esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Calculation of Cost Savings by Incorporating Forecasting Error Cost
Experimental results show that cost increases are more effective as the constant value increases and the order quantity increases (Table 5). In addition, as the confidence interval of 2D KDE becomes larger, the cost-saving effect is reduced. As a whole, since the constant value has exceeded a certain level, the readjusted result is consistently meaningful, and the maximum savings of KRW 9,715,207 have occurred. On the other hand, according to the constant value, the cost-saving effect did not appear in less than a certain level. As a result, the smaller the order quantity, the larger the exponential increase in the predicted cost per unit. Finally, we confirm that the result of the rebalancing for cost reduction is realistic. According to the 64 order datasets used in the curve fitting step of the order cost per unit, the quantity ordered at one time ranges from a minimum of 111 to a maximum of 10,560. Using the Gaussian kernel estimation technique, we calculated the probability distribution of the order quantity. As a result, the quantity ordered at one time was more than 101 at 99% probability, more than 8453 at 95% probability, and more than 837 at 90% probability (Table 6). According to the above experiment and Figure 11, the cost savings were continuously shown when the constants in # 2 and # 3 exceeded the order volume in 2010 and 2015, respectively. At this time, the minimum number of orders used in the formula is 70 and 113, respectively. In reality, the probability of such a small order is less than 2%. Therefore, it can be concluded that the proposed method can record meaningful results with a probability of 98.87% or more in reality. \begin{table}
\begin{tabular}{l l l l l l} \hline
**  
As a result, it can be confirmed that the re-adjusted prediction result reflecting the prediction error cost is more effective in cost reduction than the general LSTM prediction model.
## 3 Experiment and Result Analysis
### Influence of Spindle Angular Speed on Twisting Tension  
In order to investigate the influence of the rotation speed of the balloon on the tension of the balloon yarn, the twisting tension of the balloon yarn was tested under different spindle angular velocities. For the convenience of measurement, the tension between the twister and the overfeeding roller is measured as the twisting tension of the balloon yarn. The measurement principle of twisting tension is shown in Figure 5.  
The configuration of the twisting assembly during the test were: the diameter of the twisting ray was 220 mm, the diameter of the yarn storage tray was 120 mm, and the diameter of spindle can was 266 mm. When using the tension meter DTMB-5000 SCHMID for the tension test of the direct-twisting machine, in order to reduce the random measurement error as much as possible, five tension measurement values were read under a certain measurement condition, and the average value was taken as the tension measurement value under the measurement condition. For the fineness of the twisted yarn was 1440 dtex, the measurement results of twisting yarn tension at different spindle speeds are shown in Table 1. The standard deviation and the overall standard deviation coefficient of measurement data in each group are calculated. The results show that the standard deviation of tension measurement values in each group fluctuates in the range of 3 cN-5 cN, 1 cN = 0.01 N, and the overall standard deviation coefficient is in the range of 0.22-0.27%, indicating that the method of measuring the twisting tension in this paper is feasible and the measurement results are accurate. Meanwhile, in the subsequent tension measurement tests in this paper, the above method was used to obtain the tension measurement values.  
The experiment's data show that the twisting tension had obvious regularity, and it increased with the increase of the spindle angular velocity. To establish the relationship between twisting tension \(P_{\omega}\) and spindle angular velocity \(\omega\), on the basis of experimental measurement data, a polynomial fitting method was used for fitting, and the relationship equation was obtained as shown in Equation (4). The tension-spindle angular velocity data distribution and fitting curve measured by the experiment are shown in Figure 6. The unit of \(P_{\omega}\) is cN, the unit of \(\omega\) is rad/s.  
\[P_{\omega}=0.00467\omega^{2}-5.72722\omega+3032.47 \tag{4}\]  
Figure 5: Principle of balloon yarn tension measurement.
