esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contribution  
In this study, we propose an OTT user classification method that responds to real-world problems. OTT users can be divided into several groups according to their data usage, and the number of members for each group is not consistent. While existing studies show good performance in classifying groups composed of similar numbers, their classification accuracy for classes with small numbers reduces with the imbalanced data environments encountered in real-world situations. We thus propose a framework to solve this problem as this is an issue that is readily discoverable in the OTT market but has not yet been considered as a research topic. First, when classifying a class with small numbers using ML, we tried to increase the classification accuracy by setting the weight for the error occurring in the class higher than the error occurring in a class with a large number of members. As the weights were set high, and the ML classifier was set to avoid errors with high weights, we were able to improve the classification accuracy of classes with a small number. To set the weight of the error, the probability of indicating the class to which a sample belongs was calculated based on the costs of misclassification errors, and, by setting the cost high, the weight for a specific error could be set high. In addition, in order to respond to changes in consumer trends, we constructed a module that can periodically update the training data. Unlike existing studies that do not take changes in trends into account, our framework periodically updates training data, which allows us to respond to frequent trend changes with regard to OTT users. According to experimental results, it can be seen that, even though consumer trends change, our framework shows a constant performance; however, the performance of existing methods degrades severely. As such, our study suggests ways to solve the practical problems encountered in the OTT market. In other words, it has great significance as the OTT user analysis framework reported here can be utilized to realistically analyze OTT users.  
The remainder of this paper is organized as follows. Section 2 discusses the importance of analyzing OTT-related trends and usage patterns. Furthermore, we examine the limitations of conventional studies. Section 3 introduces the OTT user analysis framework proposed in this study, and Section 4 examines the experimental results. Finally, Section 5 presents conclusions and a brief description of future studies.
# Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information
## 2 Related Work
### Polarimetric and Spatial Features of PolSAR Image  
For PolSAR images, there are several representations of the collected data [2]. For original single-look complex (SLC) PolSAR images, at each pixel, the data is stored in the scattering matrix \(\mathbf{S}=\left[S_{hh},S_{hp};S_{vh},S_{vv}\right]\). According to the reciprocity theorem, we have \(S_{hp}=S_{vh}\). Therefore, we can transform the scattering matrix into a vector form \(\mathbf{k}_{L}=\left[S_{hh}\ \sqrt{2}S_{hv}\ S_{vv}\right]^{T}\) using linear basis, where \(T\) denotes transpose of a vector. For both training data and test data, the covariance matrix can be further derived as \(\mathbf{C}=\left\langle\mathbf{k}_{L}\mathbf{k}_{L}^{H}\right\rangle\), where \(\left\langle\cdot\right\rangle\) denotes the operation of multi-looking, \(\mathbf{k}_{L}^{H}\) denotes conjugate transpose of \(\mathbf{k}_{L}\). Similarly, another vector form of the SLC PolSAR data can be obtained as \(\mathbf{k}_{P}=\left[S_{hh}+S_{vv}\ S_{vv}-S_{hh}\ 2S_{hp}\right]^{T}\!\!/\sqrt{2}\) by using the Pauli basis, and the multi-looking coherency matrix can be derived as \(\mathbf{T}=\left\langle\mathbf{k}_{P}\mathbf{k}_{p}^{H}\right\rangle\)[2].  
In the proposed approach, two feature spaces are constructed in parallel to account for different information contained in the PolSAR images, as shown in Figure 1. For one thing, the polarimetric features describe the pixel-wise scattering mechanisms that are related to the dielectric properties of ground material. For another, the spatial features describe the relationship between neighboring pixels that are related to the ground object structures. Therefore, those two sets of features contain complementary information.
###### Abstract  
This study aims to ensure that the maximum crack width of underground working linings for compressed air energy storage (CAES) meets the allowable limit under high internal pressure conditions. Drawing on crack width calculation methods from hydraulic tunnels, this study proposes a design method for segmented linings with preset seams. The method accounts for the shear mechanical behavior of the sliding layer, with parameters determined through laboratory testing. A typical case study validates the reliability of the crack width calculation method that accounts for lining damage and plasticity. The study determined, from an engineering case, that six seams are optimal when the lateral pressure coefficient \(\lambda\) is below 1, while four seams are more suitable when \(\lambda>1\). Additionally, reinforcement ratios and retractable joints of the segmented lining were designed for the case. When the surrounding rock quality is lower than that of hard rock mass and gas pressure exceeds 12 MPa, monolithic cast-reinforced concrete linings often fail to meet the allowable crack width limits. However, segmented linings offer greater flexibility, as they can still meet the requirements even with fair-quality rock mass. These findings provide critical theoretical foundations for the design of CAES workings under high internal pressure.  
