esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Saliency Map Computation  
To interpret the decision-making process of the neural network model, saliency maps were utilized to extract the influential features within the EEG data for classification. Saliency maps are a well established technique in model interpretability, highlighting the sensitivity of the output to changes in the input [30]. This section explains the approach required to interpret the spatial significance of individual electrodes for particular class scores in an EEG classification task using EEGNet. The class score function of EEGNet, \(S_{c}(\mathbf{I})\), is investigated for an input EEG signal, \(\mathbf{I}_{0}\), and a specific class, \(c\), to identify the influence of electrode readings on the score of class \(c\) for \(\mathbf{I}_{0}\). Saliency maps are employed to visualize the spatial support of the class within the EEG signal.  
Due to the intricate non-linear mappings in EEGNet, direct inference of pixel (electrode) importance from the weights is not possible. Thus, the class score function \(S_{c}(\mathbf{I})\) near \(\mathbf{I}_{0}\) is approximated with a linear model using the first-order Taylor expansion:  
\[S_{c}(\mathbf{I})\approx\mathbf{w}^{\text{T}}\mathbf{I}+b, \tag{9}\]  
where \(\mathbf{W}\) represents the derivative of \(S_{c}\) with respect to the EEG signal at \(\mathbf{I}_{0}\), computed as [31]  
\[\mathbf{W}=\left.\frac{\partial S_{c}}{\partial\mathbf{I}}\right|_{\mathbf{I}_{ 0}}, \tag{10}\]where **W** denotes the saliency map for class \(c\). To ensure that the saliency map values fall within a suitable range for recognized practice in the field--thereby making the maps meaningful and useful for interpreting the decision-making process of neural network models--the saliency map is normalized as follows [31]:  
\[\textbf{W}^{\prime}_{c}(\textbf{I})=\frac{\textbf{W}_{c}(\textbf{I})-\text{min} (\textbf{W}_{c}(\textbf{I}))}{\text{max}(\textbf{W}_{c}(\textbf{I}))-\text{min} (\textbf{W}_{c}(\textbf{I}))}. \tag{11}\]  
Here, \(\textbf{W}^{\prime}_{c}(\textbf{I})\) represents the normalized saliency map for class \(c\). For multiple samples, the saliency maps are aggregated to obtain a mean map for the class [32]:  
\[\tilde{\textbf{W}}_{c}=\frac{1}{N}\sum_{j=1}^{N}\textbf{W}^{\prime}_{c,j}, \tag{12}\]  
where \(\textbf{W}_{c}\) is the mean saliency map for class \(c\), \(N\) is the number of samples, and \(\textbf{W}^{\prime}_{c,j}\) is the normalized saliency map for the \(j\)-th sample.
# Inferring TLB Configuration with Performance Tools
## 2 Materials and Methods
### Materials and Instrumentation
#### 2.2.2 Power9 IBM ISA  
The truth data for the Power9 were obtained from references [23,24]. The Power9 system, which runs Linux, supports the getconf command, and the PAGESIZE argument was utilized to determine the page size. The system under test employed a 16 K page size. Table 3 summarizes the TLB configuration for this system, including a Data Effective-to-Real Address Translation (DERAT) and Instruction Effective-to-Real Address Translation (IERAT) at the first level, with a TLB at the second level. The perf tool was also considered, though the events available differ from those in the Intel architecture. The primary challenge with Power9, or any ISA, involves the TLB mapping function. One approach to exploring a new TLB configuration is to test various standard mapping functions. Investigating common mapping functions used by specific vendors and experimenting with them could be effective. However, without proper documentation, it would be extremely difficult to infer the TLB configuration, especially since some vendors implement complex mapping functions involving multiple operations (e.g., multiple N-XOR operations).  
Another challenge is the availability of event counters. For security reasons, some systems disable access to event counters. Additionally, each ISA has its own set of counters, so a performance counter that works in Intel may not be available in IBM. In such cases, an equivalent counter needs to be identified, if one exists.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results
### Location Estimation Simulation
#### 3.2.4 Location Estimation Method  
This section describes a method for obtaining the estimated coordinates of a radio source from the direction of arrival obtained by the MUSIC and Beamformer methods. In this simulation, the direction of arrival of radio waves is estimated from three points as shown in Figure 15.  
