esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Try-On Systems in Fashion Consumption: A Systematic Review
## 3 Results and Discussion
### Antecedents and Consequences
#### 3.4.1 Influential Factors Essential to Purchase Intention and Adoption Intention
Emotional values encompass the feelings and psychological states evoked by using the technology. They cover the general emotional and psychological experiences associated with a product or service, such as the sentiments stirred by meaningful interpersonal communication, personal success, or alignment with individual beliefs [67]. Emotional values include eight variables: decision comfort, flow experience (or immersive experience), satisfaction, inspiration, perceived intrusiveness, perceived enjoyment (or perceived pleasure), perceived fantasy, and perceived playfulness. Perceived enjoyment, perceived fantasy, and perceived playfulness are closely related variables jointly known as hedonic value, which involves the beneficial emotional and experiential contentment consumers derive from using products or services, particularly regarding VTO systems [101; 102]. Utilitarian values play a pivotal role in consumer decision-making, emphasizing practicality, helpfulness, and financial advantages [103]. We recognized five key variables: perceived ease of use (or convenience or effort expectancy), perceived usefulness (or performance expectancy), product benefit, perceived esthetic quality (or vividness), and risk. In addition, previous studies examined how user characteristics influence technology adoption and fashion purchase intention, revealing twelve variables: age, gender, innovativeness, individualism, familiarity, fashion consciousness, privacy priming, technology anxiety, sensation-seeking tendency, self-monitoring, self-discrepancy, and body esteem (or body satisfaction).
## 3 Experimental Results and Analysis  
### Experimental Results of Different Proportions of Dataset Division  
Different dataset division methods and proportions can have a certain impact on model performance. The most commonly used division method is random division according to a certain proportion, such as 6:4, 7:3, and 8:2 [30]. This study used three proportions of 6:4, 7:3, and 8:2 to divide the dataset for the heat transfer coefficient prediction of the straight channel and the hexagonal channel. To ensure that both the training set and the test set contained all areas of the heat exchanger, every ten samples were regarded as a group, and the division was carried out within each group. After dividing all groups, the divided data were combined to form the training set and the test set.  
In this study, in addition to using RF, SVM, XGBOOST, and CATBOOST, four traditional machine learning models to perform the optimal segmentation ratio experiment, ANN was also used to test the optimal dataset division ratio. From the experimental results, we found that the ratio of 7:3 was dominant in most cases. Further, this study used ANN to conduct further research to explore the optimal segmentation ratio and number of neurons under different layers and neurons. The input layer of this study consisted of 13 features, the output layer was a single target variable, and the number of neurons in the hidden layer varied within the range [25; 50; 75; 150; 200], while the number of hidden layers ranged within [1; 2; 3]. Grid search combined with ten-fold cross-validation was employed to determine the optimal parameters. The experimental results are summarized in Table 2. As shown in Table 2, the model's performance fluctuates as the ratio of the training set to the test set varies. Notably, when the number of hidden layers is set to 2, the 7:3 split achieves the best performance. Under this configuration, the R\({}^{2}\) value reaches 0.888936, and both RMSE (1275.91) and MAE (123.90) exhibit relatively low errors. In contrast, the 8:2 and 6:4 splits yield slightly inferior results. Similarly, in Table 3, the 7:3 split again demonstrates superior performance when the number of hidden layers is 2, particularly in terms of the R\({}^{2}\) value, which attains 0.995199, with RMSE and MAE values of 79.016 and 36.672, respectively. These results are significantly better than those obtained with other ratios.  
Therefore, for the straight channel, the 7:3 split represents the optimal proportion. Under this configuration, the model achieves a higher R\({}^{2}\) value and lower prediction errors, suggesting that this ratio enhances the model's generalization capability while effectively mitigating issues of overfitting or underfitting. Similarly, for the hexagonal channel, the 7:3 split outperforms other ratios, yielding a higher R\({}^{2}\) value. This indicates that the division between the training set and test set significantly influences the model's learning performance and predictive accuracy.
# An Adaptive INS-Aided PLL Tracking Method for GNSS Receivers in Harsh Environments
## 5 Software Simulation
### Simulation Result Analysis
Figure 13: The choices of \(T\) (**left**) and \(B_{n}\) (**right**) when \(p=200\) m/s\({}^{3}\). In summary, the adaptive tracking method can choose the optimal noise bandwidth and integration time under the minimum tracking error criterion. By comparing with the traditional INS-aided PLL tracking method as well as the methods of adjusting the noise bandwidth, it is seen that it works better in harsh environments. The improvement of the tracking performance can be verified by software simulation.
