esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Design
### System Overview and Key Components
The limited tilt range (120\({}^{\circ}\)) of the PTU-D48 E also precludes an arrangement with parallel radiance and irradiance sensors since the radiance sensor would need to be tilted through 140\({}^{\circ}\) to go from vertical (zenith 180\({}^{\circ}\)) to the necessary water-viewing angle (zenith 40\({}^{\circ}\)). The radiance sensor was, therefore, fixed at an angle of 40\({}^{\circ}\) to the irradiance sensor, giving a zenith angle range for the irradiance sensor of 180\({}^{\circ}\) (downwelling irradiance measurement) to 60\({}^{\circ}\) (parked) and a zenith angle range for the radiance sensor of 140\({}^{\circ}\) (sky radiance measurement) through 40\({}^{\circ}\) (water radiance measurement) to 20\({}^{\circ}\) (parked). On average, the PT unit consumes 12.3 W during start-up, 6.2 W during hold, and up to 20 W during combined axial movements. This demonstrates the necessity of keeping the system in a low-power sleep mode as much as possible. The InSYS MRO-L200 gateway/switch connects all Ethernet devices, as well as providing the gateway to a 4G cellular network. This industrial device can set up and manage a VPN connection to the onshore server, and has a serial port and digital output, which could be used to remotely power cycle PANTHYR in case of problems. Its high power consumption (2.5 W typical) is the main drawback. A low-power network switch and a separate 4G gateway that is switched on only during short intervals would mitigate this, but also remove our option to connect to the installation in case of problems. Two mechanical structures were designed to mount to existing structures at the measurement sites and install the instruments. A versatile mounting system accommodates different measurement sites, while remaining simple. We started with a 50-mm steel pipe as a range of clamps are commercially available for this standard diameter. Welded on top of this pipe is a triangular plate. The head is  
Figure 1: Pan-and-tilt hyperspectral radiometer system (PANTHYR) system diagram. mounted on a second similar plate. Three threaded rods connect both plates atop each other. Leveling of the head is achieved by changing the spacing along the three rods. The result is a simple system with a variety of mounting options (Figure 2a). On top of the head, both sides of a U-shaped bracket accept adapter plates that hold the radiometers. Bolting holes for fixed inclinations of 20\({}^{\circ}\) and 60\({}^{\circ}\), as well as a slot that allows a variable 10\({}^{\circ}\) to 70\({}^{\circ}\) inclination, are available (Figure 2b).
## 3 Implementation  
The code is primarily written in Python 3, relying on the scientific Python (v3.8) software stack for parallel computing through mpi4 py [46] and petsc4 py [47]. Additional scientific Python packages utilized include NumPy for efficient array operations and SciPy for numerical computations. Furthermore, the algorithm leverages the PETSc library [48] and its extensive data structures and parallel iterative solvers, accessible in Python via petsc4 py and mpi4 py.  
Figure 1: Schematic diagram of an unstructured tetrahedral edge-based element.  
The accuracy and convergence rate of the iterative solver's solutions heavily depend on the mesh quality. In this study, unstructured meshes are generated using Gmsh (v4.13.1) [49] through the following process (see Figure 2):  
1. Define the boundary points for the computational domain, as well as the points corresponding to each formation boundary, anomaly boundary, and position of the receivers and transmitters.
2. Connect these points to form lines, surfaces, and volumes, constructing the initial model mesh.
3. Refine the mesh around the anomalies, receivers, and transmitter locations.
4. Export the final model in msh format for reading by the main program.  
Parallelization of the code is achieved through MPI's efficient communication mechanisms, flexible parallel programming model, and excellent scalability. The computational mesh is partitioned into multiple subdomains, each assigned to an independent process. MPI's point-to-point and collective communication functions ensure efficient and reliable data transfer between processes. The linear system is correspondingly decomposed, with each process responsible for storing and solving its portion. This strategy not only significantly enhances computational performance and efficiency but also effectively manages large-scale parallel computing tasks.  
