esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Study
#### 4.4.3 Ablation of Loss Function  
To validate that the two mechanisms associated with losses in the semi-supervised branch contribute to improving the model's functionality, ablation tests were carried out on the loss functions. The outcomes of the ablation experiments are shown in Table 3. In Table 3, Q1 indicates the application of only the mechanism of depletion \(L_{GMM}\) in the unsupervised branch, Q2 indicates the use of only the KL divergence loss \(D_{KL}\) in the unsupervised branch, and Q3 represents the total loss function used in the unsupervised branch of the proposed method, which combines both \(L_{GMM}\) and \(D_{KL}\). As shown in Table 3, both loss functions contribute to enhancing the deraining performance to varying degrees.  
In summary, by using these loss functions, the network can not only utilize labeled synthetic data for supervised training, but also express the distribution of real data in the unsupervised portion. Compared to existing algorithms, the method proposed in this chapter demonstrates better generalization and practicality.
###### Abstract
Thirdly, realize dynamic adjustment and real-time feedback, and realize real-time feedback and dynamic adjustment through incremental learning technology and the self-attention mechanism of the model to ensure that the recommended content always matches the latest learning progress and personal needs of students [19]. Finally, increase the diversity and innovation of the recommended content, enrich the diversity of recommended paths with the help of the GAN, and encourage students to try more diverse learning tasks [20]. In order to achieve the above goals, this study proposes a personalized learning path recommendation system based on GPT-4. The system firstly collects and preprocesses data, cleans it up and normalizes it, then extracts and fuses features, extracts frequency features of time-series data by Fourier transform, and fuses multimodal data by the weighted summation method. Then, using the generated pre-training model and its training process, combined with the self-attention mechanism of GPT-4, the long-term dependence in the input sequence is captured and the personalized learning path is generated. Finally, innovation is introduced through incremental learning technology and the GAN to ensure that the learning path is dynamically adjusted according to the real-time feedback of students. This paper mainly consists of six parts. The first part introduces the research background, challenges, objectives, methods, and significance of this study. The second part introduces the related literature research results. The third part describes the methods used in detail, including data collection, data preprocessing, feature extraction and fusion, generating a pre-training model and its training process based on GPT-4, generating the method of the personalized learning path, and real-time feedback and the dynamic adjustment mechanism. The fourth part shows the recommendation effect of the system and analyzesthe performance of different models for recommendation accuracy, coverage, and recall rate through comparative experiments, which verifies the advantages of the system proposed in this study. The fifth part discusses the specific application of the method proposed in this study, as well as the limitations of this study and future solutions. Finally, in the conclusion, the main contributions of the research results are summarized. Through the application of large-scale pre-training model GPT-4, this study not only provides an innovative and effective solution for personalized learning path recommendations in the second classroom, but also lays a foundation for further exploring the application of AI technology in the field of education.
# Utilizing Blockchain for IoT Privacy through Enhanced ECIES with Secure Hash Function
###### Abstract  
Blockchain technology has been widely advocated for security and privacy in IoT systems. However, a major impediment to its successful implementation is the lack of privacy protection regarding user access policy while accessing personal data in the IoT system. This work aims to preserve the privacy of user access policy by protecting the confidentiality and authenticity of the transmitted message while obtaining the necessary consents for data access. We consider a Modified Elliptic Curve Integrated Encryption Scheme (ECIES) to improve the security strength of the transmitted message. A secure hash function is used in conjunction with a key derivation function to modify the encryption procedure, which enhances the efficiency of the encryption and decryption by generating multiple secure keys through one master key. The proposed solution eliminates user-dependent variables by including transaction generation and verification in the calculation of computation time, resulting in increased system reliability. In comparison to previously established work, the security of the transmitted message is improved through a reduction of more than 12% in the correlation coefficient between the constructed request transaction and encrypted transaction, coupled with a decrease of up to 7% in computation time.  