## 3 Results
### Limitations  
In this study, we did not consider the influence of reservoirs and other water storage options, which is the main limitation of this study. The authors of [26] studied long-term changes in annual water discharge and found that it is mainly controlled by precipitation variation. However, the construction of reservoirs/dams has had little influence on water discharge in the Zhujiang River Basin. The authors of [63] concluded that the impact of reservoirs should be considered in simulation of runoff. The projections of the SWAT model of hydrological components for the future scenarios were based on the use of the same LULC and soil properties as exist at present (LULC ESA-2005, in this study). This assumption may have led to underestimation of the actual evapotranspiration in the future periods [23]. Some hydropower and irrigation projects exist, which were also not included in this study, which may have altered the hydrology of the study area. Therefore, considering these limitations in the current research work, they should be accounted for in the projected results for the future planning of water resources.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 4 Modified Identity-Based Encryption Scheme
### Modified IBE Construction
Let \(\mathbf{A}_{id}^{\prime}=[\mathbf{u}_{id}\parallel\mathbf{A}_{id}]\in\mathbb{ Z}_{Q}^{n\times m^{\prime}}\) and output the private key corresponding to each user \(id\) as \(\mathbf{sk}_{id}=(-1,\mathbf{t}_{id})\in\mathbb{Z}_{Q}^{m^{\prime}}\), satisfying \(\mathbf{A}_{id}^{\prime}\mathbf{sk}_{id}=-\mathbf{e}_{app}\)_mod_\(Q\). * \(\mathbf{IBE.Enc}(MPK,id,msg)\): Input \(MPK\), the user identity \(id\) and a message \(msg\in\{0,1\}\) to be encrypted. Let the vector \(\bar{\mathbf{v}}=\left(msg\frac{Q}{2},0,\ldots,0\right)\in\mathbb{Z}_{Q}^{m^{ \prime}\times 1}\). Uniformly select a vector \(\mathbf{y}\xrightarrow{\bar{\mathbf{y}}}\left\{0,1\right\}^{n\times 1}\), and uniformly select an error vector \(\mathbf{e}\xleftarrow{\xi}\mathbf{x}_{\overline{\mathbf{v}}_{\overline{ \mathbf{v}}_{\overline{\mathbf{v}}_{\overline{\mathbf{v}}_{\overline{\mathbf{v} }}}}}}^{\prime}\times 1}\), such that \(\left\|\mathbf{e}\right\|<B_{\chi}\). Output the ciphertext vector \[\mathbf{c}_{id}=\mathbf{A}_{id}^{\prime T}\mathbf{y}+\bar{\mathbf{v}}+\mathbf{ e}\in\mathbb{Z}_{Q}^{m^{\prime}\times 1}.\]
* \(\mathbf{IBE.Dec}(MPK,\mathbf{sk}_{id},\mathbf{c}_{id})\): Input \(MPK\), the private key \(\mathbf{sk}_{id}\) under identity \(id\), and the ciphertext \(\mathbf{c}_{id}\) under identity \(id\). Set \(msg^{\prime}=\mathbf{sk}_{id}^{T}\cdot c_{id}\in\mathbb{Z}_{Q}\), Calculate \[\mathbf{sk}_{id}^{T}\cdot\mathbf{c}_{id} =\mathbf{sk}_{id}^{T}\left(\mathbf{A}_{id}^{\prime T}\mathbf{y}+ \bar{\mathbf{v}}+\mathbf{e}\right)\] \[=\mathbf{sk}_{id}^{T}\mathbf{A}_{id}^{\prime T}\mathbf{y}+\left< \mathbf{sk}_{id}^{T},\bar{\mathbf{v}}\right>+\left<\mathbf{sk}_{id}^{T}, \mathbf{e}\right>\] \[=-\mathbf{e}_{app}\mathbf{y}+msg\left|\frac{Q}{2}\right|+\left< \mathbf{sk}_{id}^{T},\mathbf{e}\right>\] If \(\left|\left|msg^{\prime}-\left|\frac{Q}{2}\right|\right|\right|<\left|\frac{Q }{4}\right|\), let \(msg=1\); if \(\left|\left|msg^{\prime}\right|\right|<\left|\frac{Q}{4}\right|\), let \(msg=0\). Output \(msg=1\).
