esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 4 Multicast Mechanism
### Data Forwarding  
In AHHM, routers can not only forward multicast packets based on the MGEID but also forward multicast packets based on the bit string. If the State Flag of the received multicast packet is set to stateful, the router checks whether its GMT contains the MGEID. If so, it copies a packet, adds the bit string to the header of the copy, and modifies the State Flag to stateless. Then, it forwards the copy through BIFT and forwards the raw packet through NFT. Otherwise, it only needs to forward the packet through NFT. If the State Flag of the received multicast packet is set to stateless, the router checks whether the bit representing itself in the bit string is set. If so, the router clears the bit string in the message, modifies the Stateless Flag to stateful, and forwards the packet through NFT. Otherwise, the router directly forwards the raw packet through BIFT.  
We took the scenario shown in Figure 7 as the example to describe the process of data forwarding in AHHM. After receiving the multicast packet in which the State Flag is set to stateful, N1 copies the packet firstly. For the copy, N1 modifies the State Flag to stateless, sets the bit string to 10, and forwards the copy through BIFT. For the raw packet, N1 forwards it through NFT. Since the GMT contains MGEID\({}_{1}\), N2 copies the multicast packet with the stateful flag. For the copy, N2 modifies the State Flag to stateless, sets the bit string to 01, and then forwards the packet through BIFT. Then, N2 discards the raw packet because the out-interface list of the NFT entry is empty. N4 directly forwards the multicast packet with the stateless flag through BIFT. After receiving the stateless multicast packet in which the bit representing it in the bit string is set, N6 clears the bit string, modifies the State Flag to stateful, and then forwards the packet to R1 through NFT. N3 and N5 directly forward the multicast packet with the stateless flag through BIFT. After receiving the stateless multicast packet in which the bit representing it in the bit string is set, N7 clears the bit string, sets the State Flag to stateful, and then forwards the packet to R2 and R3 through NFT.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the CIC-IoT2023 Dataset Using a Supervised Machine Learning Approach
#### 4.3.2 Research Analysis According to [50]  
Research [50] has investigated anomaly detection in IoT networks, utilizing the CIC-IoT 2023 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
A dataset was used, characterized by a large variety of attacks and significant class imbalance. In order to improve data quality and model performance, the following measures were carried out, and the feature scale was standardized using Standard Scaler. The collection was split into a training set and a test set. Random Oversampling (ROS) and Balanced Random Forest Classifier (BRFC) techniques were applied to balance the proportions of samples across classes.  
Step 2: Model utilization  
The Random Forest model was chosen as the classification model. To increase the efficiency of the model, two feature selection methods were used: the Correlation-based Feature Selection (CFS) and Information Gain Ratio (IGR) methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **ML Approach** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Source** \\ \hline Random & Supervised & 99.45\% & 99.45\% & 99.45\% & 99.45\% & [49] \\ Forest & Supervised & 99.887\% & 99.891\% & 99.987\% & 99.880\% & [50] \\ \hline Random & Supervised & 99.16\% & 99.13\% & 99.16\% & 99.09\% & [51] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification report summary on the CIC-IoT 2023 dataset (own elaboration).  
Step 3: Model evaluation  
Model performance was assessed using five metrics: precision, sensitivity, F1-score, accuracy, and Cohen's Kappa score. Results were compared for different model configurations, including the base model and models using feature selection and class balancing.  
Step 4: Result verification  
The use of feature selection and class balancing significantly improved model performance, especially for minority classes. The best results were obtained for a configuration combining the CFS technique with the BRFC method. Improved performance for minority classes is crucial for the effective detection of rare and potentially dangerous attacks.
# Enhancing Privacy in Large Language Model with Homomorphic Encryption and Sparse Attention
## 3 Materials and Methods
### Proposed Method  
#### 3.3.1 Overview  
A privacy-preserving dialogue model framework that integrates FHE with a dynamic sparse attention mechanism is presented in this paper. This framework, based on the transformer architecture, is specifically designed to perform complex natural language processing tasks on encrypted data without revealing the content of user data, as shown in Figure 3. Due to the reliance of traditional transformer models on linear and nonlinear operations over real numbers, they are not directly applicable to homomorphically encrypted data. Consequently, modifications have been made to the internal operators of the model. Specifically, linear transformations in the model are replaced with homomorphic linear transformations, employing integer approximation and quantization techniques to convert weights into a format operable within the homomorphic encryption environment. Activation functions are substituted with homomorphic-compatible versions that can be approximated by polynomials or piecewise linear functions. Additionally, special homomorphic batch normalization and integer optimization algorithms are introduced to accommodate the training and inference of the model on encrypted data.  
