esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Computed Tomography (CT)  
CT is a widely used medical imaging modality that provides detailed anatomical information. It is commonly used in the diagnosis and treatment planning of various diseases, such as cancer and cardiovascular diseases. DL techniques have been applied to various tasks in CT, including image segmentation, disease detection, and diagnosis.
## 4 Framework Dimensions and Measurement
### Adaptive Recovery Index (A)  
The Adaptive Recovery Index (A) quantifies a system's capability to restore functionality post-disruption, evaluating the interplay between the diversity and effectiveness of adaptive strategies and the need for rapid restoration. It offers a dynamic, time-sensitive measure of recovery performance, balancing strategic adaptability with temporal efficiency to mitigate downtime.  
Theoretically, \(A(t)\) is rooted in systems recovery dynamics and diminishing returns, expressed as follows:  
\[A(t)=A_{\text{max}}\cdot\frac{\ln(1+\omega\cdot N_{\text{adapt}})}{\ln(1+ \omega\cdot N_{\text{max}})}\cdot\frac{1}{1+\tau\cdot t_{\text{recovery}}} \tag{6}\]  
This formulation balances two principles: (1) recovery effectiveness grows sublinearly with the number of adaptive strategies (\(N_{\text{adapt}}\) out of a theoretical maximum \(N_{\text{max}}\)) due to saturation, modeled by the logarithmic term with coefficient \(\omega=0.1\) (derived from empirical recovery drill data showing diminishing returns); and (2) recovery latency (\(t_{\text{recovery}}\)) imposes a hyperbolic penalty (calibrated by \(\tau=0.05\) per second to reflect escalating downtime costs), reflecting the disproportionate impact of delays. The multiplicative structure ensures that neither adaptability nor speed alone dominates, reflecting real-world trade-offs.  
Practically, \(A(t)\) is operationalized through measurable indicators for adaptability scope and recovery latency, using mature technologies. Adaptability scope is measured by Recovery Strategy Coverage (\(N_{\text{adapt}}/N_{\text{max}}\)), using data from disaster recovery plans or tools like Ansible Tower or ServiceNow. Recovery latency is assessed via Actual Recovery Time against Recovery Time Objectives (RTO), sourced from drill logs or monitoring platforms (e.g., SolarWinds, Datadog). Supporting indicators include Backup Restoration Success Rate (from backup solutions like Veeam or Acronis) and Recovery Verification Compliance (via audit logs or ticketing systems like Jira). Data collection leverages existing infrastructure (backup systems, monitoring dashboards, ITSM platforms) with methodological flexibility (e.g., Commvault instead of Veeam). Aggregation involves collecting raw data, normalizing it (e.g., \(t_{\text{recovery}}\) inverted), and computing A(t) with theoretical weights, ensuring practical applicability while balancing strategy diversity and recovery speed.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 6 Methodology
### Classification  
This stage involves performing the classification of the dataset before and after removing the outliers (reducing the data dimentionality) using three classifiers, k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest (RF) with ten-fold cross-validation. The reason behind the choice of these classifiers is that k-NN is the simplest classifier that is recommended for small datasets [46], while SVM and RN are well known for their efficiency in tackling large datasets [46,47].
# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
###### Abstract
Fortunately, we are inspired by translations in  
Figure 1: Intuitiveillustration of a sentiment-enhanced word embedding. Vectors with different colors represent that they belong to different vector spaces. the embedding space (TransE) [19] and contrastive learning [20,21]. TransE is a knowledge graph embedding method. It shortens the distance between two related entities and increases the distance between two unrelated entities in vector space. Meanwhile, contrastive learning narrows the distance between positive examples and increases the distance between negative examples in vector space. The bootstrap your own latent (BYOL) [20] minimizes the distance between two similar images; the supervised contrastive pre-training (SCAPT) [21] used supervised contrastive learning to cluster explicit and implicit positive sentiments, cluster explicit and implicit negative sentiments, and separated these two clusters. By borrowing these ideas, we propose a novel sentiment-enhanced word embedding (S-EWE) method to improve the performances of sentiment classification models. Specifically, we convert words in the sentiment lexicon to word embeddings and arrange each word embedding for a sentiment mapping vector. Then, we add original word embeddings and their sentiment mapping vectors to obtain the sentiment-enhanced word embeddings with the information of word and sentiment. Thirdly, we adopt one fully connected layer to reduce the dimensions of sentiment-enhanced word embedding to get the predicted sentiment orientations. Finally, we calculate the loss of the predicted and true sentiment orientations by the cross-entropy function. We further train the sentiment mapping vectors through backpropagating the loss so that the trained sentiment mapping vectors can find the mapping relationship between words in sentiment lexicons and their sentiment orientations. Under different sentiment classification models, we confirmed the effectiveness of the S-EWE method. The main contributions are as follows:  
1. A new sentiment-enhanced word embedding method is proposed. This method establishes the mapping relationship between words and their sentiment orientations by vector addition. 2. Abundant experiments were performed on TextCNN [22], TextRNN [23], TextRCNN [24], TextBiRCNN [24], an attention-based bidirectional CNN-RNN deep model (ABCDM) [25], and CNN-LSTM [26] using Word2 Vector [1]; and GloVe [2] with or without sentiment-enhanced word embeddings on the semantic evaluation 2013 (SemEval-2013) dataset [27] and the Stanford sentiment treebank (SST-2) dataset [28]. Experimental results show that sentiment-enhanced word embeddings can improve the ability of these models' sentiment classification. The subsequent parts of this paper are organized as follows. Section 2 introduces the related work. The sentiment-enhanced word embedding method is shown in Section 3. Datasets, empirical results, and model analysis are presented in Section 4. Finally, we summarize our paper and put forward a future outlook in Section 5.