high-pressure CAES; segmented lining; design method; optimal seam number; maximum crack width of lining Article
## 5 Discussion
### Limitations and Future Research  
Our study, while providing a robust framework, has certain limitations that open avenues for future research. First, the model's performance is highly dependent on the accuracy of demand forecasts. The certainty-equivalent approach does not explicitly model the stochastic nature of demand, which can lead to suboptimal decisions as discussed. Future work could explore more advanced stochastic optimization techniques or two-stage programming to better account for uncertainty.  
Second, practical implementation requires a sophisticated IT infrastructure. This includes systems for real-time inventory tracking, automated data pipelines for the demand forecasting model, and sufficient computational power to solve the MILP on a daily basis.  
For future research, several promising directions can extend the contributions of this study. First, to provide a more comprehensive benchmark, our ADP model could be evaluated against established warehouse heuristics and purely deterministic models to better quantify the value added by the adaptive framework. Furthermore, the model could be enhanced by explicitly incorporating sustainability objectives. Inspired by the work of [PERSON] et al. [21] on optimizing dock door allocation to save energy, our objective function could be augmented to include energy consumption costs associated with internal vehicle movements, creating a trade-off between traditional operational costs and environmental impact. Building on this, the advanced stochastic optimization methods discussed by [PERSON] et al. [22] could be explored to manage the dual uncertainties of demand and energy pricing, moving beyond our certainty-equivalent approach to a more distributionally robust framework. Finally, the integration of our framework with warehouse automation presents a significant opportunity. The current ADP model could be adapted to generate optimal task sequences for an Automated Storage and Retrieval System (AS/RS), where the model's decisions would directly command robotic agents. This extension would involve reformulating cost functions to reflect robotic operational parameters and could leverage specialized optimization techniques, such as the cross-entropy method proposed by [PERSON] et al. [23], to solve the resulting complex scheduling problems.
## 2 Radiolabeled Tacrine and Its Derivatives Used in Alzheimer's Disease
### Radiotracers Based on Tacrine
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Tacrine-Based Radiotracers** & **Research Purpose and Conclusions** & **References** \\ \hline \hline \multirow{8}{*}{[9-14C]acrine ([14C]THA)} & Distribution of the radiopreparation in the rat body after both intravenous and oral administration, studies of the process of tacrine metabolism in the brain & \\  & In both cases, [14C]THA crosses the blood–brain barrier; & \\  & Biodistribution of the radiotracer is similar, although after oral administration, the absorption of the radiotracer persists noticeably longer in the organs; & \\  & The highest levels of radioactivity were detected in the cortex, hippocampus, cerebellum and striatum; & [20; 21] \\  & The radiotracer is excreted mainly in the urine and partly through the intestines; & \\  & Tacrine metabolism practically does not occur in the brain; & \\  & The main metabolite of tacrine is [9-14C]I-OH-THA, and it has a very limited ability to cross the blood–brain barrier. & \\ \hline \hline \multirow{8}{*}{[7-3H]Iacrine ([3H]THA)} & AChE enzymatic test, localization of [3H]THA binding sites in the rat brain, kinetic parameters of [3H]THA binding, ability of THA to inhibit AChE & \\  & \(\bullet\) [3H]THA binds to the membrane almost rapidly but in a reversible manner; & \\  & Binding of [3H]THA in the rat brain is not blocked by a number of other neurotransmitters/neuromodulators, so the binding site of [3H]THA is different from the sites of action of these compounds; & \\  & \(\bullet\) [3H]THA binding sites are not located together with the activity of acetylcholine (and other acetylcholinesterase inhibitors), so it can be concluded that the clinical effect of THA may also result from an action other than through the cholinergic neuronal system. & \\ \hline \hline \multicolumn{2}{p{113.8 pt}}{[7-3H]Iacrine and [5,7-3H]Iacrine} & Procedure for the synthesis of tritium-labeled tacrine, analysis of products using TLC, HPLC and NMR methods & [23] \\ \hline \hline \multirow{8}{*}{[99 mTc]Tacrine solution ([99 mTc]Tc-TS)} & Studies on the intravenous (IV) and intranasal (IN) administration of tacrine labeled with technetium-99 mTc, biodistribution studies in mice & \\  & \(\bullet\) & The tacrine concentration in the brain after IN administration is significantly higher than after IV administration; & [24] \\ \cline{1-1}  & Nasal-to-brain administration of tacrine may provide an alternative route to the currently used oral route, which is limited by tacrine’s low bioavailability and pronounced side effects. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Radiotracers based on tacrine.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
