esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### Results of Malware Classification  
Figure 7 shows the loss and accuracy of the CNN model used to classify malware by applying the proposed method. The loss function represents the difference between the predicted and actual data. If the loss value is zero, the trained model predicts the data perfectly. Cross-entropy was used to train the GAN during the experiments. The train loss (Figure 7a) was measured as 2.6 in the first epoch. In the 28 th epoch, the loss value converged to 0.0543. The train accuracy (Figure 7b) started from 15.6% in the first epoch and converged to 99.9% in the 28 th epoch. The learning loss value was 0.0543 that converged to a low value; the learning accuracy was 99.9% that converged to a high value. This result shows that the CNN is well-trained using the proposed method.  
Table 3 shows the malware classification accuracy of each TFIDF classified using the proposed method. When the local image was created using the top 55 TFIDF, the accuracy was 99.65%. [PERSON] et al. [20] proposed a malware classification method that utilizes global image and local features. Their method differs from the proposed method in that the proposed method uses global and local images while their method uses one global image and local feature (text) extracted from the malware. The accuracy of the proposed method is 2.18% higher than that of the method proposed by [PERSON] et al. [20] [PERSON] et al. [22] proposed a method that visualizes local features to classify the malware. They used the same dataset as the one used in our approach; however, they did not use obfuscated malware. Although the proposed method utilizes all datasets, including obfuscated malware, it derives a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**GAN**} & \multicolumn{2}{c}{**CNN**} \\ \hline Parameter & Value & Parameter & Value \\ \hline Batchsize & 64 & Batchsize & 32 \\ Imageshape & (32,32,1) & Imagshape & (256,256) \\ Learnagrate & 0.0002 & Learnagrate & 0.0001 \\ Epoch & 10 & Epoch & 50 \\ Filter\_size & \(5\times 5\) & Filter\_size & \(3\times 3\) \\ G\_h0 & \(4\times 4\times 128\) & conv1 & \(128\times 128\times 32\) \\ G\_h1 & \(8\times 8\times 64\) & conv2 & \(64\times 64\times 32\) \\ G\_h2 & \(16\times 16\times 32\) & conv3 & \(32\times 32\times 64\) \\ G\_h3 & \(32\times 32\times 1\) & Fc1 & 128 \\ D\_h0 & \(16\times 16\times 1\) & Fc2 & 9 \\ D\_h1 & \(8\times 8\times 64\) & & \\ D\_h2 & \(4\times 4\times 128\) & & \\ D\_h3 & \(64\times 1\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters and values used in the experiments.  
higher accuracy of 0.39% than the method proposed by [PERSON] et al. [22] [PERSON] [17] and [PERSON] and [PERSON] [18] proposed malware detection and classification methods using the global image of the malware. Compared to our method, the only difference is the feature extraction method used in these methods. The proposed method shows a higher accuracy of 1.65% and 3.7% compared to the methods proposed by [PERSON] [17] and [PERSON] and [PERSON] [18], respectively.  
Figure 6: Merge results of global and local images.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 3 Preliminaries and Problem Definition
### Equivalence Class and Match  
In a published _l-diversity_[25] dataset, an equivalence class or bucket consists of \(l\) distinct sensitive values from a particular sensitive value domain \(S\) and highly similar _QI_ attribute values. In an equivalence class, any individual is linked with \(l\) distinct sensitive values so that the adversary cannot learn the sensitive values of the individual with a probability greater than \(1/l\).  