## 5 Experiments
### Experimental Evaluation
#### 5.3.2 Random Environments
The only configuration, where one method clearly outperforms the other two is 5b, where the LSAP method scores significantly better than both STTF and SMP. For the other configurations we see no significantly best performance. In particular, related to a high vehicle utilisation and a low P/T ratio (3b, 5b), the STTF method performs significantly worse than the other methods. A similar pattern can be observed with configuration 1b, but here the difference is significant only between STTF and LSAP. For most configurations with high vehicle utilisation and a high P/T ratio however (2b, 4b), no clear difference could be obtained between the methods. As an exception, we obtain a small but significant difference with configuration 6b, where STTF performs worse than the other two. Most configurations with low vehicle utilisation show no clear outliers in terms of throughput performance (1a, 3a, 5a, 6a). We see significant differences only between the respective best and worst performances with configurations 2a and 4a. Here, STTF performs significantly better than only SMP (2a), and significantly worse than only LSAP (4a). For all configurations, we observe that the STTF method generates significantly more traffic in general. Consequently, STTF also scores worse with the efficiency-related performance measures, with a lower travel efficiency and a higher throughput effort. For most configurations, this difference is significant. The only exceptions to this are configurations 1b and 2b, where the difference is only significant between STTF and LSAP. The performance of the LSAP method regarding the efficiency-related measures are generally better than or equal to that of SMP. The SMP method scores best with the total travelled distance metric, which seems to be correlated with a lower throughput rate and, thus, does not lead to better relative efficiency scores. We notice no clear difference to the above described observations with the pseudo-random layout configuration (that is, the parking positions, as well as input and output storage are systematically located near each other). Only for the configuration 5b, with low P/T ratio and high vehicle utilisation, we observe a significantly superior performance by the LSAP method across all metrics but the travelled distance, and a significantly inferior performance by the STTF method for all performance metrics. Here too, in general, we observe a clear benefit in terms of efficiency with LSAP and SMP when compared to STTF. For the other configurations (5a, 6a, 6b), no clear difference was observed between the former two methods.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 3 Overview of Fault Detection and Diagnosis (FDD) in DHC Systems
### Fault Detection in Substations and Customer Facilities
#### 3.2.4 Malfunction in Heat Pump Components  
Heat pumps (HPs) at the customer-sited substations are used in 4 GDH systems as booster stations for DHW production, whereas in 5 GDHC systems, they are needed to supply both space heating/cooling and DHW loads at the right temperature for the distribution and emission system. Due to that, it is really important to assure an efficient operation detecting possible faults and to prevent them. A comprehensive study that analyzes the most important faults reported by both original equipment manufacturers (OEMs) and insurance companies in Sweden is presented in [122]. The results state that the issues in control and electronics are one of the most common and costliest faults in all types of HPs. According to OEMs, the shuttle and shunt valves are the second most common faults that occurred in ground source and exhaust air HP systems, respectively. Unfortunately, there are not any investigations in the current literature about fault detection applied to these specific parts. Moreover, an additional impediment for the development and implementation of the HPs fault detection algorithm is the fact that the equipped software is often a closed system that can only be accessed by the manufacturer. However, there exist some studies leading to detect leaky check and reversing valves using their own test benches or modified commercial heating pumps to be able to measure internal temperatures and mass flow of the refrigerant [123].  
It is proved that for HP systems, there is a maximum coefficient of performance (COP) at the optimal charge amount and refrigerant leakages cause performance degradation and a decrease in thermal comfort [124]. However, several heat-pump-based units usually do not have an optimal amount of refrigerant. For this reason, it is important to detect these leakages in an early phase to be able to fix them without energy losses and to limit their contribution to global warming. Several studies have been developed in this direction. For instance, [PERSON] et al. (2019) [125] proposed a novel refrigerant charge fault detection strategy for HPs using convolutional neural networks trained using a real commercial HP system and the variables used for internal control provided by the manufacturer. [PERSON] et al. (2020) [126] defined the sub-health operation concept of HP systems, which is used to define the intermediate state between normal and fault. Moreover, an online undercharge sub-health diagnosis method was proposed that analyses the theoretical behavior of the system facing a refrigerant leakage.  