###### Abstract  
This article presents a numerical implementation of the activation process for gigabit and 10 gigabit next generation and Ethernet passive optical networks. The specifications are completely different because GPON, XG-PON and NG-PON2 were developed by the International Telecommunication Union, whereas Ethernet PON was developed by the Institute of Electrical and Electronics Engineers. The speed of an activation process is the most important in a blackout scenario because end optical units have a timer after expiration transmission parameters are discarded. Proper implementation of an activation process is crucial for eliminating inadvisable delay. An OLT chassis is dedicated to several GPON (or other standard) cards. Each card has up to eight or 16 GPON ports. Furthermore, one GPON port can operate with up to 64/128 ONUs. Our results indicate a shorter duration activation process (due to a shorter frame duration) in Ethernet-based PON, but the maximum split ratio is only 1:32 instead of up to 1:64/128 for gigabit PON and newer standards. An optimization improves the reduction time for the GPON activation process with current FLOAM messages and with no changes in the transmission convergence layer. We reduced the activation time from 215 ms to 145 ms for 64 ONUs.  
activation process; EPON; GPON; MATLAB; NG-PON2; timing; transmission convergence layer; XG-PON +
Footnote †: journal: Applied Physics Letters  
_Article_
# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 1 Introduction
There is convincing evidence that the hybrid modeling approach based on LS-SVR and RF is reliable and that the SSA produces superior outputs. Thus, for improving the forecasting performance of the standard models, the data preprocessing technique is adopted in the current study. The primary objectives are as follows:  
1. Linking SSA with machine learning techniques (i.e., LS-SVR and RF) to construct hybrid models (SSA-LSSVR and SSA-RF) for monthly rainfall forecasting in two reservoir watersheds of Taiwan where the hybrid models have not been applied before. 2. Comparison between the hybrid models (i.e., SSA-LSSVR and SSA-RF) and the standard models (i.e., LS-SVR and RF) to validate the efficiency of the data preprocessing technique. The remainder of this paper is arranged as follows. Section 2 (\"Study Sites and Data collection\") summarizes the study sites and the data collection. Section 3 (\"Methodology\") briefly describes the machine learning techniques (LS-SVR and RF), data preprocessing technique (SSA), coupling of SSA with LS-SVR and RF, and measurement of model performance. Section 4 (\"Results and Discussion\") shows the results of the proposed models for monthly rainfall forecasting, and elaborates and discusses the findings of the current study. Section 5 (\"Conclusions\") gives the conclusions and considers the need for future work.
# A Fine-Tuning Based Approach for Daily Activity Recognition between Smart Homes
## 2 Related Work
### The Development Process of Fine-Tuning  
Deep transfer learning methods offer unmatched advantages in accuracy over traditional transfer learning methods. Compelling results have been achieved in the field of images. [PERSON] et al. pioneered the study of the transferability of deep neural networks [16]. Based on the AlexNet network trained on the ImageNet dataset [17], the authors performed fine-tuning experiments layer by layer on layers one to seven. Finally, two conclusions were drawn: one was that deep transfer networks are more effective than the random initialization of weights. Second, the transfer of network layers can accelerate the learning and optimization of the network.  
Fine-tuning is difficult to handle in a situation where the training data and test data are distributed differently. Many deep learning methods have developed an adaptation layer to accomplish the adaption of source and target domain data.  