Internet of Things; blockchain; ECIES; secure hash function; privacy; reliability +
Footnote †: journal: Future Internet
# Photocatalytic Cementitious Material for Eco-Efficient Construction--A Systematic Literature Review
## 4 Discussion  
### Influence of Using Cementitious Materials as Catalyst Supporting Media on Photocatalytic Activity  
As discussed earlier, the immobilization of TiO\({}_{2}\) on solid support, such as cementitious materials, greatly enhances its use in practical applications. However, it may reduce the photocatalytic efficiency due to the reduction in active surface and mass-transfer limitations. For TiO\({}_{2}\) immobilized systems, the accessible active surface area is a key element affecting the efficiency of photocatalytic activity. The accessible active surface area depends on the amount of TiO\({}_{2}\) in the hardened cement and the pore structure of cementitious materials. The factors controlling the accessible active surface area should be well understood before developing an efficient photocatalytic process.
# Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide
## 3 Results and Discussion  
### Open Circuit Potential Measurement  
Figure 1 shows the time dependence of the open circuit potential (\(E_{\text{OC}}\)) for AISI 316L in seawater at different experimental conditions (different temperatures and different S\({}^{2-}\) concentrations).  
The \(E_{\text{OC}}\) is a result of the electrochemical reactions that take place at the stainless steel/seawater interface. As can be seen, the \(E_{\text{OC}}\) value depends strongly on the experimental conditions: the composition of the stainless steel, the properties of the seawater (temperature and presence of S\({}^{2-}\) ions), and the immersion time. Immediately after immersion in seawater at 288 K, AISI 316L steel reaches a potential of \(\approx\)\(-\)45 mV, which becomes more positive with time (due to the formation and thickening of the protective passive film on the electrode surface) [38,39], and after about 10 min a more or less stable value of \(\approx\)\(-\)15 mV is established (Figure 1a). As the temperature of the seawater increases, the final \(E_{\text{OC}}\) value becomes more negative.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Ions** & **Cl\({}^{-}\)** & **Na\({}^{+}\)** & **SO\({}_{4}\)\({}^{-}\)** & **Mg\({}^{2+}\)** & **Ca\({}^{2+}\)** & **K\({}^{+}\)** & **Br\({}^{-}\)** & **Sr\({}^{2+}\)** & **F\({}^{-}\)** \\ \hline mg L\({}^{-1}\) & 21846 & 12168 & 2958 & 1409 & 453 & 439 & 74 & 9 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: The approximate chemical composition of seawater (main ions).  
It can also be observed that the presence of S\({}^{2-}\) ions (as seawater pollution) cause a significant shift of the potential toward more negative values (Figure 1b). Thus, it can be seen that an increase in concentration from 10 to 40 ppm of S\({}^{2-}\) ions in seawater at 298 K shifts the \(E_{\mathrm{OC}}\) up to 150 mV toward more negative values, from \(\approx\)\(-\)60 mV to \(\approx\)\(-\)200 mV.  
Similar results are obtained for AISI 304L steel. With increasing seawater temperature in the range of 288-308 K, the final \(E_{\mathrm{OC}}\) value decreases from \(-\)100 mV to \(-\)170 mV. The presence of sulfide ions also changes the \(E_{\mathrm{OC}}\) value on the cathodic side from \(-\)120 mV (0 ppm S\({}^{2-}\), 298 K) to \(-\)310 mV (40 ppm, 298 K).
## 2 Related Studies: State of the Art about Analytics Tools on the Market
### IBM Watson Analytics  
IBM Watson Analytics [13] is a cloud based system that allows the final user to run complex analytics using a simple interface, using nothing but a web browser (no specific clients or plug-ins to be installed on local machines). The goal is allowing users, experts who may be familiar with data analytics techniques or not, to focus only on their experiment or case study.  
Once the database is uploaded on the cloud, the system offers three categories of functions: Explore, Predict and Assemble (Figure 1). The \"Explore\" mode provides data clustering to detect patterns and intrinsic relationships between data (non-supervised training techniques). The \"Predict\" mode allows the user to perform predictions on the data, disclosing the predictive strength of the most significant parameters, compared to a single target parameter set up by the user. \"Assemble\" mode is dedicated to efficiently show data using infographics.  
The fields of application of such general purpose systems are very large; there is a Watson Analytics Community where users can share use cases as samples of application in various areas [14].  
For example, the system is used by a human resource manager to identify the parameters that affect the workers resignations [15]. One more example is the analysis of product sales near particular events, such as fireworks near July 4 th in US [16]. Other use cases include banking, insurance, retail, telecommunications, government, nonprofit, education, marketing, sales, information technology, finance and more.  