Even though it seems that every time more algorithms used for fault detection are based on neural networks and machine learning techniques, a recent study concludes that some of these approaches are not useful when working with real data series from heat pumps [95].
# Improved Direct Linear Transformation for Parameter Decoupling in Camera Calibration
## 3 Improved Direct Linear Transformation
### Physical Experiment  
We used a light-emitting diode (LED) as the space point fixed on a coordinate measurement machine, as shown in Figure 8. The size of the plane was 0.7 m \(\times\) 0.7 m, which contained a pattern of \(8\times 8\) points. There were 20 planes; the data for three of these planes were used for one DLT calculation. The DLT results are shown in Table 1. The fluctuations in the Euler angle errors are less than 0.06\({}^{\circ}\), and they are not very volatile. The most volatile values are \(t_{y}\) and \(v_{0}\). The fluctuation in \(t_{y}\) is less than 28.03 mm, and the fluctuation in \(v_{0}\) is less than 2.59 pixels. The results of the IDLT algorithm are shown in Table 2, where the calibration parameter values are different from those of DLT.  
Figure 7: Calibration error analysis between DLT and IDLT with noise and distortion: (**a**) error of \(u_{0}\); (**b**) error of \(v_{0}\); (**c**) error of \(t_{x}\); (**d**) error of \(t_{y}\); (**e**) error of \(a_{x}\); (**f**) error of \(t_{z}\) based on \(a_{x}\); (**g**) error of \(a_{y}\); (**h**) error of \(t_{z}\) based on \(a_{y}\).  
To compare the calibration accuracy of the two algorithms, the results of IDLT and the first set of DLT data were used to calculate the re-projection errors of 20 planes. Each plane image error was described by the mean plus three times the standard deviation of the image point re-projection errors. The results are shown in Figure 9. The calculation errors of DLT increase with the number of planes. This shows that the DLT calibration results are optimal for some planes, but not for all planes. The calibration errors of IDLT decrease with the number of planes. The reason is because the distance between the image point and optical center point of the image is small, and the effect of distortion is small.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \(a_{x}\) & \(a_{y}\) & \(u_{0}\) (pixel) & \(v_{0}\) (pixel) & \(t_{x}\) (mm) & \(t_{y}\) (mm) & \(t_{y}\) (mm) & \(T_{z}\) (mm) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: IDLT results of the physical experiment.  
Figure 8: Photograph of the physical experiment.
## 2 Signal Analysis and Neural Network
### Hilbert-Huang Transform (HHT)
#### 2.1.1 Intrinsic Mode Functions  
For any function, if the following two conditions are met, it can be called the IMF:  
1. The sum of the number of local maximum and local minima must be the same as or different from the number of zero-crossings, which means that an extreme value must have a zero-crossing point behind it.
2. At any time, the upper envelope defined by the local maximum and the lower envelope defined by the local minimum must be averaged to approach zero.
## 5 Implementation  
As noted above, convolution in general can be programmed employing time-domain and/or frequency-domain operations. The trade-off between the two methods are latency between input and output, and efficiency of computation. With an implementation solely using time-domain processing, which we call _direct_ convolution, there is no extra latency imposed by the operation, but we have \(O(N^{2})\) complexity. If we implement it in the spectral domain, we can use a radix-2 FFT and reduce the computation demands to \(O(N\log N)\), but since we need to wait for all of the \(N\) samples of input to be received, we will impose a latency between input and output. This can be compensated for in offline processing but not in real time. However, this latency can be reduced by moving some of the spectral operations to the time domain and reducing the transform size to a fraction of the filter size. This approach is known as _partitioned_ convolution. These ideas are discussed in the remainder of this section.
## 3 Results and Discussion
### Suggestions to Management and Policy Makers  
Although there are some uncertainties, the results of this study can still provide valuable reference information for water management and ecosystem management in the study area and other regions.  
