esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 6 Conclusions  
In this paper, we introduce a new deep fusion network that offers a comprehensive approach to predicting global maintenance projects. To enhance our understanding of the relationships between various maintenance projects, we introduce a correlation representation framework utilizing the maintenance project co-occurrence matrix. Building upon this correlation learning, we propose a deep fusion network that integrates the attention mechanism to synthesize vehicle mileage and vehicle base information. By conducting extensive experiments with actual vehicle data, we demonstrate the effectiveness and robustness of our model.  
In summary, our model accounts for the interplay between vehicle maintenance projects, mileage, and vehicle base information. Furthermore, the strong interrelation between vehicle maintenance projects, vehicle breakdowns, and vehicle parts enables the seamless application of our model to forecast breakdowns and parts requirements. However, the correlation calculation is based solely on the co-occurrence frequency between maintenance projects, overlooking the correlation embedded in the textual information of maintenance projects. In addition, the co-occurrence can suggest correlation but does not necessarily imply causality or a meaningful relationship for prediction. In future studies, we intend to delve deeper into the correlations of maintenance projects and consider integrating explicit vehicle maintenance technology information into the prediction task.  
Figure 7: Correlation of frequency and attention weights for each group of projections. (**a**) Correlation of frequency and attention weights for Group I; (**b**) Correlation of frequency and attention weights for Group II; (**c**) Correlation of frequency and attention weights for Group III; (**d**) Correlation of frequency and attention weights for Group IV.  
This approach is expected to enhance the interpretability of our model and promote further advancements in the field.  
[PERSON] contributed to the data analysis, algorithm construction, and writing and editing of the manuscript. [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. [PERSON] proposed the idea, contributed to the data acquisition, performed supervision and project administration, and reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author due to privacy.  
On behalf of all authors, the corresponding author states that there are no conflicts of interest.
## 2 Background
### Ternary Addressable PUF (TAPUF)  
A TAPUF or ternary PUF is a variation of a PUF that maps unstable responses to a third state X [18; 19]. These responses, known as trits, allow for the flagging of unstable bits,so they are not used for authentication purposes or key generation. TAPUFs can lower the bit error rate (BER) by filtering out unstable cells. They can also increase the security of cryptographic protocols obfuscating the process [20; 21]. Additionally, TAPUFs can leverage their ternary states for true random number generation protocols.  
A TAPUF has certain limitations that need to be considered. One drawback is that it requires additional computational power to generate challenges, as the system needs to analyze and classify a larger number of responses. Additionally, it demands more bandwidth to transmit the responses since not all are utilized. Moreover, a mechanism for identifying and communicating unstable responses to the PUF must also be established.
# A Hybrid Strategy Improved SPEA2 Algorithm for Multi-Objective Web Service Composition
## 3 Background and Problem Formulation
### QoS Evaluation  
\(Q_{o}S_{i}=\left\{T_{i},C_{i},A_{i},R_{i},SIM_{i},MT_{i}\right\}\) is a set of service functional attributes of \(S\). The attributes \(T_{i},C_{i},A_{i}\)_and_\(R_{i}\) refer to the response time, cost, availability, and reliability of service \(S\), respectively. \(MT\) and \(SIM\) are matching type and semantic similarity. For the calculation of \(MT\), the formula is as follows:  
\[MT=\left\{\begin{array}{ll}1&\text{ exact match}\\ 0.75&\text{ plugin}\\ 0.5&\text{ subsumes}\\ 0.25&\text{ fail}\end{array}\right. \tag{2}\]  
For the calculation of \(MT\) between the ontology of current services \(a\) and given services \(b\), we follow the definition in [37]. Exact match means \(a\) equals or is a subclass of \(b\). Plugin means \(a\) is a \"subconcept\" of \(b\). Subsum means \(a\) is a \"superconcept\" of \(b\). The rest of the situations are defined as failures. Note that there is a deviation from the definition of \"subconcept\" and \"superconcept\" as presented in [37]. We focus solely on the layer difference between two layers to prevent excessive performance losses. Also, \(MT\) is not set to 0 in a fail situation. This is connected to our QoS treatment of the overall link, which is elaborated on in detail later. For the calculation of \(SIM\), we refer to [38]. The method is described as follows:  
\[SIM=\frac{2N}{N_{a}+N_{b}} \tag{3}\]  
where \(N\) represents the distance from the nearest common parent of nodes \(a\) and \(b\) on the semantic tree to the root node, \(N_{a}\) is the distance from \(a\) to the root node, and \(N_{b}\) is the distance from \(b\) to the root node.  