The latitude and longitude of each HAPS observation point are converted to the Cartesian coordinate system, and a direction line is created in the x-y coordinate system from the direction of arrival. Figure 16 shows two localization methods from the estimated angular information. In the left-hand side case, all angular information is available from the three sensors, and the center of gravity of the intersections of each directional line is considered as the estimated coordinates of the emitter. In the right-hand side case, the angular information from one of three sensors is missing, so the estimated coordinates are the intersection points of the direction lines of the remaining sensors where angular information is available. For all other cases, the location of the emitter is considered to be unidentified (undetected).  
Figure 15: Direction of arrival estimation using high altitude sensors.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 2 Background
### Quantum Circuit Basics  
Quantum computing is a system of computation which relies on the principles of _quantum mechanics_[17]. In particular, it relies on _superposition_[18], which states that a quantum object can exist in a combination of its possible states simultaneously and _entanglement_[18], which enables quantum objects to become strongly correlated. A _qubit_ is the fundamental unit of quantum computation and it can exist in a superposition of two basis states \(|0\rangle\) and \(|1\rangle\), with probability amplitudes that determine measurement outcomes. The state of a single qubit, expressed as a vector \(|\psi\rangle\), can be written as a linear combination of the probability amplitudes of its corresponding basis states, \(|\psi\rangle=c_{0}|0\rangle+c_{1}|1\rangle\). Similarly, an \(n\)-qubit state can be written as \(|\psi\rangle=\sum_{i=0}^{2^{n}-1}c_{i}|i\rangle\), where \(\sum_{i=0}^{2^{n}-1}|c_{i}|^{2}=1\), \(c_{i}\in\mathbb{C}\). Operations on qubits are called quantum _gates_, and can be mathematically represented by unitary operators or matrices [19]. An operator is considered unitary if its matrix \(U\) satisfies \(UU^{\dagger}=I\), where \(U^{\dagger}\) is the conjugate transpose. Two quantum operations \(U\) and \(V\) are considered equivalent, up to a global phase \(\theta\), if \(U^{\dagger}V=e^{\mathrm{i}\theta}I\).  
Quantum computations evolve a statevector through unitary operations which can be represented as matrix-vector multiplications, \(U|\psi_{in}\rangle=|\psi_{out}\rangle\). Multiple quantum operations can be combined into a single unitary operation, where gates applied in series can be modeled as matrix multiplications, while parallel gates are described by Kronecker products. Thus, any circuit can be reduced to a single unitary matrix. In some cases, a unitary operation could also be decomposed into a sequence of smaller individual operations. This property is crucial for quantum compilation. The _circuit model_, or _gate model_, is a universal framework for quantum computation, representing quantum operations as gates acting on qubits [20], with time flowing from left to right. In this model, qubits are depicted as wires, and gates placed on those wires represent quantum operations acting on those qubits. Gates that can be applied in parallel form a _layer_. The total number of layers in a circuits is called its _depth_, and determines the execution time of the circuit; see Figure 1.  
Figure 1: Characteristics of a quantum circuit.
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
## 1 Introduction  
The multi-GNSS Pilot Project (MGEX) [1] was initiated in 2013 with the major motivation to increase the effort to prepare full integration of new constellations, e.g. Galileo, BeiDou, and QZSS, into the International GNSS Service (IGS) [2] processing routine, which is currently focused on GPS and GLONASS only solutions. Several IGS Analysis Centers (AC) prepare separately combined multi-GNSS solutions consisting of homogeneous and aggregated processing of all systems, i.e., GPS, GLONASS, Galileo, BeiDou, and QZSS. Processing of GNSS data for all mentioned navigational systems is complicated due to several satellite structural aspects, such as differences in signals and frequencies of transmitted data, shape, dimensions, and surface properties of satellites' bus and solar panels, and used technologies, for example, different attitude modes. On the other hand, the multi-GNSS orbit products are used by civil users, who expect intuitive and real-time information about the quality of multi-GNSS products. Therefore, it is possible to use the Satellite Laser Ranging (SLR) [3] technique as an independent validation tool for the orbit products. The approach of SLRvalidation of microwave-based GNSS orbits is well known and applied by many ACs [4; 5; 6]. However, there is a limited number of dedicated tools that would guarantee a constant and near real-time information about the quality of the products provided by particular ACs based on an autonomous SLR validation process. Thus, a new Associated Analysis Center of the International Laser Ranging Service [7] at the Wroclaw University of Environmental and Life Sciences (IGG ILRS AAC) has been established providing a service called as multi-GNSS Orbit Validation Using SLR (GOVUS) as its main component.