Let \(E_{T^{*}}\) and \(E_{F^{*}}\) be two equivalence classes in the published datasets \(T^{*}\) and \(F^{*}\), respectively. These two equivalence classes match if their attribute value pairs \(QI(E_{T^{*}})\) and \(QI(E_{F^{*}})\) are equal or have a non-empty intersection, and sensitive values \(\mathsf{S}(E_{T^{*}})\cap\mathsf{S}(E_{F^{*}})\
eq\emptyset\). For example, \(E_{T^{*}}\) and \(E_{F^{*}}\) are two equivalence classes if \(QI(E_{T^{*}})\) = (Age = 22, Sex = *, Zipcode = 47905) and \(QI(E_{F^{*}})\) = (Age = 22, Sex = *, Zipcode = 47905). Suppose that the sensitive value domains are \(S(E_{T^{*}})\) = (Ovarian Cancer, Gastritis, Flu, Breast Cancer) and \(S(E_{F^{*}})\) = (Blood Cancer, Gastritis, Breast Cancer, Flu). The \(QI\) values of the two equivalence classes are 22, Male, 47905 or 22, Female, 47905. Therefore, \(QI(E_{T^{*}})\cap QI(E_{F^{*}})\
eq\emptyset\). In addition, \(S(E_{T^{*}})\cap\mathsf{S}(E_{F^{*}})\) = (Gastritis, Breast Cancer, Flu )\(\
eq\emptyset\), and therefore, the equivalence classes \(E_{T^{*}}\) and \(E_{F^{*}}\) are matched.  
Let \(t\) be the tuples of a user in \(T\) and \(F\), \(s\) the possible sensitive value for \(t\). The probability of a true match is defined as \(P_{T}=P(t\in E_{T^{*}}\wedge t\in E_{F^{*}})\). In other words, the probability of a true match is the probability that the records of \(t\) are in both equivalence classes.  
A match can be generated by two independent persons: this is called a false match. Even If the user is not in two published datasets, there is still a probability that two records match. This is the probability of a match in \(F^{*}\) and \(T^{*}\) that is generated by the uncertainty of two independent individuals, denoted by \(P_{F}\). For example, in the published dataset, two persons may have the same age, live in the same zip code area and even have suffered from the same disease, without having visited the same hospital for medication.
## 3 The Proposed Algorithm
### Weighted Strategy  
A weighted strategy is proposed to obtain the final segmentation thresholds for further refinement of segmentation results. The weighted strategy is simple and effective. It is defined as follows:  
\[T_{f}^{i}=\alpha T_{o}^{i}+\beta T_{h}^{i}\ (i=1,\ \ldots,K) \tag{9}\]  
where \(T_{f}^{i}\) (\(i=1,\ \ldots,K\)) denotes the final optimized segmentation thresholds, \(T_{o}^{i}\) and \(T_{h}^{i}\) refer to thresholds of the original image and its hybrid filtering layer, respectively, and \(\alpha\) and \(\beta\) are two weights to balance \(T_{o}^{i}\) and \(T_{h}^{i}\), \(\alpha+\beta=1\). Here, we set \(\alpha=\beta=1/2\) empirically.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 4 Evaluation  
### Cost Model  
Join computation cost is the total cost of several stages, including pre-processing, data reading, map processing, communication between nodes, reduction processing, and data storage. The parameters used in the cost model are shown in Table 1. The general cost model for the join computation of two datasets is described in Equation (4).  
\[C(J)=C_{pre}+C_{read}+C_{map}+C_{tran}+C_{reduce}+C_{write} \tag{4}\]  
where:  
* \(C_{pre}=0\) (there is no preprocessing task of the three algorithms)
* \(C_{read}=(|R|+|L|)\cdot c_{r}\)
* \(C_{map}=(|R|+|L|)\cdot c_{m}\)
* \(C_{tran}=|D|\cdot c_{t}\)
* \(C_{reduce}=MAX(C_{reduce(1)},C_{reduce(2)},\ldots,C_{reduce(n)})\)
* \(C_{write}=C_{write(1)}+C_{write(2)}+\cdots+C_{write(n)}\)  
Some values (\(|D|\), \(C_{reduce}\), and \(C_{write}\)) are different in the three algorithms.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Meaning** \\ \hline \(n\) & Number of partitions \\ \(c_{r}\) & Cost of reading/writing distributed data (units of time) \\ \(c_{m}\) & Cost of processing map in memory \\ \(c_{d}\) & Cost of processing reduction in memory \\ \(c_{t}\) & Cost of communication between nodes \\ \(a_{i}\) & Ratio of skew-join key \(i\) \\ \(k\) & Number of skew-join keys \\ \(|R|\) & Number of records in dataset \(R\) \\ \(|L|\) & Number of records in dataset \(L\) \\ \(|D|\) & Number of records in intermediate dataset \(D\) \\ \(|R^{\prime}|\) & Number of records in \(R\) after the mapping process \\ \(|L^{\prime}|\) & Number of records in \(L\) after the mapping process \\ \(|R^{\prime\prime}|\) & Number of skew records in \(R\) after the mapping process \\ \(|L^{\prime\prime}|\) & Number of skew records in \(L\) after the mapping process \\ \(|O|\) & Number of join result records in each partition \\ \(|O^{\prime}|\) & Number of join result records of \(|R^{\prime\prime}|+|L^{\prime\prime}|\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the cost model.