Due to variations in the average value and magnitude of different \(QoS\) attributes, a unified normalization process was implemented for these attributes. For those positive \(QoS\) attributes, the normalized formula was adopted as follows:  
\[Q_{i}=\begin{cases}\frac{Q_{i}-Q_{\min}}{Q_{\max}-Q_{\min}}&Q_{\max}-Q_{\min} \
eq 0\\ 1&Q_{\max}-Q_{\min}=0\end{cases} \tag{4}\]  
For those passive \(QoS\) attributes, the normalized formula was adopted as follows:  
\[Q_{i}=\begin{cases}\frac{Q_{\max}-Q_{i}}{Q_{\max}-Q_{\min}}&Q_{\max}-Q_{\min} \
eq 0\\ 1&Q_{\max}-Q_{\min}=0\end{cases} \tag{5}\]  
The \(QoS\) of the composition result was calculated through the \(QoS\) of all the services on the link. The combination structure of entire link was classified as sequential, parallel, and choice. Table 1 shows the formulas of \(QoS\) attributes in different combination structures, where \(w_{1},\ldots,w_{d}\) with \(\sum_{k=1}^{d}\ w_{k}=1\) denotes the probabilities of the different options of the choice.
# Study on the Causes and Control Measures of Mg-Al Spinel Inclusions in U75V Heavy Rail Steel
## 3 Results and Discussion
### Precipitation of Inclusions during the solidification of Molten Steel  
The precipitation and average composition changes of inclusions in molten steel during the solidification and cooling of U75V heavy rail steel were calculated. The mass fractions of each element were \(\rm{w_{[C]}}\) = 0.75%, \(\rm{w_{[Si]}}\) = 0.61%, \(\rm{w_{[Mn]}}\) = 0.95%. \(\rm{w_{[Y]}}\) = 0.046%, \(\rm{w_{[Y]}}\) = 0.0013%, \(\rm{w_{[Ca]}}\) = 0.0008%, \(\rm{w_{[Al]}}\) = 0.0040%, \(\rm{w_{[Mg]}}\) = 0.0004% and \(\rm{w_{[Si]}}\) = 0.0040%. The calculated results are shown in Figure 13, where Figure 13a shows the phase transition of the inclusions and Figure 13b shows the change in the average composition of the corresponding inclusions. T. means Total. This is a habitual expression. The 1600 \({}^{\circ}\)C molten steel inclusions were mainly liquid. With decreases in temperature, the contents of CaO and SiO\({}_{2}\) gradually decreased, while the contents of MgO, Al\({}_{2}\)O\({}_{3}\) and CaS gradually increased. When the temperature was lower than 1470 \({}^{\circ}\)C, the Mg-Al spinel phase was precipitated from the steel. Furthermore, the average composition of the inclusions showed that the Al\({}_{2}\)O\({}_{3}\) and MgO content gradually increased. When the temperature was approximately 1100 \({}^{\circ}\)C, a large number of MnS inclusions were precipitated.  
From the above thermodynamic calculation, it can be seen that spinel inclusions will also be produced during the process of liquid cooling of steel. Compared with the transition of inclusions, the temperature required for the precipitation of spinel inclusions during the solidification of liquid steel is relatively low. The spinel inclusions precipitated from molten steel are controlled by two factors: molten steel composition and temperature. Temperature mainly affects the kinetic conditions of spinel inclusion precipitation. The size of spinel inclusions precipitated during the solidification of liquid steel is relatively small, as shown in Figure 14. Inclusion formation can be controlled by controlling the composition of liquid steel [28; 29]. Inclusion precipitation can be achieved by controlling the cooling temperature.  
Figure 14: Morphology and composition of spinel inclusions in billet.  
Figure 13: Precipitation of inclusions during the solidification and cooling of molten steel: Amount of inclusions (**a**) and Composition of inclusions (**b**).
# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 1 Introduction
### Contributions  
Motivated by recent convolutional activation map based metrics [28; 41], we make the following contributions in our study. Previous activation map-based approaches compared directly the similarity between reference and distorted activation maps by histogram-based similarity metrics. Subsequently, the resulted sub-scores were pooled together using different ad-hoc solutions, such as geometric mean. In contrast, we take a machine learning approach. Specifically, we compile a feature vector for each distorted-reference image pair by comparing distorted and reference activation maps with the help of traditional image similarity metrics. Subsequently, these feature vectors are mapped to perceptual quality scores using machine learning techniques. Unlike previous combined methods [35; 36; 39; 40], we do not apply directly different optimization or machine learning techniques using the results of traditional metrics; instead, traditional metrics are used to compare convolutional activation map and to compile a feature vector.  
We demonstrate that our approach has several advantages. First, the proposed FR-IQA algorithm can be easily generalized to any input image resolution or base CNN architecture, since image patches are not required to crop from the input images like several previous CNN-based approaches [24; 25; 29]. In this regard, it is similar to recently published NR-IQA algorithms, such as DeepFL-IQA [42] and BLINDER [43]. Second, the proposed feature extraction method is highly effective, since the proposed method is able to reach state-of-the-art results even if only 5% of the KADID-10k [44] database is used for training. In contrast, state-of-the-art deep learning based approaches' performances are strongly dependent on the training database size [45]. Another advantage of the proposed approach is that it is able to achieve the performance of traditional FR-IQA metrics, even in cross-database tests. Our method is compared against the state-of-the-art on six publicly available IQA benchmark databases, such as KADID-10k [44], TID2013 [46], VCL-FER [47], MDID [48], CSIQ [49], and TID2008 [46]. Specifically, our method is able to significantly outperform the state-of-the-art on the benchmark databases.
## 5 Result and Discussion
### Comparative Discussion  
The findings from comparing the BCHO-TCN LightGBM model with existing methods highlight its significant implications for predicting out-of-stock events and the results are presented in Table 10. By surpassing traditional techniques like perceptron, CNN-LSTM, and BiLSTM, the BCHO-TCN LightGBM model demonstrates the potential of combining models to enhance prediction accuracy and dependability. Despite its achievements, we should consider potential limitations such as data quality, scalability, and model interpretability. Nevertheless, the model's potential impact on industry practices is substantial. By integrating BCHO for fine-tuning, a TCN for feature extraction, and LightGBM for prediction, the method effectively captures complex temporal patterns and achieves superior accuracy. Compared to traditional and advanced models like perceptron, CNN-LSTM, and BiLSTM, the BCHO-TCN LightGBM model demonstrates enhanced performance. Additionally, it surpasses other hybrid models like rabbit-TCN LightGBM, hawk-TCN LightGBM, and culture-TCN LightGBM, showcasing its effectiveness in proactive stock management. In order to showcase its excellence, the BCHO-TCN LightGBM model was tested against other models to assess its performance. The evaluation involved analyzing metrics with a TP of 90 and a k-fold of 10. The findings demonstrate that when utilizing the product listings from the Amazon India dataset, the model exhibits high levels of accuracy, in addition to excellent NPV, PDV, and threat scores, reaching values of 94.52%, 95.16%, 94.81%, and 95.76%, respectively. Similarly, at ak-fold of 10, the model achieves values of 94.81%, 95.60%, 96.28%, and 95.86% for the same metrics.
# Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems
## 5 Detailed System Analysis
### Information Components
#### 5.1.2 Ad-Report Chain (ARC) and Integrity Hash (IH)  
Rather than dealing with Ad-Reports individually as they are being created, our system enables each user to aggregate multiple Ad-Reports throughout the course of defined period and then submit all of them as a single unit. As it has already been explained in Section 5.1.1, each Ad-Report contains the hash digest of the previous. This enables the user to link several Ad-Reports together in a form that resembles the architecture of a blockchain which is termed the Ad-Report Chain or _ARC_ for short.  
As shown in Figure 4, the first block of the _ARC_ contains an initiating value which is marked as _ARC-ID_. This is hashed to produce \(h(ARC-ID)\) that is included in the second block \(N=1\) with each consecutive block following the same arrangement. The \(h(ARC-ID)\) essentially works as a unique identifier which also marks the start of a specific _ARC_. The _ARC-ID_ is dictated by the Broker and sent to the user within the \(RF\) (Report Form) as depicted in the same figure. Recall from Section 4.1 that the \(RF\) (Report Form) is a message that comes as a response to the user's request to file Ad-Reports.  