## 3 Results and Discussion
### Adsorption and Photocatalytic Processes
Based on the present experimental results, which consider satisfactory photocatalytic degradation activity and good charge separation, the ternary heterostructure can be further tested in dynamic system applications. The main oxides present in fly ash are mulitte (2 SiO2\(\cdot\)3 Al2O3), \(\alpha\)-quartz (SiO2), hematite (\(\alpha\)-Fe2O3), and magnetite (Fe3O4), which comprise over 70% of the fly ash. The majority of oxides present in fly ash are photocatalytic inactive; fly ash is a good adsorbent in dye removal. The magnetite and hematite, together with hydrogen peroxide, act as in situ Fenton reactive, which improves the efficiency of dyes degradation. The results presented in Figures 6b and 7b demonstrate that in situ Fenton and photo-Fenton homogeneous processes, caused by the Fe\({}^{2+}\), Mn\({}^{3+}\) and Cu\({}^{2+}\), on the surface of the fly-ash-based material increase the MB degradation very quickly. Under UV irradiation, the effect of H2O2 is much stronger, and the efficiency strongly increases, suggesting these materials be recommended for wastewater remediation. Fenton is a homogeneous process that consists of ferrous, cuprous and cupric ions and hydrogen peroxide reaction that generated hydroxyl radical as in Equations (3)-(5) [77; 78]. The hydroxyl radicals degrade dye molecules, resulting in intermediates and mineralisation products as in Equation (6). \[\mathrm{Fe}^{2+}+\mathrm{H}_{2}\mathrm{O}_{2}\rightarrow\mathrm{Fe}^{3+}+ \mathrm{OH}^{-}+\mathrm{HO}^{\prime} \tag{3}\]  
\[\mathrm{Cu}^{2+}+\mathrm{H}_{2}\mathrm{O}_{2}\rightarrow\mathrm{Cu}^{+}+ \mathrm{HO}_{2}+\mathrm{H}^{+} \tag{4}\]  
\[\mathrm{Cu}^{+}+\mathrm{H}_{2}\mathrm{O}_{2}\rightarrow\mathrm{Cu}^{2+}+ \mathrm{HO}^{-} \tag{5}\]  
\[\mathrm{HO}\,+\mathrm{dyes}\rightarrow\mathrm{intermediates}\rightarrow\mathrm{ inorganics} \tag{6}\]  
The heterojunctions are used for photocatalyst due to their carrier-injecting-ratio properties, improving the light response and achieving more efficient separation of photoexcited electron-hole pairs and thus achieving a higher photocatalytic reaction rate and overall output. These hybrid materials consist of a wide range of oxides from fly ash (SiO2, Al2O3, Fe2O3), TiO2 Degussa P25, and copper sulfides (CuS, Cu2S); will take advantage of the very stable TiO2 photocatalyst; and, coupled with the VIS response of the copper sulphide (CuxS, 1 \(<\) x \(<\) 2) photocatalyst, will yield a hybrid photocatalyst which provides excellent photocatalytic performance in visible light and cost-effectiveness [79].
# Malware Classification Based on Shallow Neural Network
## 3 Classification Methodology
### An Improved n-gram Algorithm Based on Control Transfer Instructions  
The standard n-gram algorithm treats all elements in the sequence equally, but for the program, not every assembly instruction is equally important. The program execution flow is divided into many blocks by select statements, in which a sequence of statements is executed sequentially. Accordingly, the assembly code is divided into a number of basic blocks (BBLs) by the control transfer instructions. The basic block is the smallest unit of assembly codes, and many analysis processes translate the decomposing program into basic blocks as the first step [27]. A BBL is a single-entry, single-outlet instruction sequence. Considering the structural characteristics of programs, we propose a control transfer instruction-based n-gram (CTIB-n-gram) algorithm. Inspired by the concept of stop words in NLP (Natural Language Processing), we regarded the control transfer instructions in the sequences as delimiters. Only the n-gram starting with such instructions was reserved for representing the corresponding basic block, while all the other n-grams were dropped.  
Each assembly instruction n-gram should be converted to a unique vector representation and added to subsequent training. However, as the number of samples and the size of n increases, the number of n-grams increases dramatically, and it is unrealistic to retain all n-grams. At the same time, the distribution of assembly instructions of n-grams is highly sparse, and some n-grams appear very infrequently, providing little information. The CTIB-n-gram algorithm filters the n-gram frequency below the set threshold and performs feature hashing on the n-gram set. Previous research [28; 29; 30] has shown that using feature hashing in multi-classification tasks, such as malicious code classification, helps to speed up training and avoid overfitting without causing a significant loss of precision.  