## 3 Results
### Bibliometric Analysis  
#### 3.1.1 Publishing Trends  
The development of the researched field is better understood through the observation of the publishing trends in the collected literature sample. Firstly, it was noted that the publication period for this study was limited to the last ten years. However, the literature screening process showed that no topic-relevant papers were found before 2018, as shown in Figure 4. All the papers collected in the sample were published in the last six years, showing a growth in the number of relevant articles in the last two years, with 14 (67%) published papers. This observation confirms the novelty of this research field and shows the contemporaneity of the research problem. This trend underlines the need for this kind of study, as all available literature is contemporary and comprehensive reviews in the field have not been conducted.  
Figure 4: Number of articles published per year.  
The relevance of the research problem is also shown by analyzing the source journals for the retrieved sample. In the final sample, only two were conference proceeding papers [46; 47]; the rest were journal articles. This study includes 19 articles published in 13 different journals. Table 3 summarizes the source journals by the number of reviewed papers and the journals' impact factors. One journal, _Construction and Building Materials_, had a considerably higher occurrence rate than the rest, with four published papers on the topic [48; 49; 50; 51]. The previous shows the existing focus on using AI tools to analyze materials' structural properties in construction. Another three journals had more than one published article, including _Cement and Concrete Research_, _Buildings_, and _Automation in Construction_. The previous study [52] showed that these journals actively publish research on the 3 DP technology's application in architecture. However, it can be noted that these journals showcase the interest in research conducted on using AI tools in 3 DP.  
The support trend for research in this field is also sustained by the fact that almost all reviewed articles were published in leading journals in the field of digital fabrication and construction with high impact factors. Four of the identified journals, publishing six (28.5%) of the reviewed papers, had significantly high impact factor values above 10.
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 4 Experimental Results and Comparative Analyses
### Number of Page Faults  
Figure 5a illustrates the number of page faults in the SPEC VIRT_SC 2013 workload, when the \(\alpha\) takes a different value. As shown in Figures 3a and 5a, 1515 page faults are reduced when \(\alpha\) is set to 0.7 and only extra 0.09 s are spent than the original hybrid-copy algorithm. Less page faults can greatly improve the user experience.  
As shown in Figure 5b, when the VM runs a Linux Kernel Compile workload, it is best to set the \(\alpha\) as 0.3. The least page faults will be occurred in the post-copy phase because the Linux Kernel Compile is a read-intensive workload compared to the SPEC VIRT_SC 2013 workload. In the SPEC VIRT_SC 2013 workload, even the \(\alpha\) is set to 0.3, and there is no significant improvement. However, the total migration time is increased, which adds to the burden of the network. Thus, it is appropriate to set the \(\alpha\) to 0.7 in the SPEC VIRT_SC 2013 workload.  
Figure 4: The total number of transferred pages. **(a)** SPEC VIRT_SC 2013; **(b)** Linux Kernel Compile.  
Figure 5: Number of page faults. **(a)** SPEC VIRT_SC 2013; **(b)** Linux Kernel Compile.
## Materials and Methods
#### Description of the Instances  
To validate the SLAUPR model, random instances based on the behavior of the actual data were generated, and to analyze its patterns, 6 repetitions for each proposed instance were performed.  
The size of each instance was related to the number of spaces available to allocate product (i) and type of products requested (\(k\)) in each order (\(p\)). The products to be assigned (\(o\)) may differ from the type of product requested, since it must only be assigned once if a product is requested in several orders.  
The Figure 3 shows how the 4 types of scenarios for computational experimentation were created:  
For the sizes of the instances, the number of spaces available was taken into account and categorized as follows:  
* Small: It considers half of the available spaces in Area A (up to 16 spaces) and is created randomly based on the behavior of the case study. These instances are numbered from 1 to 15, see Table 2.