# Adaptive Ransomware Detection Using Similarity-Preserving Hashing
## 3 Methodology
### File-Hashing Techniques  
Three different, yet incremental, methods were developed to compare between them: full hashing, file-selective hashing (FSH), and similarity-preserving hashing (SPH). The methods vary in the number of hashes they generate, how many bytes they read from the file, and where in the files the bytes are read from (i.e., which snippets of the file are used to generate the hash).  
First, full hashing is depicted in Figure 1. In this method, the entire file is hashed to produce a single hash value, regardless of the file size.  
Second is FSH, as depicted in Figure 2[15]. TOTAL_BYTES are chosen from the file randomly and spread across partitions of size SELECTION_SIZE. Following that, the randomly chosen bytes are concatenated and then hashed, resulting in a single hash but only reading TOTAL_BYTES bytes from any file, regardless of its size. If the file is smaller than TOTAL_SIZE, the entire file is hashed in a manner similar to the full hashing method.  
Finally, SPH is shown in Figure 3, which is our proposed approach that aims to adapt to larger file sizes, quantify the size of file modification, and reduce performance footprint. Let us elaborate; the file is split into partitions of size PARTITION_SIZE, and within each partition, two blocks each of size BLOCK_SIZE bytes are chosen randomly, concatenated, and then hashed, resulting in a variable size of hashes for each file and allowing for the recognition of changed partitions in the file. Hashing partitions, instead of files, provides a finer resolution, which allows
## 3 Results and Discussion
### Phase-Amplitude Resonant Measurement Method
The measurements were made when the system reached a setpoint value of \(T_{set}\) within the range from 50 to 500 \({}^{\circ}\)C in 2 stages, i.e., with a K-type open-package thermocouple located inside the nozzle at a height of 90%, 50% (z = 8.5 mm), 10% of the lower nozzle section,  
Figure 10: Timings for measurements of useful signal a and b at times O-n and O+n. as well as outside the nozzle by soldering a thermocouple to its surface. The calibration results for \(T_{set}=250\)\({}^{\circ}\)C are presented in Table 2.  
The results of the experiment indicate a higher inertia of measurements carried out by the thermocouple method for all cases. During measurements, the output of the system responding to a setpoint value of \(T_{set}\) was estimated based on the previously obtained results for measuring the temperature with the internal location of the thermocouple at a height of 50% (reference temperature), since the thermocouple is a single point sensor and its location at a distance from the most heated point affects the measurement result and overshoot. The developed method excludes time delays associated with the thermal conductivity of the nozzle or sensor material, non-optimal location of the sensor. Measurement time consists of the ADC conversion delay (0.2 \(\upmu\)s [66]), need to wait for the zero-crossing moment of time (delay inversely proportional to the heating frequency \(f\)), and the oversampling delay (which depends on the target output resolution). In general, this delay ranges from 10 \(\upmu\)s (for ADC resolution of 12 bits, which corresponds to a temperature resolution of 8 \({}^{\circ}\)C) to 100 ms with an increase in ADC resolution to 20 bits (corresponding to a temperature resolution of 0.05 \({}^{\circ}\)C). The results of the experiments demonstrate the accuracy of measuring the nozzle temperature by the developed method of \(\pm 3\)\({}^{\circ}\)C at a measurement time of 100 \(\upmu\)s and \(\pm 0.2\)\({}^{\circ}\)C at a measurement time of 20 ms (output resolution of the ADC is 17 bits) in the temperature range from 50 to 500 \({}^{\circ}\)C.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
###### Abstract  
Wireless sensor networks (WSNs) based on the IEEE 802.15.4 standard have important applications in many fields, such as the Internet of Things and smart cities because of their low energy consumption. Hybrid carrier sense multiple access/time division multiple access (CSMA/TDMA) is the key technique to reduce energy consumption in the standard, but it also increases packet delay and reduces network throughput. Although the cluster tree topology is a typical topology defined by the IEEE 802.15.4 standard, there are few efficient medium access control (MAC) protocols specifically for this type of topology. To this end, we present an improved hybrid CSMA/TDMA MAC protocol based on a sharable slot algorithm for WSNs with cluster tree topology, called sharable slot-based MAC (SS-MAC). By designing the operating mechanism and frame structure, improving the hybrid CSMA/TDMA and channel-hopping techniques of IEEE 802.15.4 MAC, and introducing a sharable slot algorithm to wake up tree nodes asynchronously as well as a short address strategy to identify member nodes, the proposed protocol improves packet delay and throughput under the premise of low collision and low node energy consumption. Moreover, we derive mathematical expressions of the parameters of the sharable slot algorithm and evaluate the energy consumption, throughput and packet delay of the SS-MAC based on the queue modeling of packet arrivals. Numerical simulations verify that the proposed MAC protocol outperforms the other three existing MAC protocols, namely, IEEE 802.15.4 MAC, SSMA and LELLMAC, in terms of energy consumption, throughput and packet delay.  