One of the strengths of Watson Analytics, is the automation of many steps of the analysis, allowing also non expert users to start using it. Main automation functionalities can be summarized as:  
* Automatic Data Preparation
* Data Transformation
* Data Quality Index, based on empty fields analysis and constant values identification
* Automatic Modeling
* Auto selection of best models and detection of strongest relationships: Decision Tree (CHAID) and Key Driver
* Auto selection of best predictive statistical method basing on data type: Watson Analytics automatically chooses the best regression model for the user data between linear, logistic, multivariate etc.  
Figure 1: Watson Analytics (WA) home screen showing three modalities: Explore, Predict and Assemble.  
In addition, Watson Analytics includes an engine for text cognitive analysis (IBM is the world leader); the user can submit a question typed in natural language to extract information from data.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
## 2 System Model
### User Distribution and Mobility  
A new user mobility model is developed for facilitating evaluation of our proposed network with MEC. The conventional hotspot model defined by 3 GPP [35] merely specifies a ratio of the number of hotspot and non-hotspot users within the macro cell and, thus, does not take user mobility into account. To circumvent that issue, this paper extends the  
Figure 1: mmWave Edge Cloud system architecture.  
3 GPP hotspot model via introducing a proper mobility model. More specifically, there are several hotspots in the target macro cell, and users move at a constant speed within the macro cell partitioned into grids of regular distance. The user movement algorithm is as follows:  
1. The user selects one hotspot area randomly as a destination when entering the target macro cell.
2. The user moves to the destination and stays there for a certain time.
3. After staying, the user goes outside of the macro cell.
4. The user enters the macro cell as a new user, and then leaves the macro cell repeatedly until the end of the evaluation time.  
As explained in the fourth step, this algorithm is repeated. Figure 2 shows the actual movement of one user in this simulation. The user moves from the initial position (circle) to the end position (square). The destination (star) in this figure is the final destination, which changes depending on the state of whether the user goes to the hotspot area or outside of the macro cell. The user stays at a nomadic position (triangle). Table 2 shows the parameters of our mobility model. Figure 3 shows the spatial distribution of users. The color bar in the figure represents the probabilistic density, and the totalization of these values within and out of hotspot areas will provide exactly the preferred hotspot ratio as defined by 3 GPP.
## 3 Materials and Methods
### Transfer Learning (TL)
#### 3.3.1 ImageNet ILSVRC Models  
The seven pre-trained ImageNet ILSVRC models considered in this research comprised two DenseNets (DenseNet-169 and DenseNet-201) [18]; InceptionResNet V2 [19]; three ResNets (ResNet-50, ResNet-101, and ResNet-152) V2 [20]; and VGG-16 [21]. Figure 4 represents a typical example of transfer learning for the seven pre-trained ImageNet ILSVRC models.  
Figure 4: Architecture of VGG-16 model classifying a neutrophil.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cell Type** & **DS-1** & **DS-2** & **DS-3** \\ \hline Neutrophbits & 400 & 1000 & 2750 \\ Eosinophils & 400 & 1000 & 2750 \\ Lymphocytes & 400 & 1000 & 2750 \\ Monocytes & 400 & 1000 & 2750 \\ Total number & 1600 & 4000 & 11,000 \\ Training & 140 & 3600 & 9900 \\ Validation & 160 & 400 & 1100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: New balanced BCCD datasets: DS-4, DS-5, and DS-6.  
In Figure 4, the transfer learning process of the VGG-16 model is depicted. The weights of all layers, excluding the classifier (MLP Head), were held constant (\"frozen\"). The parameters of these layers in the feature extractor were designated as non-trainable. The only parameters subject to training during the fitting process were those associated with the classifier (MLP Head), revealing the new four-class WBC output layer.