# Computational Complexity and ILP Models for Pattern Problems in the Logical Analysis of Data
###### Abstract
Other problems arising from the analysis of patterns are related to understanding whether two different sets of rules actually explain the same data set, or, in other words, the two pattern sets are equivalent. In particular we would like also to know whether a given set of rules explains all possible data, and so is in some sense \"useless\". On the opposite side we would like to know whether there are some data that cannot be explained by a particular set of rules. In addition, given a set of patterns we would like to know whether there exists another smaller set of patterns that explains the same data set. This problem that we call _Pattern Equivalence Minimality_ looks similar to Pattern Cover Minimality. The difference is that here we start from a pattern set and not from a data set. Though patterns can be expanded into strings and we might solve a Pattern Cover Minimality problem from these strings, it is obviously computationally intractable expanding the patterns. Hence we should be able to find a better set of patterns starting directly from the given pattern set. In the following we will review the computational complexity of these pattern problems, which are, in general, quite complex [12] (see also [13] for a fixed-parameter analysis of some related pattern problems). We then give an integer linear programming (ILP) formulation for Pattern Cover Minimality and for Pattern Equivalence and we address the effectiveness of our formulations by means of extensive computational experiments. An ILP formulation for Pattern Cover Minimality is also given in [PERSON] et al. [14]. The formulation we propose in this paper reduces the problem to a Set Covering with a (lowdegree) polynomial number of columns. Pattern Cover models for non-binary data are, e.g., a branch-and-price procedure described in [15] and heuristic procedures proposed in [16]. Formulating a solution procedure for Pattern Equivalence Minimality seems quite challenging since, as we will prove in the paper, this problem is NP-hard and co-NP-hard at the same time. The paper is organized as follows. In Section 2 we provide precise mathematical definitions of the concept we are dealing with and the related problems. In Section 3 we investigate the computational complexity of the problems defined in the previous section. In Section 4 we investigate strings and patterns that are mutually compatible. In particular, we provide a polynomial algorithm to list all patterns that are compatible with a given set of strings (the inverse problem of listing all strings compatible with a set of patterns is necessarily exponential). In Section 5 we give ILP models both for the Pattern Cover Minimality and Pattern Equivalence problems. These procedures are tested in Section 6 devoted to the computational experiments. Some conclusions are drawn in Section 7.
## 3 Methodology
### Performance Evaluation Methods  
The quantitative evaluation of each of the six models relies on parameters such as accuracy, sensitivity (or recall), specificity (precision), and positive predictive value (PPV). The accuracy (1), specificity (2), sensitivity (3), and F-score (4) are calculated using samples of true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) cases. Recall, also known as sensitivity, refers to the model's capacity to accurately detect and classify all relevant instances in a specific dataset. Dividing the overall number of positive outcomes yields the true positive to false negative ratio. The research showcases the capacity to correctly identify individuals diagnosed with the condition. In the field of medical diagnostics, diseases are typically classified as positive. Disregarding this advantageous classification may result in adverse outcomes, such as delays in providing medical attention due to an incorrect diagnosis. Diagnosing medical images requires a high level of recall or sensitivity. Positive predictive value (PPV), also known as specificity, is a measure that quantifies the proportion of positive examples that are correctly identified as positive. The quantity of genuine positives can be properly determined by dividing the PPV by the sum of true positives and false positives. Medical imaging necessitates a considerable degree of accuracy. The F1 score is derived by utilizing the sensitivity and specificity of each model. The F1 score, accuracy, specificity, and sensitivity can be computed using the following formulas:  
\[Accuracy=\frac{\boldsymbol{TP}+\boldsymbol{TN}}{\boldsymbol{TP}+\boldsymbol{ TN}+\boldsymbol{FP}+\boldsymbol{FN}} \tag{2}\]  
\[Specificity=\frac{\boldsymbol{T}_{\boldsymbol{N}}}{\boldsymbol{T}_{\boldsymbol{ N}}+\boldsymbol{F}_{\boldsymbol{p}}} \tag{3}\]  
\[Precision=\frac{\boldsymbol{T}_{\boldsymbol{p}}}{\boldsymbol{T}_{\boldsymbol{ p}}+\boldsymbol{F}_{\boldsymbol{p}}} \tag{4}\]  
\[Sensitivity=\frac{\boldsymbol{T}_{\boldsymbol{p}}}{\boldsymbol{T}_{\boldsymbol{ p}}+\boldsymbol{FN}} \tag{5}\]  
\[F_{1}-Score=2\times\frac{precision\times Sensitivity}{Precision+Sensitivity} \tag{6}\]
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
## 3 Dataset  
The dataset we used to validate our approach is UNSW-NB15, which was created by researchers at the University of South Wales in Australia. This dataset was produced by the IXIA PerfectStorm where raw packets were gathered for common attacks such as fuzzers, analysis, backdoors, and denial-of-service (DoS). We selected this dataset primarily due to its extensive use in research studies, serving as a standard benchmark for comparing various network intrusion detection systems. Additionally, it includes a time feature, enabling the conversion of the problem into a time series context.  
The UNSW-NB15 dataset contains approximately 2.5 million samples of network packets and 49 corresponding labels (48 features and 1 label). These features can be viewed in Table 1. From these features, we employ 44 after the pre-processing steps. The features include basic information such as source and destination IP addresses, as well as more advanced features such as the packet size, time to live, and protocol type. This dataset also contains time-based features, namely the starting and ending times of a packet, which helps us sort and convert the samples to a time series format. Concerning the labels (binary), the dataset consists of 2,218,760 benign packets and 321,283 malicious. The mean number of packets involved in the interactions between specific entities (IPs) in this dataset is 8167. Also, the mean time between two consecutive packets is 1 second.