CSMA/CA; TDMA; MAC; wireless sensor networks; sharable slot algorithm
## 3 Results and Discussion
### Welding Test Result
Figure 6: SEM images of bead-on-plate welds (welding speed: 7 m/min): (**a**) weld metal; (**b**) coarse grain heat-affected zone; (**c**) fine grain heat-affected zone; (**d**) intercritical heat-affected zone; (**e**) subcritical heat-affected zone; (**f**) base metal. Figure 8: Nanoindentation hardness test result in ICHAZ and SCHAZ. (**a**) Indentation location and (**b**) measured hardness (welding speed: 7 m/min). Figure 7: Vickers hardness profiles of bead-on-plate welds corresponding to welding speed. Figure 9: Microstructure around the indentation location no. 7: (**a**) 1500\(\times\) magnification and (**b**) 5000\(\times\) magnification (M: Martensite, F: Ferrite, T: Tempered Martensite). In the range of welding speeds of 6-8 m/min, there was no significant variation in the hardness of the WM and the tensile strength of the welds, except for a slightly lower tensile strength at the lowest welding speed (Figure 10). The crack initiation location in the tensile tests is indicated by the red arrows in Figure 11. The fractures were estimated to be initiated at the boundary of the ICHAZ and SCHAZ, which is consistent with the location of the lowest hardness in Figures 7 and 8. An examination of the fracture surfaces revealed that the fracture mode was ductile (Figure 12). The fracture surfaces contain dimple features that indicate a ductile fracture, and there are finer dimples at the fracture surface of the top and bottom parts than those of the middle part. Moreover, a surface similar to the cleavage facet, indicated by red arrows in Figure 12, was observed in the middle part of the fractured surface. Figure 11: Fractured specimens after tensile test. Figure 5:0 Tensile strength of bead-on-plate welds.
# ErbB4 Is a Potential Key Regulator of the Pathways Activated by NTRK-Fusions in Thyroid Cancer
## 3 Results  
### Differentially Expressed Genes  
To find genes that are activated in response to NTRK-fusion formation, we analyzed the RNA-seq data and identified DEGs in the following comparisons: (1) thyroid cancers without NTRK-fusions versus normal thyroid gland tissues; (2) thyroid cancer with _NTRK1_-fusions versus normal thyroid gland tissues; (3) thyroid cancer with _NTRK3_-fusions versus normal thyroid gland tissues. In order to select a high number of upregulated genes in response to NTRK-fusion, we identified genes with a log2 FoldChange \(\geq\) 1 in any of these three comparisons and used them for further analysis. The \(p\)-values were not used for gene filtering due to the low number of samples with NTRK-fusion, which could lead to the unreliable estimation of statistical significance and missing identification of key upregulated genes. We compared the three obtained lists of genes using the Venn diagram method (Figure 1A). About 90% of upregulated and downregulated genes were common for all three comparisons (Figure 1B). We identified 8809 upregulated genes common in all three subtypes--_NTRK1_-fusions, _NTRK3_-fusions, and no NTRK fusion, comparing tumor tissue vs. normal tissue. Gene set enrichment analysis (GSEA) of the GO and pathways for these common genes identified among the most enriched categories--\"adaptive immune response\", \"ER and Golgi membranes\", \"degradation of purine ribonucleotides\", \"ubiquitination\", \"glucoseogenesis\", and \"the citric acid cycle (TCA) and respiratory electron transport.\" Thus, our analysis demonstrated that cancerogenic mechanisms are greatly shared between all subtypes of thyroid tumors, which suggests the potential for common therapeutic opportunities for all thyroid tumors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Genes** & **Primers and Probes** \\ \hline \multirow{3}{*}{_TBP_} & TBP-R2 5’-CACCTGGATACAATATITITGCAG-3’ \\  & TBP-U2 5’-CACCTGCCACGCGACT-3’ \\  & TBP-PC2 5’-ROX-CGGAGAGTTCTGGAGATTGTACCG-BHQ2-3’ \\ \hline \multirow{3}{*}{_ERBB4_} & ERBB4-U2 5’-CCAGATCCGGGATGCACC-3’ \\  & ERBB4-R2 5’-ATGACTACTGGGACCGTTACAC-3’ \\  & ERBB4-PF2 5’-FA-TGCCATCCAACTGCACCCAAGG-BHQ1-3’ \\ \hline \multirow{3}{*}{_ETV6-NTRK3_} & ETV6-NTRK3-U 5’-CACAGCTTCCGCTCTAG-3’ \\  & ETV6-NTRK3-U 5’-CACAGCCGACTATCCTTG-3’ \\ \cline{1-1}  & ETV6-NTRK3-PH \\ \cline{1-1}  & ETV6-NTRK3-PH \\ \cline{1-1}  & 5’-HEX-CAGCACATTAAGAGAGAGACACATG-BHQ1-3’ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Oligonucleotide primers and fluorescently labeled probes used for the _ERBB4_ expression estimation in the FFPE blocks with and without NTRK gene fusions.  