As we can see, the \(RF\) also contains a cryptographic signing key \(sk\). While the _ARC-ID_ is used to identify and mark the start of an _ARC_, the \(sk\) is used to mark the end in such a way that it prevents the removal of addition of blocks. More specifically, when the user completes the creation of the _ARC_, the user produces \(h(ARC)\) and then signs it with \(sk\). The resulting message, which we can also see in the same figure, is termed as the _IH_ (or Integrity Hash) and is sent to the Broker along side the _ARC_.  
Figure 4: Structural elements of the Ad-Report Chain.  
The Broker can use a secret verification key \(vk\) in order to confirm that the \(IH\) was created by the user and then determine that the \(ARC\) has not been altered by comparing the \(h(ARC)\) from within the \(IH\) to an \(h^{\prime}(ARC)\) which the Broker computes himself.
## 5 Conclusions  
In this experimental study, the effects of different cutting parameters on cutting forces, surface roughness, and tool wear were investigated in the milling of Ti-6242S alloys. The results of the study are given below:  
1. ANN and RSM models were developed to predict cutting force, surface roughness, and tool wear.
2. To train a network in ANN, a Levenberg-Marquardt algorithm was used. Models in RSM used Box Behnken design. Values obtained from the ANN and the RSM were found to be very close to the data obtained from experimental studies.
3. The increase of the feed rate and the depth of cut increased the cutting force, surface roughness, and tool wear.
4. The increase of the cutting speed reduced the cutting force and the surface roughness and increased the tool wear.
5. To obtain low cutting force and a good surface quality, high cutting speed, low feed rate, and low depth of cut should be selected as machining parameters.
6. Cutting speed, feed rate, and cutting depth must be set at low levels to minimize tool wear.
7. From the results obtained from the experiment and RSM models, the predicted and measured values are quite close. The developed models can be effectively used to predict the cutting force and surface roughness.  
Experimental results using optimization and modelling methods such as ANN and RSM can be obtained in less time and result in economical machining. Results for different input parameters can be modelled without experimental studies.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] are equal to the contributions.  
**Conflicts of Interest:** The Authors declare no conflict of interest.
# Reliable Application Layer Routing Using Decentralized Identifiers
## 4 Design
### The Use of Decentralized Identifiers
#### 4.3.2 Resource URL Assignment  
A domain owner can create a \(Resource_{URL}\) and assign it to an IoT device. The IoT device will \"advertise\" this \(Resource_{URL}\) and will sign these advertisements using its private key. \(Resource_{URL}\) assignment is simply implemented by adding a new entry in the assertion list that includes the public key of the IoT device.  
As discussed in Section 4.2, the only cryptographic operation that IoT devices have to perform is to generate a digital signature every time they send an advertisement message. There can be cases where an IoT device is not powerful enough to perform this operation. In these cases, the advertisement process can be delegated to another entity or even to the edge node. This can be trivially achieved by including the public key of that node in the corresponding entry of the assertion list.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 4 Experimental Analysis
### Global Performance  
Figure 16 shows the cumulative distribution function of the distance error \(\epsilon_{d}\) for the different tracking methods (UWB, IMU + OFS, UKF) by considering all the test cases I-IX. As it can be noticed, the error of the UKF is bound to 4 m, which is acceptable for a tracking method in an industrial scenario of 4560 m\({}^{2}\) by also considering the forklift sizes. The forklift orientation, indeed, is better reconstructed with the UKF as shown in the histogram in Figure 17. In particular the global mean orientation errors are \(\bar{\epsilon}_{\theta_{\text{UWB}}}=0.1\) rad, \(\bar{\epsilon}_{\theta_{\text{IMU}+ OFS}}=0.18\) rad, and \(\bar{\epsilon}_{\theta_{\text{UKF}}}=0.014\) rad, whereas the standard deviations are \(\sigma_{\theta_{\text{UWB}}}=0.85\) rad, \(\sigma_{\theta_{\text{IMU}+ OFS}}=1.12\) rad, and \(\sigma_{\theta_{\text{UKF}}}=0.22\) rad. Accordingly to what was observed in the simulated analysis, the UWB system with a single tag is still not able to correctly retrieve the forklift orientation, but it has a better behavior with respect to the simulated analysis. This effect is obtained thanks the data processing implemented by the UWB system. The small orientation error committed with the UKF allows for determining the forklift orientation at the time of pallet unloading and, therefore, to better recognize the location in which the pallet is unloaded.  
Figure 16: Cumulative distribution function of the distance error \(\epsilon_{d}\) for the test cases I–IX.  