There is a Deep Domain Confusion approach (DDC) for solving the adaptive problem of deep networks, first proposed by [PERSON] et al. [18]. To begin with, they also used the AlexNet network trained on the ImageNet dataset [17]. Then, DDC fixed the first seven layers of AlexNet and added the adaptive metric MMD [19] on the eighth layer. This was the previous layer of the classifier. [PERSON] et al. proposed a Deep Adaptation Networks approach (DAN) [20], which is an extension of DDC. DAN changes the migration of only one layer in the DDC to multiple layers. It adds three adaptive layers to the first three layers of the classifier at the same time. In addition, DAN uses a multi-core MMD metric (MK-MMD) [21] with better characterization capability. Additionally, it solves the problem of selecting kernel functions in a single MMD. Furthermore, an increasing number of researchers are focusing on methods for migration using trained deep network models such as AlexNet, Resnet, etc. [22; 23; 24; 25; 26; 27]. This significantly reduces the model training time and improves the efficiency.  
Up to this point, most of the fine-tuning studies have been based on large datasets in the image domain. Studies in the time series domain were almost non-existent until [PERSON] et al. proposed a time-series-based network migration method on the basis of fine-tuning [6]. Additionally, the DTW Barycenter Averaging (DBA) method [28] is used to find out the datasets that are similar to the target domain for migration. The results show that deep networks are portable in the time series domain with impressive success. This also gives us more confidence to explore the possibility of fine-tuning in the smart home domain.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Path Prediction Training
#### 2.5.5 Connectivity Component  
The last component of \(\mathcal{L}_{\text{pinn}}\) is the connectivity loss, denoted as \(\mathcal{L}_{\text{connectivity}}\). Its purpose is to penalize predictions that contain more than one path. To achieve this, we use the algebraic connectivity [101] and the spectral properties of path graphs [102]. Formally, let \(\mathbb{L}=D_{\text{out}}-\hat{Y}\) be the Laplacian of the graph, and let \(\{\lambda_{i}\}\) be the eigenvalues of \(\mathbb{L}\). We define \(C\) as the number of components in the graph, \(\epsilon\sim 0\) a small value, and \(\Lambda\) an estimate of the maximum path length from the matrix \(\hat{Y}\):  
\[C=\sum_{i=1}^{N}(|\lambda_{i}|<\epsilon),\qquad\Lambda=\sum_{ij}\hat{Y}_{ij}+1 \tag{12}\]  
If \(\hat{Y}\) only contains a path and isolated nodes, then we expect \(C_{\text{expected}}=|V|-\Lambda+1\) components in the graph. Based on this value, we can compute:  
\[P_{\text{component}}=\frac{\left(C-C_{\text{expected}}\right)^{2}}{C_{\text{ expected}}^{2}} \tag{13}\]  
We know from spectral graph theory [102] that the expected eigenvalues of a path-graph of length \(\Lambda\) should follow \(\{\lambda_{i}\}^{*}=2-2\cos(i\pi/\Lambda),\quad i=1,\dots,\Lambda-1\). Thus, we define the second penalty component as the mean squared error of the expected and observed spectrum, written as follows:  
\[P_{\text{spectral}}=\frac{1}{\Lambda}\cdot\sum_{i=1}^{\Lambda}(\lambda_{i}^{* }-\lambda_{i}) \tag{14}\]  
Averaging over batch size \(N\), we obtain the connectivity component loss, written as follows:  
\[\mathcal{L}_{\text{connectivity}}=\frac{1}{N}\sum_{n=1}^{N}\left(P_{\text{ component}}+P_{\text{spectral}}\right) \tag{15}\]  
The connectivity component ensures that the predicted graph consists of a single connected path rather than multiple disconnected segments. This is achieved using the _Laplacian matrix_, which describes the structure of the graph. The _eigenvalues_ of this matrix provide information about how connected the graph is: in particular, the number of zero eigenvalues indicates how many separate components exist. If the number of components does not match the expected value for a properly connected path, the model is penalized. Additionally, spectral graph theory tells us the expected eigenvalues for an ideal path graph, so a second penalty is added if the observed eigenvalues deviate from this expected pattern. By enforcing these constraints, the model learns to generate graphs that form a single continuous sequence without fragmentation.