* Medium: It considers Areas A and B (from 32 to 61 spaces). Instances 16-19, 22-25, 28-31 and 34-37 have the behavior of real data; on average, per day, the minimum number of spaces visited is 32, the number of products requested is 8 (in the four scenarios) and the orders number 3. These instances are numbered from 16 to 69, see Table 3. For a better analysis, we used medium type 1, which are all analyzed in Area A (in 32 spaces) and type 2 in both areas (up to 61 spaces).
* Large: It considers Areas A, B, C and D. In these instances, Scenario 4 was not considered. Number 79 has the behavior of the actual data, considering the minimum average number of orders per day. These instances are numbered from 70 to 79, see Table 4.  
For the execution of all computational experiments, a computer with an Intel Xenon 2.4 Ghz processor, 64 Gb RAM and a hard disk with 2 TB capacity was used. The model was developed in the GAMS software version 23.8.2. Cplex 12.  
Figure 3: Scenario descriptions.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 2 Element Test of Viscoelastic and Rotational Friction Damper
### Damper Hysteresis
#### 2.5.2 Rotational Friction Damper  
Figure 10a shows the hysteresis of the west side RF dampers while Figure 10b is for east side under different clamping forces. The damper hysteresis was used to evaluate the relationship between torque force and angular rotation under cyclic loading conditions. Table 3 represents the clamping force applied to RV. The RF damper testing protocol initiated with 0 kN initial clamping force, followed by progressive increments up to 10 kN. The experimental data demonstrates a direct correlation between clamping force magnitude and resulting torque output, with measured torque values increased corresponding to the applied normal force.
## 2 Channel Propagation Characteristics
### Characteristics of Mid-Band and High-Band Frequency Spectra
#### 2.1.1 Short Wavelength  
The wavelengths in the mid-band are relatively longer compared to higher frequency ranges, allowing for better signal propagation and penetration through obstacles. The longer wavelength of mid-band frequencies makes them less prone to attenuation and allows for coverage over larger distances [15]. On the other hand, the high-band frequency spectrum, which encompasses frequencies above the mid-band range, has shorter wavelengths. For example, in the case of millimeter-wave frequencies used in 5G communication, the wavelength can be in the range of a few millimeters to a few centimeters. However, they also result in reduced distance range and limited penetration through buildings and other obstacles due to their higher susceptibility to attenuation [16].
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 2 Constellations and Observation Simulation
### Observations Simulation Configuration
The dry component of slant tropospheric delay is computed using the Saastamoinen model [29], as well as the global mapping function (GMF) [30], while the wet component is derived as a parameter of the PPP solution. We set the carrier phase ambiguities of each continuous arc as integer values and the phase delay was assumed to be a small floating constant. The residuals of code and carrier phase observations of the PPP process are employed to calculate multipath errors and noise corrections. Moreover, the relativistic correction, phase wind-up correction, and tidal displacements are also taken into consideration in the simulation. In the LEO onboard observations simulation, the calculation method of the components associated with GNSS satellites is the same as that for the ground measurements simulation. Similar to GNSS satellites, the position of the LEO mass center can be acquired from the simulated LEO orbits. The PCO and PCV values of onboard antennas are set to zero. The receiver clock offsets of ground stations are used with the ISB and IFB values derived from GFZ multi-GNSS bias products to simulate the multi-GNSS clock offsets for each LEO onboard receiver. Different from the ground stations, the signals between the GNSS and LEO satellites only pass through the topside part of the ionosphere. Hence, the ionospheric delay for onboard observations is generated according to its contribution to total electron content computed from GIM [31]. In terms of observation noise, the standard deviations of the random noise for code and phase observations are set to 1 m and 5 mm respectively. Details of the employed models and simulation standards are described in [PERSON] et al. [9].
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions  
As we anticipated in the Introduction, the Proxy can provide extended functions to support QoS-aware services at the network edge. Motivated by the congestion issues showed by the experiments reported in Section 3, we propose to introduce a requests-shaping and a caching function. Motivated by the payload size limitations showed in Section 3, we propose to also introduce a compression function.  