Once the parameter and model files are defined, the algorithm imports them and begins assembling the global linear system. Kernel generation involves multiple processes, each handling its subdomain to cover the entire computational domain. Each process then assembles its local contributions into the global linear system for solving.  
Figure 2: Code structure.
## 3 Materials and Methods
### Dataset Description and Characterization
#### 3.1.2 Justification of Dataset Selection and Comparative Analysis  
In this work, we focused on the attacker attribution of MSNAs using a dataset derived from a real CTF competition. After preprocessing, our CTF dataset contained 53,830 MSNA sequences, each representing a temporally ordered series of malicious alerts generated by 50 unique attacker IPs (corresponding to 20 teams) against 20 victim teams, across 272 competitive rounds. This structure provided a rich, realistic, and highly diverse environment for evaluating attribution models in genuine multi-adversary scenarios. To illustrate the uniqueness and suitability of the CTF dataset for this task, we applied the same preprocessing methodology to UNSW-NB15 [23], one of the most widely used benchmark datasets in the cybersecurity literature. UNSW-NB15 is a synthetic dataset designed primarily for intrusion detection and attack classification, not for attribution. When extracting MSNA-type sequences from UNSW-NB15, we found that it contained a maximum of 40 attack sequences (MSNA) originating from only 4 unique source IPs (the \"attackers\"), targeting 10 different destination IPs (the \"victims\"). The vast majority of the dataset consisted of benign or non-attributable traffic, and the adversarial interactions were extremely limited in both scale and diversity. Importantly, UNSW-NB15 was never intended or declared as a multi-adversary benchmark; its design and labeling do not reflect the complexity or concurrency of real-world adversarial scenarios. In contrast, the CTF dataset is inherently multi-adversary by nature as it captures simultaneous, competitive interactions among multiple attackers and defenders in a dynamic environment. This stark contrast demonstrates that while UNSW-NB15 and similar datasets are valuable for detection and classification tasks, they are not suitable for studying multi-adversary attribution. Their synthetic nature, lack of temporal structure, and minimal number of attackers and victims make them fundamentally different from the complex, dynamic, and adversarial environment captured in the CTF dataset.
# Quantum Circuit Design of Toom 3-Way Multiplication
## 3 Proposed Method
### Efficient Scheduling  
Borrowing from the hardware terms, to reduce the number of operations, it is essential to employ an efficient scheduling (or sequencing) of the operations, merging similar operations whose values are overlapped. Regarding the number of operations that need to be performed, note that different from the classical implementation in which several values can be operated at once (e.g., ternary adders, carry-save adder tree (CSAT) for addition operation), common quantum approaches still leverage two-input operation. Following this concept, in the theoretical, unoptimized Toom 3-way multiplication method as presented in Section 2.2.2, there exists twelve \(\frac{y}{3}\) additions and five \(\frac{y}{3}\) submultiplications from the initial up to the point-wise multiplication stage (Equation (5)). At the interpolation step (Equations (7)-(11)), there are eleven \(\frac{2n}{3}\) additions/subtractions. Additionally, taking into account the coefficients of \(P\), \(Q\), \(R\), \(S\), \(T\) in those equations, there are two multiplicationsby 2, five divisions by 2, three divisions by 6, and one division by 3, each of size \(\frac{2n}{3}\). It is worth noting that in quantum, the first two operations are considered trivial since they can be implemented conveniently by a doubling and halving circuit, while the latter two are nontrivial since there has been no straightforward way to perform such operations. Lastly, at the recomposition stage, another four \(\frac{2n}{3}\) additions are required to combine the submultiplications' outcomes into the final multiplication result.  
In this paper, we adopt the sequence proposed by [PERSON] [25] for classical Toom 3-way multiplication, which is known to give a much lower number of operations. Additionally, since there are differences from the classical approach, we adjust the sequence and components for use in quantum computation.  