## 3 Results
Focus for scenarios included: (1) BVLOS landing/takeoff operations (these were conducted as Enhanced VLOS operations, as we always had a safety spotter in place at the remote locations); (2) complex flight geometries in close proximity, including altitude stratified operations for CON1/2/4/5 scenarios; (3) field operations implementing flexible, deployable communications, and networking systems; (4) scenarios incorporating dynamic airspace replanning (e.g., emergency operations supporting various CON1/2/4/5 scenarios); and (5) scenarios designed to demonstrate the utility of increased visual/audio indications made available within the USS web client and UAS GCS displays. * Environmental challenges: Most mission profiles planned for the TCL3 campaign were flown either as originally scheduled or with slight modifications-necessitated by weather conditions or to provide better flight value supporting NASA objectives. Not every mission set could be accomplished with the entire UTM system in concert as planned due to a combination of factors, including UAS/system capabilities, connectivity issues of the new UAF \"wheel and spoke\" communications architecture, late winter breakup, and bad weather (winds, rain, and snow). * Multiple UAS: UAF performed numerous mission sets, which included flights by multiple simultaneous UAS (up to five, but generally 3-4). These missions incorporated several airspace constructs, including combinations of altitude-stratified local and EVLOS operations. The missions utilized several launch/recovery locations and demonstrated both fully centralized C2 of flight operations utilizing a single UTM operator for all UAS, as well as two sets of USS operations providing simultaneous centralized C2 over respective UAS operations (CON4). * UTM architecture: As with previous campaigns, UAF employed a centralized approach to the UTM system, generally using a single team member as interface to the UTM system. While UAF was not selected for operations involving interactions between competing USS systems, the team still successfully demonstrated this capability utilizing two separate instances of the Simulyze USS. * UTM information to flight crews: While UAF primarily utilized a centralized UTM system for all flight crews, we did increase the amount of interaction between crews and the USS Web Client. Over time the team gradually introduced the UTM system displays into the crews' equipment and incorporated this into their mission planning and cross-check during mission execution. Not only did the flight crews demonstrate the ability to adopt this new tool, but Simulyze was able to inject much of the valuable airspace volume information and third-party UAS information (via ADS-B and the MESA-SSR radar) directly into the flight crew's organic Mission Planner GCS, keeping the information confined to a single display and in a more familiar (and safer) format.
## 3 Method
### Anomaly Explanation  
For the explanation of anomalies, this paper compares the number of anomalies that may occur on each dimension of the time series data \(T\) by calculating the anomaly scores' matrix and threshold _threshold_ (if an anomaly score is greater than the threshold, it indicates an anomaly and is assigned a corresponding position in \(Y\) with a value of 1; otherwise, it is assigned a value of 0) and locates the measurement from the largest possible anomaly to determine whether the number of anomalies is the most significant.  
\[\lfloor(S_{(i,j)}-threshold)/threshold\rfloor+1=R_{(i,j)} \tag{12}\]  
\(\lfloor\ \rfloor\) means floor. Count the number of ones in each dimension in \(R\) and obtain the number of the first two dimensions with the most significant number.  
It is summarized in Algorithm 2 as the testing phase and anomaly explanation.  
```
Initialization: \(\hat{T}\) used for \(\tilde{W}\)  Trained \(Encoder\), \(Decoder1\), and \(Decoder2\)  for\(t=1\) to \(T^{\prime}\) \(\tilde{R}_{1}\gets ED_{1}(\tilde{W}_{t})\), \(\tilde{R}_{2}^{\prime}\gets ED_{2}(\tilde{R}_{1}^{\prime})\) \(s=a||\tilde{W}-\tilde{R}_{1}||_{2}+\beta||\tilde{W}-\tilde{R}_{2}^{\prime}||_ {2}\) \(y_{i}=1\) (\(s_{i}\geq\text{POT}(s_{i})\)) \(y=\vee\,y_{i}\) \(\lfloor\left(S_{(i,j)}-threshold\right)/threshold\rfloor+1=R_{(i,j)}\)
```  
**Algorithm 2** CAT-IADEF testing algorithm.  
In Algorithm 2, line 1 takes the data for testing \(T\), and lines 2 and 3 calculate the anomaly score \(s\). Lines 4 to 5 judge whether there is an anomaly based on the anomaly score. As long as there is an anomaly in any dimension, we will treat the timestamp as an anomaly. Finally, line 6 compares the anomaly score of each data point with the threshold, stores the generated label sequence \(Y\) in the matrix \(R\), and then judges the two dimensions with the most anomalies.
# Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
## 2 Materials and Methods
### Custom Loss Function Cost
```
1:\(y_{forecast}\): Sales forecasts, \(y_{actual}\): Actual sales
2:\(total\_loss\): Accumulated loss
3: CPEI \(\leftarrow\) 2.0 \(\triangleright\) Cost Per Excess Inventory
4:\(\text{CPIS}\leftarrow\) 6.0 \(\triangleright\) Cost Per Inventory Shortage
5:\(\text{inventory}\leftarrow\) 0 \(\triangleright\) Initial inventory
6:\(\text{total\_loss}\gets\) 0 \(\triangleright\) Initial loss
7:\(\text{diff}\leftarrow\)\(y_{forecast}-y_{actual}\)\(\triangleright\) Differences among forecasts and actual values
8:for each value in diff do
9:\(\text{value}\leftarrow\text{inventory}+\text{round}(\text{value})\)
10:if value \(>\) 0 then
11:\(\text{loss}\leftarrow\text{value}\times\text{CPEI}\)
12:\(\text{total\_loss}\leftarrow\text{total\_loss}+\text{loss}\)
13:\(\text{inventory}\leftarrow\text{value}\)
14:\(\text{elseif value }<\) 0 then
15:\(\text{value}\leftarrow\text{value}\times-1\)
16:\(\text{inventory}\leftarrow\text{0}\)
17:\(\text{loss}\leftarrow\text{value}\times\text{CPIS}\)
18:\(\text{total\_loss}\leftarrow\text{total\_loss}+\text{loss}\)
19:endif
20:endfor
21:return total_loss
```  
**Algorithm 1** Custom loss function cost
## 3 Results and Discussion
### Ni\({}^{2+}\) Adsorption on Sand and Soil Colloids
#### 3.2.2 Ionic Strength Effect  
In the presence of soil colloids, it was found (see Figure 3b) that the concentration of Ni\({}^{2+}\) in the solution increased when the IS increased from 0 to 30 mM, indicating that Ni\({}^{2+}\) adsorption by soil colloids decreased as the IS increased. Increasing IS can make the zeta potential of soil colloids more positive, which may be harmful to positively-charged Ni\({}^{2+}\) adsorption by soil colloids [38; 39]. There is a strong cationic bridging effect between clay colloids and metal cations, which can explain the decrease of the Ni\({}^{2+}\) adsorption by the soil colloids with the increased IS [32]. The metal cations were adsorbed through an ion-exchange reaction to form outer-sphere complexes; these reactive sorption sites can be defined as variable charge sites. On the contrary, it was said that if the metal ion adsorption on the soil particles increased or had no change with the IS increasing, these reactive sorption sites can be treated as permanent charge sites, which generally have difficulty exchanging with other cations in the aqueous phase [40]. It was reported that the increase of ionic strength inhibited the transport and release of clay colloids in porous media [41; 42], affecting the adsorption behavior of heavy metals in groundwater. Moreover, Na\({}^{+}\) in the solution increased with the increased IS (which may reduce the Ni\({}^{2+}\) adsorption through competitive adsorption for the reactive sites on the soil colloids). However, in the absence of soil colloids, it was found that the concentration of Ni\({}^{2+}\) in the solution decreased when the IS increased from 3 to 30 mM, indicating that Ni\({}^{2+}\) adsorption by sand increased as the IS increased; this may be attributed to the increasing Na\({}^{+}\) concentration increasing the attraction between Ni\({}^{2+}\) and the surface of sand.  
Figure 3: Effect of pH and ion strength (IS) on the adsorption of Ni\({}^{2+}\) on the soil colloids and quartz sand (**a**) pH effect; (**b**) IS effect. The initial concentration of Ni\({}^{2+}\) was 0.6628 mg\(\cdot\)L\({}^{-1}\), different ISs were adjusted with NaNO\({}_{3}\), the concentration of soil colloids was 220 mg\(\cdot\)L\({}^{-1}\), different pHs were adjusted with 0.1 M HNO\({}_{3}\) or 0.1 M NaOH, the equilibrium time was 24 h.  
In addition, it was obvious that the Ni\({}^{2+}\) adsorption was much larger on the surface of soil colloids than that on the surface of sand when the IS was 0 or 3 mM. However, the Ni\({}^{2+}\) adsorption was slightly larger on the surface of sand than that on the surface of soil colloids when the IS was 30 mM. These results indicate that high IS has significantly negative and positive effects on Ni\({}^{2+}\) adsorption by soil colloids and by sand, respectively.