The proposed CTIB-n-gram algorithm uses n-grams, starting with control transfer instructions, to express the corresponding basic blocks, then filters out the n-grams whose frequencies are lower than the threshold. Finally, the feature hash method is used to compress the set size. The n-gram set generated by the CTIB-n-gram algorithm is significantly smaller than the set obtained by the standard n-gram, which is helpful for accelerating the training and detection process. The pseudocode of the CTIB-n-gram algorithm is shown in Algorithm 2.  
```
Input: opcode sequence, frequency threshold, number of hash buckets \(v\) Output: n-gram set V
1. temp_set = []
2. forn-gram in sequence:
3. ifn-gram starts with []MP, []NZ, LOOP,...]
4. temp_set_append(n-gram)
5. end if
6. end for
7. forn-gram in temp_set:
8. ifn-gram.frequency() < threshold:
9. drop n-gram
10. end if
11. end for
12. hash temp_set to v buckets
13. \(V\leftarrow\) buckets return V
```  
**Algorithm 2** The proposed CTIB-n-gram algorithm
## 3 Proposed Method  
### Input Data Preprocessing  
The network input uses local blocks from the original fingermark image after preprocessing. This approach avoids introducing incorrect information into the network, which could result in the loss of discriminative information required for effective network training. Signed fingermarks' local blocks are preferred over those with rich textures to ensure network training effectiveness. Signed fingermark preprocessing involves foreground extraction, local block extraction, and image enhancement. The foreground region is extracted from the original fingermark image file using the color space method. An average color threshold per unit area is used to identify the effective area of the fingermark, filtering out interference items that may be similar in color to the fingermark (e.g., stamps).  
The center of gravity of the foreground region, which represents the original image of the fingermark, is calculated and positioned as the central point. A sliding window with a fixed size of p \(\times\) p (p = 300 pixels) is then used to intercept the local blocks by sliding in steps of 50 pixels, as illustrated in Figure 1.  
To ensure the continuity and consistency of strokes, a larger window size should be selected for signed fingermarks, as they have complex stroke lines. This will avoid limiting  
Figure 1: Local blocks and image enhancement with a sliding window, which shows a fingermark with Chinese signature.  
the characteristics of stroke lines and reduce the risk of accuracy reduction in liveness detection. It is important to ensure that the training data contains sufficient fingermark information and does not have a large number of blank areas. The accuracy of signed fingermark liveness detection is significantly reduced when the size of the localized blocks is too small or too large. To increase diversity and enrich the data, the intercepted local blocks are augmented with random-direction mirror flipping and random-angle rotation (rotation angles of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)) before being sent to the network for training. To enhance network efficiency, we convert local blocks to single-channel grayscale maps without color information. We then perform contrast enhancement based on the grayscale histogram, using the formula given in Equation (1).  
\[\text{F(s)}=\text{round}(\frac{\text{cdf(s)}-\text{cdf}_{\text{min}}}{\text{( w\times h)}-\text{cdf}_{\text{min}}})\times 255 \tag{1}\]  
where w and h refer to the width and height of the image, respectively; cdf is the cumulative distribution function; s is the original gray value; and round represents the rounding function.  
Enhancement of the image contrast is performed using grayscale stretching to increase the grayscale difference between the texture of the fingermark and the blank background and to reduce the effect of some of the fingermarks not being visible in the curve of the line due to too light a press.
## 2 Related Works
The system's performance was evaluated in terms of HOR and CDR, but other KPIs were not considered in their evaluation. The maximum speed for the UE was set at 50 km/h. In reference [19], the authors proposed an algorithm based on FLC that uses RSRP, RSRQ, and UE speed to dynamically determine HOM in an LTE HetNet. The algorithm's effectiveness was assessed by employing KPIs such as HOR, RLF, and HOPP. The speed of the UE was taken into account within the range of 0 km/h to 80 km/h. Additionally, the impact of various inputs on FLC was studied individually, including HOR, HOF, HOPP, and the average time spent by the UE inside an SC. In [20], an HO algorithm that uses FLC has been introduced. This algorithm dynamically determines the HOM by considering both the current received SINR and the expected future SINR values for the UE in a 5G network. The authors claim that future SINR values can be predicted based on the SINR variations in the previous 200 milliseconds (ms). The network performance was evaluated in terms of RLF and HOPP. The UE speed was set at 3 km/h for indoor scenarios and 30 km/h for outdoor scenarios. Moreover, the authors in [21] presented a technique that combines FLC with weight functions to adjust TTT and HOM settings in an LTE/5G HetNet. The FLC takes inputs from the SINR, cell load, and UE speed functions. The system's performance was evaluated using metrics such as RLF, HOPP, and HOF. The study considered a maximum UE speed of 160 km/h. In both [22; 23], the authors introduced the same method that uses FLC in the context of 5G and beyond networks. This method uses inputs such as RSRP, RSRQ, and UE speed to estimate the TTT and HOM. The primary aim of this approach, as mentioned in [22], is to mitigate HOR and HOPP issues. However, the