Let  
\[i_{1} =\frac{S-Q}{3} \tag{13}\] \[i_{2} =R-P\] (14) \[i_{3} =\frac{Q-R}{2} \tag{15}\]  
Then, the original mathematical formulation of Toom-3 interpolation can be rewritten as:  
\[A =T \tag{16}\] \[B =\frac{i_{2}-i_{1}}{2}+2T\] (17) \[C =i_{3}+i_{2}-T\] (18) \[D =i_{3}-B\] (19) \[E =P \tag{20}\]  
As can be inferred from Equations (17)-(19), a substantial simplification to the interpolation stage can be obtained. In particular, interpolation step requires only eight additions/subtractions. More importantly, as indicated by Equations (13) and (17), the execution of the costly division operation can be reduced to only one time, i.e., one division by 3.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Resource Estimation
#### 2.5.2.1 Direct Estimation
The advantage is low control overhead. The node, however, cannot release the bandwidth immediately when a route breaks [52] since it does not associate resources with flows. * Rate summation--In [15, 32, 42] the capacity is estimated as the difference between the raw rate and the sum of rate of the flows through the nodes. The bandwidth between the node and its neighbor is just obtained by recording the rate through the node. Figure 2 presents the classification of available resource estimation mechanisms of QoS enable multicast protocols in our survey. In IEEE 802.11, the carrier sense range is usually more than twice transmission range (the default ratio between these parameters is often 2.2 [47]). Therefore, the transmission of k-hop neighbor nodes (where k is more than two) must be considered. None of protocols in surveyed papers considers the transmission of more than two hop neighbor nodes when estimating available bandwidth. Only one protocol, QMRP [18], considers transmission of two-hop neighbor nodes; two protocols, Hu [17] and AQM [29], take into account transmission of one-hop neighbor nodes. In networks with a common channel with multiple accesses, the hidden node problem may cause packet collisions. QoS implies stricter boundaries on the packet jitter at each node. The more synchronized multicast is, the higher is the likelihood of packet collision due to hidden node problems. None of the protocols in the survey have investigated this effect. An additional concern is that available capacity is not the same as usable capacity. If the capacity is utilized to the maximum, the delay might not be acceptable in systems with contention-based MAC. Ideally, protocols should estimate the threshold load that results in a not acceptable delay. This is different from the total capacity. In QAMNet [11], a conservative rate is used instead of the threshold rate. Then the available bandwidth of a node is the difference between the conservative admission control rate and the current rate of the real-time traffic. However, this protocol does not consider any method to obtain this conservative admission control rate, which is still difficult to obtain. In the previous paragraphs, we have argued that resource estimation requires detailed knowledge of topology and transmission, interference, and carrier sense range. There is a continuous improvement in resource estimation in proposed QoS multicast systems. We also argue that the models are still based on simplified assumptions. The resource estimation will always be an estimate, not only because it is based on modeling assumption. The resource estimation is made when a source or receivers connect. It is therefore a point estimate for a longer period is the future. The accuracy of this point estimate will be a function of movement of all nodes involved and the delays in the information exchange between the nodes. It is therefore not given that more accurate models are the recommended path to follow.
## 2 Principle of Linearity Improvement
### Steps of Circuit Implementation  
If the Gilbert-cell based structure is adopted for a high-IP1 dB down-conversion mixer using the phenomenon of \"reverse uplift\", the bias voltage of the transconductance stage should be mainly determined by \(gm_{2}\). At the same time, \(gm\) should also be considered along with power budget to determine the size of the transconductance stage. By adjusting the transconductance stage amplification phase by inductors, as mentioned above, it should be determined whether the compression point curve of the conversion gain has a \"reverse uplift\", which can be carried out by using the large-signal harmonic balanced simulation. Furthermore, the amplitude can be controlled by the size of the switching stage, because it determines the output impedance of the transconductance stage. Finally,  
Figure 7: **(a)** Simulated real part and imaginary part of the input impedance of the switching core versus emitter length, and **(b)** the input impedance of the switching core versus RF frequency in Smith chart.  
parameter optimization, matching the network design for every port, passive design, and EM (electromagnetic) simulation are carried out iteratively. The whole design process is shown in Figure 8.
