordinates of the Sensed Object
The graph is shown in Figure 9.  
Experimental measurements were performed for each axis of the sensing object 20 times. The measured values were statistically processed, and the normality of the data of individual coordinates of the sensing object was verified first. Results are shown in the graphs in Figures 9-11, where we can see that the unique points are, for the most part, at a sufficiently small distance from the line. It, therefore, implies the normality of the captured data. Figure 8: Determination of the object’s coordinates. Figure 9: Normality test for “\(x\)” axis of sensing object. We perform the test of normality based on the hypothesis that the sample file with the range \(n=20\) comes from the normal distribution. After the graphical test of normality verification, we chose the level of significance \(\alpha=0.05\). Then, we tested it with the zero hypothesis H0, which means that the sample file is from the normal distribution opposite to the alternative hypothesis H1, which comes from another distribution. Finally, the results based on the S.W. test confirms goodness, so the selected sample file comes from the normal distribution probability (value of tested statistics = 0.9726, \(p\)-value = 0.4335).
# Uniform Convexity in Variable Exponent Sobolev Spaces
## 5 Uniform Convexity of the Luxemburg Norm on \(W^{1,p}_{0}(\Omega)\)  
This section is devoted to the proof of the fundamental result that for a variable exponent \(p\) bounded away from \(1\) and \(\infty\), the Luxemburg norm on the Sobolev space \(W^{1,p}_{0}(\Omega)\) is uniformly convex. The originality in this section is the range \(1<p<2\). A few well-known facts about the modular spaces \(L^{p}\) are summarized below.  
Theorem 5 ([2, 14]): Assume \(p_{+}<\infty\). In the notation of Definition 5, for any \(u\in L^{p}(\Omega)\),  
\((i)\quad\varrho(u)=1\) if and only if \(\|u\|_{\varrho}=1\).  
\((ii)\quad\min\{\|u\|_{\varrho}^{p_{+}},\|u\|_{\varrho}^{p_{-}}\}\leq\varrho(u )\leq\max\{\|u\|_{\varrho}^{p_{+}},\|u\|_{\varrho}^{p_{-}}\}\).  
Let us recall the following definition.  
Definition 7: Let \(\Omega\subseteq\mathbb{R}^{n}\) be a domain and \(p\in\mathcal{P}(\Omega)\) be an admissible exponent. Denote the closure of \(C_{0}^{\infty}(\Omega)\) in \(W^{1,p}(\Omega)\) (Definition 6) by \(W^{1,p}_{0}(\Omega)\).  
The following theorem is well known.  
Theorem 6 ([3, 16]): For \(1<p_{-}\leq p_{+}<\infty\), then on \(W^{1,p}_{0}(\Omega)\) the norm \(\||\
abla u|\|_{\varrho}\) is equivalent to the Luxemburg norm \(\|u\|_{\rho}\). Specifically, there exists \(\alpha>0\) depending only on \(\Omega\) and \(p\) such that for all \(u\in W^{1,p}_{0}(\Omega)\),  
\[\frac{1}{\alpha}\,\|u\|_{\rho}\leq\||\
abla u|\|_{\varrho}\leq\alpha\,\|u\|_{ \rho}\,.\]  
The following result holds.  
Theorem 7: Let \(\Omega\subset\mathbb{R}^{n}\) be a domain and \(p\) be an admissible exponent with \(1<p_{-}\leq p_{+}<\infty\). Then, the Sobolev-Luxemburg norm \(\|\cdot\|_{\rho}\) on \(W^{1,p}(\Omega)\) is uniformly convex. Likewise, the norm \(u\to\||\
abla u|\|_{\varrho}\) defined on \(W^{1,p}_{0}(\Omega)\) is uniformly convex.  
Proof: Let \(0<\varepsilon<2\) and take \(u\) and \(v\) with \(\|u\|_{\rho}=\|v\|_{\rho}=1\), \(\|u-v\|_{\rho}\geq\varepsilon\); that is, by virtue of Theorem 5, \(\rho(u)=\rho(v)=1\) and \(\rho\left(\frac{u-v}{2}\right)\geq\left(\frac{\varepsilon}{2}\right)^{p_{+}}\). On account of Theorem 4 for some \(\eta>0\) it holds \(\rho\big{(}\frac{u+\eta}{2}\big{)}<1-\eta\). On account of \((ii)\) in Theorem 5, it follows that \(\big{\|}\frac{u+\eta}{2}\big{\|}_{\rho}<1-\theta\), for some \(0<\theta<1\).  
The rest of the claim follows along the same lines from a direct application of Theorem 2.
## 3 Method
### Method Architecture  
To address the confusion among foreground instances of different classes, as well as the confusion between foreground features and background features in remote sensing FSOD, we propose a remote sensing image FSOD framework named PSGFE based on transfer learning, with MSOCL [22] as the baseline model. The overall architecture of this framework is shown in Figure 1a. On the one hand, the framework utilizes PSGM to mine potential foreground instances from the background, which are then filtered and refined to generate high-quality pseudo-samples for training, thereby addressing the confusion between background and foreground instance features. On the other hand, FEM is employed to enhance ROI features in order to highlight the core features of each class and enhance the classification capability of the model, thus resolving the issue of class confusion.  
PSGM integrates self-training and teacher-student paradigm. Self-training [44] leverages the pseudo-samples generated by the model itself to further train the model. The teacher-student paradigm uses the output of the teacher model to guide the training of the student model [45]. Specifically, the teacher model makes predictions on unlabeled background regions to generate a set of instructional information, which guides the student model to produce pseudo-samples for training. This approach enables the model to extract potential foreground instances from the background, thereby allowing it to learn the real background features and effectively distinguish them from the foreground features. PSGM is composed of STPPGN and STCPSGN, with their structures illustrated in Figure 1b and Figure 1c, respectively. FEM employs multiple large-kernel convolutions with different kernel sizes and depthwise separable convolutions to enhance ROI features. Large-kernel convolutions expand the receptive field to better capture global image information [46], while depthwise separable convolutions perform convolution operations on each input channel separately, helping the model capture richer local features [47]. This method helps extract distinctive core features of each class from ROIs, thereby addressing the issue of class confusion. Its structure is shown in Figure 1d.  
The overall architecture of PSGFE is shown in Figure 1a. It is divided into three parts: Branch 1 is dedicated to object detection using Faster R-CNN with FEM, starting from RPN to generate proposals, followed by ROI pooling and FEM, and finally the original bounding box-head (original BBH) for refinement and classification. Branch 2 is the pseudo-sample generation branch, including the backbone, STPPGN, and STCPSGN. The STPPGN takes in unlabeled background data to produce pseudo-proposals, and the STCPSGN performs ROI pooling and classification regression on them, retaining high-confidence samples to generate high-quality pseudo-samples to integrate into the training set. Branch 3 is the contrastive learning branch for extracting and enhancing multi-scale features to compare them with original proposals and pseudo-samples. It is untrainable and shares weights with those in Branch 1.