## 2 Materials and Methods
### Learning Phase
#### 2.3.2 Feature Selection and Establishment of Classifiers
For simplicity, we considered the following notation: \[C^{1}=OD,\,C^{2}=MA,\,C^{3}=EX,\,C^{4}=HE,\,C^{5}=NR\] (8)  
Calculation of Feature Weights and Establishment of Classifiers  
In the Feature Selection task, a set of efficient features was selected for each class \(C^{i}\), \(i=1,\,2,\,3\), 4 (e.g., excluding _NR_). Then, for each class \(C^{i}\) and selected feature \(F^{i}\), from the corresponding _CM_ a weight \(w_{ij}\) is calculated using Equation (9) and a local classifier \(D_{ij}\) (11) is considered and associated with \(w_{ij}\). \[w_{ij}=\frac{C^{i}/C^{i}}{C^{i}a} \tag{9}\]  
A local classifier \(D_{ij}\) for the class \(C^{i}\) and the feature \(F^{i}\) is implemented in the following way. First, a confidence interval \(C^{i}_{i}\) is created (Equation (10)), where \(F^{i}_{i}\) min and \(F^{i}_{i}\) max represent, respectively, the minimum and the maximum of the values \(F^{i}\) for the class \(C^{i}\) obtained from the  
Figure 5: Confusion matrix (_CM_) for one feature \(F\). \(LS1\) set. If \(F^{j}=LBPH\), then a confidence band for the histogram is considered. The classifier \(D_{ij}\) is given in Equation (11), where \(B\) is the box to be classified. \[C{I}_{i}^{j}=\left[F_{i}^{j}\text{min};F_{i}^{j}\text{max}\right] \tag{10}\]  
Based on local classifiers \(D_{ij}\) for each class \(C^{i}\), a global classifier \(D_{i}\) is developed (Equation (12)). Thus, by considering the local classifier \(D_{ij}\), an intelligent processing network for classification predicts that the patch \(B\) belongs to class \(C^{i}\), \(i=1,\,2,\,3,\,4\), if the associated weighting vote \(D_{i}(B)\) (Equation (12)) is maximum and higher than \(0.7\times\sum\limits_{j=1}^{s}w_{ij}\), where \(s\) is the number of features associated with the class \(C^{i}\). If not, then \(B\) belongs to the class \(C^{5}=NR\). The threshold \(0.7\) was experimentally chosen. This can be considered as classification process based on a voting scheme with a threshold. \[D_{ij}(B)=\left\{ \begin{array}{ll}w_{ij},&\quad\text{if}\quad B\in C{I}_{i}^ {j}\\ 0,&\quad\text{else}\end{array} \right. \tag{11}\]  
\[D_{i}(B)=\sum\limits_{j=1}^{s}D_{ij}(B) \tag{12}\]  
Taking into account the selection criteria, in the learning phase, Step 2, for each RoI a dedicated classifier is built: \(D_{1}\) for the \(OD\) class, \(D_{2}\) for the \(MA\) class, \(D_{3}\) for the \(EX\) class, and \(D_{4}\) for the \(HE\) class. These classifiers are visible in the operating phase (Figure 6). So, each classifier has specific features (Table 5): \(D_{1}\)--\(ImG\), \(ConG\), \(FDG\), and \(LBPH\); \(D_{2}\)--\(ConG\), \(EnG\), \(LG\), and \(LB\); \(D_{3}\)--\(ImG\), \(ConG\), \(EnG\), \(HomG\), and \(FDG\); \(D_{4}\)--\(ImR\), \(FDR\), \(LB\), and \(LR\).
## 4 Experiments
### Evaluation Metrics  
This study addresses two main tasks: DST and end-to-end dialogue generation, which includes both DST and response. To evaluate the DST performance of the AraConv model, we used the joint goal accuracy (JGA) metric to compare the predicted dialogue state to the ground truth for each dialogue turn. If all predicted slot values exactly match the ground-truth values, the model's output is considered correct. To evaluate the performance on the end-to-end generation task by the AraConv model, we used four metrics:  
Figure 2: The multi-lingual Seq2 Seq model workflow.  
the BLEU metric to assess the generated response fluency;
* the API call accuracy (API\({}_{\text{Acc}}\)) metric to assess if the system generates the correct API call;
* the task success rate (TSR) metric to assess whether the system finds the correct entity and provides all of the requested information for a particular task. TSR can be defined as \[\text{TSR}=\frac{\sum\text{ success task}}{\text{total number of tasks}}\] (4) where the tasks involve searching task and booking task for hotel and restaurant domains, and search task for attraction and weather domains.