Figure 15: Mean distance error \(\bar{\epsilon}_{d}\) as a function of the initial uncertainty matrix \(\mathbf{P_{0}}\) computed for test case IX.
## 2 Methodology
### Multiple Converter Configuration Model (C-CM)
#### 2.6.1 Battery Electrical Model  
The battery is represented by a resistor-capacitor (RC) circuit as shown in Figure 5.  
The battery voltage \(V_{b}\) is defined in the following equation:  
\[V_{bo}={V_{b}}^{0}(t)-R_{bs}I_{b}(t)-V_{bc}(t) \tag{1}\]  
where \(V_{bo}\) is the voltage at the battery terminals, \({V_{b}}^{0}\) is the open circuit voltage of the battery and is given by \({V_{b}}^{0}(t)=c_{1}+c_{2}SOC_{b}(t)\), where \(c_{1}\) and \(c_{2}\) are constants obtained from the specification sheet of the selected battery. The resistance \(R_{bs}\) is the battery's internal resistance, and \(C_{bp}\) represents the dynamic response of the battery due to the diffusion of lithium ions. The battery current is given by \(I_{b}\) where \(I_{b}\) < 0 is considered for battery charging and \(I_{b}\) > 0 is considered for battery discharge. \(V_{bc}\) is the voltage drop in the sub-circuit \(R_{bp}\) in parallel with \(C_{bp}\). The voltage dynamics between the capacitor terminals \(C_{bp}\) are:  
\[C_{bp}\frac{dV_{bc}(t)}{dt}=I_{b}(t)-\frac{V_{bc}(t)}{R_{bp}} \tag{2}\]  
The state of charge (SOC) is expressed in the following equation:  
\[SOC_{b}(t)=SOC_{b}(t_{0})-\int_{t_{0}}^{t}\frac{I_{b}(t)}{C_{o}(t)} \tag{3}\]  
Figure 5: Battery electrical model.  
where \(C_{o}\) is the battery's nominal capacity. Equation (3) shows that the state of charge \(SOC_{b}\) is a time-dependent variable that has an initial condition that depends on the amount of charge that is extracted or stored in the battery. Such amount of charge is calculated by integrating the current \(I_{b}\) over time.
## Appendix A T-H-O-Risk Methodology
### Step 2--Event Tree  
An event tree (ET) is built to perform an overall system analysis (in two steps as follows) through a logical modeling technique for both success and failure through a single initiating event.  
* The probabilities are defined for each successive event through fault tree analysis and some typical events.
* Based on the logical structure of the events, the overall risk is then estimated for the building design related to fire safety. The overall risk is presented as ERL.  
Thus, the goal of the event tree is to compute the probability of a negative outcome that can cause harm, starting from an initiating event. Some key advantages of the event tree analysis are:  
* It can identify critical events that result in higher risk;
* It can determine cause and effect relationship;
* It can be automated.  
The following events can be found typically in the ET (refer to Figures 11 and 12):  
1. Initiating event;
2. Location of fire, e.g., apartment or corridor, concealed space or in a room;
3. Challenging fire or smouldering fire
4. Detection failure;
5. Alarm failure;
6. Sprinklers failure;
7. Egress protection where an emergency exit is blocked.  
The first row in Figure 11 shows the failure probability for each event that results in probabilities for each of the pathways. The ET begins with an initiating event that can cause failure (represented by 'FAIL') cases. In the present example, relevant design scenarios are sequenced. Figure 12 shows a typical event tree encompassing apartment and corridor fire and sub-systems: Fire in a CS--Concealed space or other room, Fire type-CF- Challenging fire or not, e.g., flaming or smouldering fire, Robustness Check includes failure probabilities for RC1: Detection failure RC2: Sprinkler failure RC3: Alarm failure, Egress protection failure BE--Blocked exit.  
Figure A2: Typical Event Tree for apartment and corridor fire.  
Reliable ignition frequency is a prerequisite for the overall risk estimation. The ignition frequency depends on the floor area of a particular building category. The annual ignition frequency is estimated based on the following generalized Barrois model [32]  
\[P_{1}(A)=c_{1}A^{r}+c_{2}A^{s} \tag{10}\]  
where \(P_{1}(A)\) is the ignition frequency of a building with floor area A during one year and \(c_{1}\), \(c_{2}\), \(s\), and \(r\) are constants that are derived empirically, computed through fire statistics available from different countries.