specific FLC rules for estimating TTT and HOM are not provided explicitly in the study. However, the authors have detailed these FLC rules in their later work, along with additional KPIs such as HOF, HOL, and InT. The maximum UE speed considered in their studies was 160 km/h. In [24], the authors introduced an algorithm in an LTE/5G environment that is similar to the methodologies discussed in references [14; 15]. It builds upon the approaches presented in [16; 21]. The newly proposed algorithm incorporates FLC into the weight functions, allowing adaptive adjustments to the TTT and HOM levels. The evaluation of system performance included metrics such as HOPP, RLF, HOL, and outage probability. The maximum UE speed considered was 160 km/h. The authors in [25] introduced an HO decision method that uses FLC in an LTE/5G ultra-dense network environment. This method adjusts the TTT and HOM dynamically on the basis of the SINR and the speed of the UE. The performance of this method was evaluated using metrics such as HOR, HOPP, and overall system throughput. The maximum speed considered for the UE was 72 km/h.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
###### Abstract
The main contributors to the high cost are the data access cost from storage locations and the processing cost while executing the user specified query for a dominance check. It is important to note that search efficiency and update criteria are two key requirements of skyline query processing and skyline result maintenance. Data-Centric Storage (DCS) [12; 13], an alternate to External Storage (ES) and Local Storage (LS), is considered to be a promising and efficient storage and search mechanism. There has been a growing interest in understanding and optimizing WSN DCS schemes in recent years, where a various number of query mechanisms such as point query, range query, similarity searching, top-k query and skyline query can be applied in a consolidated framework. As part of developing such consolidated framework, this paper proposes a threshold based hierarchical approach, which uses temporal correlation among the sectors and sector segments to calculate DS in a region of interest. To our knowledge, this is the first practical demonstration of DS in DCS of WSN in current state-of-the-art. In this paper, first a novel tree building algorithm is incorporated so that each head node of a cluster could construct a tree with itself as a root. This allows exertion of three core operations such as _Tree Propagation_, _Regular Update_ and _Triggered Query_ from any head node with a particular range. The performance of the proposed model is analyzed by framing and implementing it in a distributed information delivery service running one or more applications in a WSN. In this service, a set of producer and consumer nodes exchange information by relaying packets through neighboring clusters for each application. This phenomenon is facilitated by a DCS [14] architecture, also referred to as DBDCS (Section 3.1). DBDCS is an adaptation of the magnetic disk storage platter consisting of tracks and sectors [15; 16]. In DBDCS, each application uses a disc track and sector analogy to map data locations and uses distance based indexing method for storing and querying multi-dimensional similar data. The member nodes in each cluster report the sensed event to their associated head node, which aggregates the received events at the end of each epoch. The aggregated event is hashed to produce a hash key, which is mapped from a one dimensional domain into a metric space [17] (Section 3.3). The remainder of this paper is structured as follows: Section 2 provides an overview of the related work in the literature. Network architecture, data processing and mapping, insertion and skyline querying are illustrated in Section 3. This is followed by the simulation results and performance evaluation of EDDS presented in Section 4. The paper is concluded in Section 5.
## 4 Results and Discussion
The indicators \(A.\), \(D.\), and \(I.\) are used to denote the structure differences between the learned and the original network, namely, the number of arcs accidentally added (\(A.\)), deleted (\(D.\)), and inverted (\(I.\)), compared with the original network. From the experimental results, it can be seen that, using the cooperative evolution strategies based on DE, the proposed coACO can greatly improve the performance of ACO in solving the BN structure-learning problem. As to the accuracy of the algorithms, coACO is superior to ACO for all test cases. This improvement is valid for not only BIC scores (see Table 2) but also the iteration number and the structural differences (see Table 3). As to the efficiency, the evolution curves shown in Figures 4-10 show that coACO requires fewer computing iterations than the basic ACO. As to the robustness, coACO is also superior to the basic ACO, which can be deduced from the standard deviations of the independent runs shown in Tables 2 and 3. The pheromone plays an important role in the solution-constructing process. The DE operations improve the capability of ants to accumulate the level of pheromone on arcs in the best structure. Due to the cooperative evolution characteristics, coACO can adjust the distribution of the pheromone trail to the best state in just a few iterations, and thus, yield the optimal network structure much faster. Though coACO employs some extra operators, it converged much faster. Therefore, coACO can obtain the best solution in a given small number of iterations. Figure 10: Evolution curves of the average BIC score achieved by ACO and coACO on the ALARM dataset with \(n=5000\). Figure 9: Evolution curves of the average BIC score achieved by ACO and coACO on the ALARM dataset with \(n=1000\)