Figure 1: The comparison of upregulated (**A**) and downregulated (**B**) gene lists for samples with _NTRK1_- or _NTRK3_-fusion, and without any NTRK gene fusions.
## 4 Results and Performance Evaluation of the Proposed and Existing Routing Protocols
### Results and Discussion
The end-to-end delay of the proposed and existing routing protocols is plotted in Figure 7. As shown in the figure, the delay of the proposed scheme is lower than all other schemes because of the lower distance between the sensor and sink node. The high delay in EELRP is to transfer data from multiple stages and not directly sent to the base station. As illustrated in the graph, the EEDORVA performs better than the EELRP because the EEDORVA transfer data to the base station using simple depth value selection. The figure demonstrates that the proposed routing scheme enhanced the end-to-end delay by 200% approximately compared to the EELRP and more than 25% compared to the EEDORVA. Figure 6: PDR of the routing scheme. Figure 7: End-to-end delay of the proposed and existing protocols. The transmission loss of the proposed CEER and the EELRP and EEDORVA schemes is plotted in Figure 8. As illustrated in the graph, using the multiple cooperative nodes is very effective in the CEER compared to the EELRP and EEDORVA noncooperative techniques. The proposed CEER scheme uses the Thorps attenuation model to measure the total loss between the source and destination during the data transmission. The other two methods, EELRP and EEDORVA, start lower and increase as the rounds increase. The figure illustrates that the proposed routing scheme enhanced the transmission loss by approximately 28%compared to both the existing EELRP and EEDORVA schemes.
## 4 Proposed Scheme
### Signing  
Before the messages are exchanged in 5G-enabled vehicular networks, the enrolled vehicle \(V_{i}\) needs to sign the messages with its private key as below. Figure 3 explains the process of the signing phase in detail.  
* The vehicle \(V_{i}\) randomly picks a value \(w\) and computes public pseudonym identity \(PPID_{v_{i}}=IPID_{v_{i}}\oplus h(A_{v_{i}}||T_{v_{i}})\), where \(T_{v_{i}}\) is the latest timestamp.
* The vehicle calculates the parameter \(Pr_{vi}=\mathcal{T}_{PPID_{vi}}(x)modP\).
* The vehicle signs the message \(SM_{vl}=h(M_{i}||PPID_{vi}||T_{vi})\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definition** \\ \hline TA & The trusted authority \\
5G-BS & The 5G-base station \\ OBU & The onboard unit \\ TPD & The tamper-proof device \\ \(\psi\) & The system parameters \\ \(P\) & A large prime \\ \(OID_{v_{i}}\) & The original identity of vehicle \(V_{i}\) \\ \(h\) & The hash function based on the chaotic map \\ \(SVP_{v_{i}}\) & A short valid period \\ \(IPID_{v_{i}}\) & An inter-pseudonym identity \\ \(T_{r}\) & The time of arriving \\ \(T_{v}\) & The time of predefined delay \\ \(T_{v_{i}}\) & The current timestamp \\ \(||\) & The concatenation operation \\ \(\oplus\) & The exclusive-OR operation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and Definitions Used.  
The vehicle calculates the message signature \(\sigma_{vi}=\mathcal{T}_{SM_{vi}}(Pr_{vi})modP\).  
Lastly, the vehicle broadcasts the message-tuple \(\{PPID_{vi},\,M_{i},\,T_{vi},\,\sigma_{vi}\}\) to others.