## 2 Materials and Methods
### Performance Studies  
Tensile testing on the membranes was carried out with a load setting of 450 N, an extension length of 1200 mm and a stretching speed of 100 mm/min using a light-weight tensile tester, model H10 KS-0748 from Tinius Olsen. Testing Machine Company supplied by Leader Technology Scientific (M) Sdn. Bhd., Malaysia. The test was carried out in accordance with ASTM D882-10 standard. The membranes were cut into dumbbell-shaped samples with gauge length of 26 mm and neck width of 3 mm for testing. The ultimate tensile strength (UTS), E-modulus and elongation at break (EB) of the membranes were recorded from this test. For each sulfur formulation, 5 samples were tested and the average result was taken. Permeation flux test was performed at the operating pressure of 0.5 and 1.0 bar with synthetic oily wastewater by using a dead-end membrane test rig custom-made by Shxp Trading, Malaysia. Five membrane samples were used for each set of tests and the average value of permeate flux was calculated. Synthetic oily wastewater with oil content of 1000 ppm was prepared by emulsifying diesel with DI water and 0.01 wt.% of sodium dodecyl sulfate (SDS) for 30 min using a probe-type QSonica ultrasonic homogenizer model Q500 supplied by DKSH Holdings (M) Berhad, Malaysia. The oil particle size was estimated by Lighthouse liquid particle counter, model LS-20 supplied by Lighthouse Worldwide Solution (M) Sdn. Bhd., Malaysia. All droplet sizes fell within a specified range of 1.0-50 um with the majority of droplet sizes around 7 um. The permeate flux was calculated using Equation (3). The membrane used for the filtration cell was fixed to a mandatory nominal diameter of 50 mm to control the effective membrane area for permeate flux at 1.0179 \(\times\) 10\({}^{-3}\) m\({}^{2}\). The time taken to collect 30 mL of filtrate was recorded.  
\[J_{p}=\frac{V_{p}}{A\times t} \tag{3}\]  
where  
\(J_{p}\)--Permeation flux, \(\frac{\mathrm{L}}{\mathrm{m^{2}\cdot hr}}\);  
\(V_{p}\)--Permeate volume collected, \(\mathrm{L}\);  
\(A\)--Effective membrane area, m\({}^{2}\);  
\(t\)--Time taken to collect the measured volume of permeate, \(\mathrm{h}\).  
The oil rejection efficiency of the membranes was evaluated by preserving the filtrate for the chemical oxygen demand (COD) test. HACH COD vials and a HACH COD digester rack were used to digest the samples. After the digestion was completed, reading of COD was taken using HACH DR6000 UV-VIS Spectrophotometer. The oil rejection efficiency was obtained by comparing the oil content in feed synthetic oily wastewater and the filtrate COD by using Equation (4). For each set of membranes and pressures, COD test was carried out five times and average value was calculated.  
\[R=\left(1-\frac{COD_{p}}{COD_{f}}\right)\times 100\% \tag{4}\]  
where  
\(R\)--Oil rejection efficiency, %;  
\(COD_{p}\)--COD level in permeate, mg/L;  
\(COD_{f}\)--COD level in wastewater feed, mg/L.
## 3 Results and Discussion
### Fourier Transform Infrared Spectroscopy (FTIR) Analysis  
The FTIR spectra of the biochars obtained at various temperatures (CCB500, CCB600, and CCB700) are given in Figure 2a. As the pyrolysis temperature gradually increases, several peaks disappear and other are preserved with different intensities. CaCO\({}_{3}\) peaks dominate the FTIR spectra. These are evidenced by the bands around 1427, 874, and 708 cm\({}^{-1}\)[32]. A clear change in the CaCO\({}_{3}\) peak intensity can be noticed with the rise in temperature. For CCB500, a broad band at 3480 cm\({}^{-1}\) depicts the -OH and -NH stretching of acid groups and aliphatic compounds, which disappears with increasing temperature, reflecting the breakdown of -H bonds from hydroxyl and the subsequent dehydration and disintegration of aliphatic compounds at a high pyrolysis temperature [33]. A stretching vibration of -CH\({}_{2}\) is noticed at 2920 and 2856 cm\({}^{-1}\), which is still present for CCB700 at low intensity. Furthermore, the peaks of C-O functional groups are found at -1060 cm\({}^{-1}\) (in CC biomass), suggesting that oxygenated C-O groups remain in the biochar samples. With increasing temperature, a peak at 3641 cm\({}^{-1}\) of -OH groups is observed for CCB700, indicating the formation of CaO and Ca(OH)\({}_{2}\)[33]. The FTIR spectra is in good agreement with the results observed in XRF (Table 1) and XRD analyses (Figure 2b), which confirm the richness of the produced biochars in CaCO\({}_{3}\).