* the dialogue success rate (DSR) metric to evaluate whether the system accomplishes all of the dialogue tasks. DSR can be defined as \[\text{DSR}=\frac{\sum\text{ success dialogue}}{\text{total number of dialogues}}\] (5)  
The evaluation method's main goal is to obtain an automated and repeatable evaluation procedure that enables efficient comparisons of the quality of different dialogue strategies. This involves focusing on the automatic evaluation metrics. However, further measurement of the quality of the generated responses also requires human review. Thus, following the literature [15], we evaluated the AraConv model's performance on end-to-end generation tasks according to two metrics:  
* the language understanding score to indicate the extent to which the system understands user inputs; and
* the response appropriateness score to indicate whether the response is appropriate and human-like.  
We performed a small-scale human review to measure these scores. The literature indicates two other common metrics used in human evaluation: TSR and DSR [56]. Given the costs and time-intensiveness of human evaluation, we measured these scores automatically (TSR and DSR).
## 4 Discussion  
This paper introduces a deep Res-CNN architecture incorporating skip connection layers for segmentation and characterization of hyperbolas in ROI-binarized hyperbolic region extraction. The aim is to expedite the identification of underground target reflection regions and enhance the interpretation of GPR imaging.  
The primary finding of the study is that networks incorporating skip connection layers facilitate improved transmission of network information, resulting in clearer and more distinct extraction of hyperbolic morphology regions in GPR images compared to structures lacking skip connections. In comparative analysis, the Res-CNN outperformed conventional segmentation methods, showcasing superior efficiency and accuracy in segmenting hyperbolic ROIs in GPR images.  
However, it is essential to acknowledge several intrinsic and extrinsic limitations of the current study. Primarily, the training dataset mainly comprises forward simulation data and may not fully capture the complexity of underground medium conditions. Therefore, enhancing the performance of the network requires the incorporation of models with dispersive or anisotropic media and the utilization of high-quality field data for training. Additionally, the model's performance may be affected by parameter selection, which requires verification and optimization of different parameters to ensure robustness under different scenarios. Furthermore, exploring advanced techniques for noise reduction and feature extraction could further enhance the model in challenging real-word conditions.  
The proposed model demonstrates the potential applicability of deep neural networks in segmenting and characterizing ROI-binarized hyperbolic regions in GPR images. Future research can further explore the model performance to leverage capabilities in deep learning, thereby enhancing the efficiency of GPR interpretation under different conditions.
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)
#### 3.1.3 SAR  
SAR sensors measure the Normalized Radar Cross Section (NRCS) variation from the wind-roughened ocean surface as a function of both OSW speed and direction. Accordingly, GMTs are developed to establish the relationship between OSW vector information, calibrated NRCS, and sensor viewing angles for different SAR bands and polarizations [96]. However, since SAR sensors operate with a single azimuth view, it is required to infer whether backscatter variations are related to OSW speed (along-view OSW vector component) or OSW direction (across-view OSW vector component) [81]. To resolve this issue, several different methods have been implemented, a comprehensive list of which are discussed in [97]. It is also worth noting that rain can negatively affect the OSW estimation accuracy from SAR data, and rain contamination is more noticeable at shorter wavelengths [81].  
SAR systems are the only satellite-based system that can provide OSW information with a subkilometer spatial resolution (between a few meters and 100 m) [96]. However, OSW retrieval using SAR data is generally implemented at spatial resolutions of 0.5-1 km for removing speckle noise as well as filtering ocean waves and other non-wind-inducedfeatures [81]. SAR systems are efficient at both low and high winds with subkilometer spatial resolutions [98]. The cross-polarized SAR data provide a very weak incidence and azimuth angle dependency, with no indication of saturation for the strongest wind speeds (up to 75 m/s). Accordingly, these capacities have opened new perspectives for OSW speed estimation in different settings, even in tropical cyclones [98; 99; 100]. In this regard, [PERSON] et al. [100] analyzed 83 SAR images, captured by Radarsat-1 and Envisat from 2001 to 2010, to investigate the morphology of the tropical cyclone eye in terms of shape and size distribution. The detailed atmospheric phenomena generated within tropical cyclones (e.g., rainbands, boundary layer rolls, and arc clouds) were also explored. This study demonstrated the application of SAR data for investigating the characteristics of windstorms with relatively higher resolution. In fact, by analyzing 30 typhoons and 43 hurricanes, the authors reported that the response of ocean surface to the storm-forced winds could be effectively explored by SAR data.