# Decentralized Consensus Protocols on \(SO(4)^{N}\) and \(Tso(4)^{N}\) with Reshaping
###### Abstract  
Consensus protocols for a multi-agent networked system consist of strategies that align the states of all agents that share information according to a given network topology, despite challenges such as communication limitations, time-varying networks, and communication delays. The special orthogonal group \(SO(n)\) plays a key role in applications from rigid body attitude synchronization to machine learning on Lie groups, particularly in fields like physics-informed learning and geometric deep learning. In this paper, N-agent consensus protocols are proposed on the Lie group \(SO(4)\) and the corresponding tangent bundle \(TSO(4)\), in which the state spaces are \(SO(4)^{N}\) and \(TSO(4)^{N}\), respectively. In particular, when using communication topologies such as a ring graph for which the local stability of non-consensus equilibria is retained in the closed loop, a consensus protocol that leverages a reshaping strategy is proposed to destabilize non-consensus equilibria and produce consensus with almost global stability on \(SO(4)^{N}\) or \(TSO(4)^{N}\). Lyapunov-based stability guarantees are obtained, and simulations are conducted to illustrate the advantages of these proposed consensus protocols.  
special orthogonal groups; consensus protocols; Morse-Lyapunov function; almost global asymptotic stability Article
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Urban Applications
#### 2.6.2 Smart Homes  
In the era of informatics, various systems can improve human lives. Wireless sensor networks can be applied in the indoor environment as in smart homes where machine to machine communications take place. Two typical examples are the indoor localization and motion monitoring and the monitoring of the indoor air quality.  
Specifically, the monitoring of indoor air quality (IAQ), which is a term that refers to the air quality of a building, has become, with the rapid urbanization process that is taking place, one of the most important topics of WSN urban applications. Modern people spend a great part of their life within a building, thus the air quality plays a critical role for their health, safety, and comfort. In order to monitor IAQ the authors of [51] developed a WSN application that utilizes wireless nodes equipped with gas sensors for sensing and communicating wirelessly in real time.  
Similarly, the WSN based IAQ monitoring system, which is described in [52], uses sensor nodes that measure temperature, relative humidity, and concentration of carbon dioxide in classrooms in order to correlate the level of indoor air quality with the students' level of performance in their studies.  
Another popular application of indoor localization and motion monitoring that uses WSNs, is described in [53]. The authors developed a system, which is used to estimate the position of a person within an indoor environment. The system utilizes mobile sensor nodes worn by the moving persons and a network of static sensor nodes at known locations. To calculate the person's position, the system gathers data from the nodes in a central PC and applies Monte Carlo based estimation algorithms to track the moving persons in real time.
## 3 Results and Discussion  
### Characterization of Emulsions Developed by Microfluidization  
Figure 1 shows the droplet size distributions (DSDs) of lemon essential oil emulsions as a function of HLB after preparation via microfluidization. Emulsions with HLB numbers of 11 and 12 exhibited monomodal distributions with single peaks at a droplet size of less than one micron in diameter. Emulsions with HLB numbers higher than 12 exhibited bimodal distributions. The appearance of a second peak is a clear indicator of recoalescence due to overprocessing [15]. This phenomenon is very common when microfluidization techniques are used [19]. Hence, we may conclude that a surfactant ratio with a resultant HLB higher than 12 does not effectively protect the interface, and that coalescence occurs during homogenization. A similar study that investigated the effect of HLB upon oil-in-water emulsions (also formulated with Span and Tween) found that an HLB number higher than 12 led to a reduction in zeta potential (absolute value) [23]. This fact is related to a higher tendency for droplets to aggregate. This could accelerate a destabilization mechanism, such as coalescence, with aging time. In addition, the values for Sauter diameters are presented in Table 1. In terms of this parameter, there were no significant differences between emulsions with HLB numbers of 11 and 12, and this finding was supported by ANOVA testing. However, a clear increase in the Sauter diameter was found in emulsions with HLB numbers above 12, due to the occurrence of secondary peaks. In all cases, the Sauter diameter was approximately 200-400 nm, i.e., a submicron figure.  