We propose here three relevant instances of said functions: (i) a request shaping function that guarantees a better QoS by controlling the delay experienced by the packets traveling in the constrained network and by reducing packet losses; (ii) a caching function that improves the QoS of the IoT system because it reduces the power consumption of the Client and the number of packets to be transmitted in the IoT network, saving network bandwidth and reducing response time, while guaranteeing information freshness; and, finally, (iii) a compression function that improves QoS by reducing the size of a packet, so that the number of transmitted packets and the overhead of a fragmentation mechanism are reduced. Indeed, if the compressed packet can fit in a network frame, fragmentation is not needed; if, instead, the compressed packet still cannot fit in a network frame, the number of transmitted packets is reduced because its length is smaller than its uncompressed version, and the fragmentation overhead is reduced because fragmentation is defined as a mechanism internal to the compression function.  
In the following, we describe each proposed extended function highlighting its objectives and showing its possible implementation.
## 3 Security and Privacy-Preserving Solutions for IoE
### Blockchain-Based Security Solutions
#### 3.1.2 Blockchain for Authentication in IoE: DID and SSI Methods  
Identity management is a critical element in ensuring system security within the IoE ecosystem. Blockchain-based authentication solutions are generally built upon two fundamental decentralized models known as DID and SSI. These models enable the management of user and device identities without relying on centralized authorities. DID allows IoE devices to create their own digital identities on the blockchain [74], while SSI enables these identities to be managed through digital wallets controlled by individuals or devices [75].  
As illustrated in Figure 3, blockchain-based identity authentication solutions allow users to verify their identities without requiring a central authority. In this structure, both the user and the validating device interact through their own SSI wallets, managing their respective digital identity information. This provides a bidirectional and secure authentication process.  
These mechanisms not only ensure security during the authentication process but also provide privacy-preserving solutions.  
\begin{table}
\begin{tabular}{l l l} \hline
**Storage Model** & **Advantages** & **Disadvantages** \\ \hline On-Chain Storage & Data is immutable and secure & High transaction costs and scalability issues \\ Off-Chain Storage & Lower cost, support for large data volumes & Dependency on external systems \\ Hybrid Storage & Balance between efficiency and security & Management complexity \\ \hline \end{tabular}
\end{table}
Table 4: Blockchain-based data storage models and their characteristics.  
Figure 3: Blockchain-based identity management model.
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 3 Simulated Annealing Based Resource Allocation
### SA for Subchannel Power Allocation  
SA is applied to determine the optimal subchannel power according to the system mode as \(\mu=2\), where \(a_{n}\) is defined as the power ratio of the total BS transmitted power to determine each subchannel power. The parameter \(a_{n}\) is a factor of the changes of the \(P_{n}\) value, where this factor is necessary to assign the power needed for each subchannel. Practically the subchannel power is evaluated as a fraction of the total transmitted power, which is a fixed value in the system. The factor \(a_{n}\) is defined as \(0\leq a_{n}\leq 1\), where \(\sum_{n=1}^{N_{c}}a_{n}\leq 1\) and \(\sum_{n=1}^{N_{c}}P_{n}=\sum_{n=1}^{N_{c}}a_{n}\times P_{max}=P_{max}\). The initial design established with random allocation of channel power \(P_{n}\) according to \(a_{n}\) factor as the current best solution for the objective function, then \(a_{n}\) factor is changed to modify channel power \(P_{n}\) for one chosen channel. The objective function is evaluated after the channel power \(P_{n}\) is modified, and the new solution is accepted if it is better than the current best solution. The accepted new solution is updated in the current best solution set, and the temperature is reduced for the current iteration. The subchannel power optimization process is modeled in Algorithm 2.  
The initial system design started with the random values of \(P_{n}\), where \(a_{n}\) determines the required subchannel power with respect to the maximum transmitted power. Moreover, equal power allocation is applied where \(a_{n}\) is not used in the optimization process. The SA determined the subchannel power is observed as close to equal power allocation. An overview of the mathematical proof is presented in Appendix A, where it shows that equal power allocation can be the optimum solution under certain assumptions.