A most important issue in sustainable land use is how to maintain the functioning of ecosystem processes and to ensure thereby the provision of ecosystem services, and it is necessary to enhance the water use efficiency to leave enough water in rivers to sustain ecosystems and meet the growing demands of cities and industries [11,52]. The results of this study, including the spatial patterns of the WUE-SC, potential soil loss, soil conservation amount and rate, can provide valuable reference information for the land use management and water management to improve the water use efficiency and balance the water availability and the provision of the soil conservation service. For example, the soil conservation service is highly location-dependent since it is influenced by the potential soil loss and the vegetation coverage rate, both of which generally show obvious spatial heterogeneity, and the results of this study show that there is high spatiotemporal inconsistency between the soil conservation rate and the potential soil loss, which may lead to the low overall soil conservation amount (Figure 2). All these results can provide significant reference information for increasing the provision of the soil conservation service. Besides, the high spatiotemporal inconsistency between potential soil loss and ET may lead to low WUE-SC. The high vegetation coverage rate can lead to a high soil conservation rate and high ET, but it does not always lead to a high soil conservation amount or high WUE-SC. The potential ability of the vegetation to reduce wind erosion can only be fulfilled in the regions with high potential soil loss. Therefore, it is very necessary to increase the soil conservation amount and improve WUE-SC by carrying out management measures such as adjustments to the spatial pattern of vegetation and water diversion, which can also be used to manipulate the balance of ecosystem services at the landscape scale [53]. In addition, natural resource management programs and land management decision making should consider the complex nature of trade-offs between ecosystem services [53,54]. It is especially necessary for local and regional ecosystem managers and policy makers to consider the balance between the water availability and provision of ecosystem services and coordinate trade-offs between different ecosystem services, the provision of which competes for increasingly scarce water resources [54,55].
## 2 Description of the PV System
For the first stage of the microinverter, the state variables are the magnetizing current (\(i_{m}\)) and the PV voltage (\(v_{pv}\)), while the leakage current (\(i_{k}\)) is equal to the diode current, which can be expressed in terms of \(i_{m}\) and the converter control signal \(u\):  
\[\frac{d_{in}}{dt}=\frac{v_{pv}\cdot u}{L_{m}}-\frac{v_{o}\cdot(1- u)}{n\cdot L_{m}+L_{k}/n} \tag{1}\] \[\frac{dv_{pv}}{dt}=\frac{i_{pv}-i_{m}\cdot u}{C_{pv}}\] (2) \[i_{k}=\frac{i_{m}\cdot(1-u)}{n} \tag{3}\]  
The previous equations are based on the binary control signal \(u\); thus, those differential equations describe both the system dynamics and the switching ripple caused by sequential activation (\(u=1\)) and deactivation (\(u=0\)) of the MOSFET and the complementary deactivation and activation of the diode. A simplification of the previous discontinuous model is performed by averaging the control signal \(u\) within the switching period \(T_{sw}\), which results in the duty cycle of the converter \(d\) given in (4); performing the same procedure to the discontinuous differential equations results in the averaged model of the system given in (5)-(7). \[d=\frac{1}{T_{sw}}\cdot\int_{0}^{T_{sw}}u\ dt \tag{4}\] \[\frac{di_{in}}{dt}=\frac{v_{pv}\cdot d}{L_{m}}-\frac{v_{o}\cdot( 1-d)}{n\cdot L_{m}+L_{k}/n}\] (5) \[\frac{dv_{pv}}{dt}=\frac{i_{pv}-i_{m}\cdot d}{C_{pv}}\] (6) \[i_{k}=\frac{i_{m}\cdot(1-d)}{n} \tag{7}\]  
Finally, the stable relations between the electrical variables of the circuit are obtained by considering the averaged differential equations equal to zero, as follows:  
\[i_{pv}=i_{m}\cdot d \tag{8}\] Figure 1:0 (9) Figure 1:1  
It is worth noting that the transfer function of the magnetizing current with respect to the duty cycle, extracted from the averaged systems in (5) and (6), has a right-hand-zero(RHZ) that is difficult to control using classical control techniques. Therefore, this paper proposes an SMC to impose both global stability and accurate reference tracking.
# A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings
###### Abstract  
Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands.  
prognostics and health management (PHM); remaining useful life (RUL); feature engineering; gear; bearing; Fisher's discriminant ratio (FDR); \(I_{3}\) value; Matlab; tutorial +
Footnote †: journal: Applied Sciences  
_J. Phys. Chem._  
_Applied_  
components is of crucial importance as well. While there have been many studies in this area, their concern was mostly on new algorithm development including the deep learning approaches recently gaining popularity [11; 12]. Relatively less attention was given to the educational aspects for beginners to implement this work by themselves. Motivated by this lack of attention, this paper presents a tutorial for feature engineering for gears and bearings.  
Recently, authors have published a series of tutorial papers for other subjects with the same objective: Aid engineers to gain better understanding of PHM and implement it by the codes. The first was Particle Filter algorithm with the example of battery degradation and crack growth problems to demonstrate the model-based prognosis, which is the last step of PHM [4]. Second was advanced signal processing using public datasets of gears and bearings, which is the very first step of PHM [13]. This paper is another tutorial effort, as a continuation from the signal processing. As in the previous tutorial, public datasets released from other institutions are used as well as those made by the authors. Matlab codes are provided in Appendix A for engineers to implement the feature engineering process by their own hands.