Figure 1: Droplet size distributions for emulsions formulated with 5 wt% lemon oil as a function of surfactant-mixture HLBs.  
Interestingly, differences in droplet size distributions did not result in any differences in flow behaviors, as can be seen in Figure 2, which illustrates the flow curves for microfluidized emulsions as a function of their resulting HLB numbers. Every emulsion developed in this study exhibited Newtonian behavior, with values for Newtonian viscosity ranging between 15.6 and 16 mPa-s. This finding indicates that viscosity does not depend on shear rate, and this is a characteristic of non-structured systems [24]. In addition, there were no significant differences between viscosity values as a function of HLB number, and this finding was also supported by ANOVA testing. Hence, we may conclude that HLB did not influence the viscosity of the samples prepared. It is well-known that viscosity in emulsions is influenced by oil concentration, droplet size distribution and the rheology of the continuous phase [24]. Consequently, if viscosity does not vary with HLB, then neither does HLB influence the rheology of the continuous phase. Thus, the viscosity was determined by the oil concentration, which was the same for all the samples, and this explains why the HLB number did not affect viscosity values.
## 5 Evaluation
### Experimental Setup  
In our privacy-preserving framework, we have six clients (data owner parties) and one server (computing party). We randomly assigned each of the six clients a portion of the total training data. The same subset is maintained by each client throughout the different experiments, while the server has the corresponding test data. We used three datasets in our empirical work: MNIST [16], Fashion-MNIST [17], and EMNIST-Letters [18].  
In the first phase of our framework, each client applies a Restricted Boltzmann Machine model to their data to encode data. We first build RBM models using the Learnergy library [67]. Table 1 shows the model architecture used for each data set. Then, each client uses and fits the built RBM model on its data to generate encodings, which are the learned hidden layer representations of the input data. In each RBM model, we set the size of the visible layer equal to input image 784 and the size of the hidden layer to 576 to generate 24 \(\times\) 24 encodings. We also employed the same RBM architectures on the server to encode the test data. Of course, the training parties must use the same RBM architecture.  
The next phase of our framework that follows the use of RBM is using an Instance Reduction Technique. Each client uses an Instance Reduction Technique to select a reduced set of data encodings to send to the server. We use the Edited Nearest Neighbor algorithm as implemented in the imbalanced-learn library [69]. The neighbor parameter is set to 3.  
Finally, the reduced sets of encodings are used by a central server to train global models. After combining the reduced sets of encodings from all the clients into a single global training dataset on the server, we randomly divided the data into training and validation sets. The validation set represents 20% of the training set. We use the validation set to tune the model. We train two types of classifiers for the global model (trained by the server): multilayer perceptron (MLPs) and convolutional neural networks (CNNs). Tables 2 and 3 show the CNN and MLP model architectures and training parameters for the MNIST and Fashion-MNIST, while Tables 4 and 5 show the model architectures for the EMNIST-Letters dataset.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Datasets** & **Model Architecture** & **Model Parameters** \\ \hline \multirow{3}{*}{**MNIST**} & Visible\_Layer\_Size = 784 & epochs = 500 \\  & Hidden\_Layer\_Size = 576 & Gibbs’ sampling steps = 1 \\  & Activation\_Function = sigmoid function & learning\_rate = 0.05 \\ \hline \multirow{3}{*}{**Fashion-MNIST**} & Visible\_Layer\_Size = 784 & epochs = 500 \\  & Hidden\_Layer\_Size = 576 & [PERSON] sampling steps = 1 \\  & Activation\_Function = sigmoid function & learning\_rate = 0.009 \\  & batch\_size = 10 \\ \hline \multirow{3}{*}{**EMNIST-Letters**} & Visible\_Layer\_Size = 784 & epochs = 500 \\  & Hidden\_Layer\_Size = 576 & [PERSON] sampling steps = 1 \\ \cline{1-1}  & Activation\_Function = sigmoid function & learning\_rate = 0.05 \\ \cline{1-1}  & & batch\_size = 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RBM architecture used for the datasets.