To enhance computational efficiency under FHE, a dynamic sparse attention mechanism is adopted. Unlike traditional attention mechanisms, which compute attention scores for all elements in a sequence, the dynamic sparse attention mechanism dynamically selects a subset of elements deemed most important for the current task based on predefined strategies. This approach significantly reduces the amount of computation in an encrypted environment and aids the model in focusing on processing information, particularly in the handling of long sequences, effectively mitigating the degradation of model performance. Within the overall workflow, user input data are first homomorphically encrypted locally and then sent to a server for encrypted inference computation. The server-side model executes inference tasks that include homomorphic operator replacements and returns encrypted results, which are then sent back to the client and decrypted locally, allowing the user to receive plaintext output results without concerns of privacy breach.  
In summary, the method described in this paper offers a novel solution to the privacy issues faced by large-scale dialogue models when processing sensitive data. By incorporating operator replacements and a dynamic sparse attention mechanism on the foundation of homomorphic encryption technology, our model maintains privacy protection while minimizing computational costs and sustaining performance, demonstrating potential for complex natural language processing tasks with secure privacy guarantees.
## 2 Few-Layer Transition Metal Dichalcogenides
### Nonlinear Optical Properties
#### 2.3.3 Other Nonlinear Effects  
Nonlinear optical phenomena beyond SA have also been observed in 2D TMDs. Indeed, for many nonlinear absorption studies on TMD materials, the effect was not monotonic: under sufficiently high intensity, the material exhibited an increase in absorption for increasing intensity (_i.e._, reverse saturable absorption, RSA, or optical limiting) [33; 42; 54; 56; 60]. This RSA effect has been attributed to two-photon absorption (TPA), where under intense illumination, two photons can transfer their energy to excite a single electron over the bandgap, explaining why this effect has been observed for excitation wavelengths corresponding to photon energies less than the material bandgap, but greater than half the gap energy. For SAs in fibre lasers, this transition from SA to RSA can influence the intra-cavity pulse dynamics and affect the transition between Q-switching and mode-locking regimes [61].  
Studies have also demonstrated a dependence of the nonlinearity on the flake sizes. For example, [PERSON] _et al._ showed that if the incident photon energy lies between the bulk bandgap and the bandgap of a monolayer for a given TMD, the flake thickness will determine whether the sample exhibits SA or TPA behaviour, since the layer count defines the energy of the bandgap [62]. [PERSON] _et al._ compared flakes of \(>\)100 nm, with \(\sim\)50 nm lateral size (of constant thickness), and showed that excited state absorption (ESA) occurred in the smaller flakes. They proposed that a greater number of edge-states existed in the bandgap for smaller flakes, which were quenched at high incident intensities [60]. The ratio of edge to surface area has also been attributed to sub-bandgap saturable absorption [32]. Additionally, it was shown that thermal effects under nanosecond or longer optical pulse excitation can lead to effects, including the formation of micro-bubbles, which cause nonlinear scattering. Nonlinear scattering manifests as an optical limiting behaviour similar to RSA [33; 60].  
Work is ongoing to understand how the fabrication procedure and flake geometry affect the nonlinear optical properties, although it is promising that such a wide parameter space can be achieved using 2D TMDs, suggesting that SAs can be engineered to meet a wide range of requirements for different lasers.
## 2 Radio Resource Management  
In this section, we describe the RRM problem by considering two different system scenarios: one with flexibility in power, bandwidth and beamwidth and the other with flexibility in illumination time assuming a system with Beam Hopping.
###### Abstract  
Radial flow is an important type of flow direction for large-scale pressure swing adsorption (PSA) oxygen generation systems. In this study, a numerical simulation of a PSA oxygen generation process based on radial-flow adsorbers was performed with two-dimensional CFD modeling. The gas distribution, the maldistribution factor and the pressure difference were comparatively investigated at each flow type of the radial-flow adsorber. Considering the gas adsorption performance, the results indicated that the centripetal \(\pi\)-flow radial adsorber has the best flow characteristics for the PSA process. The oxygen purity distribution within the adsorption bed was studied to compare centripetal and centrifugal \(\pi\)-flows, and the former was shown to perform better on oxygen enrichment and adsorbent desorption. The steady state was achieved after eight cycles for the centripetal-\(\pi\) adsorber and each of the four steps of the PSA process was explored in detail to show the advantageous properties for oxygen generation in terms of adsorption and desorption. The relationships between the product flow rate and the oxygen purity and recovery were further investigated.  
radial flow; adsorber; pressure swing adsorption; CFD modeling; oxygen generation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Weathering Experiment  
The weathering system used for the accelerated aging experiment consists of two main parts: the weathering chamber and the system for mixing the synthetic air with the selected gas. The weathering chamber used is made of a co-polyester glass (Bel-Art(tm) SP Scienceware(tm), Vienna, Austria), capable of blocking 99% of UV radiation and equipped with a gas inlet and outlet to allow continuous gas circulation (total volume of 30 cm\({}^{3}\)). Synthetic air (Messer Griesheim Austria, O\({}_{2}\) 20.5%, rest N\({}_{2}\)) is humidified using double-distilled water and mixed with the selected gas. The obtained acidic gas is then made to flow through a valve into the weathering chamber (flow rate 100 L/h). Gas concentration values were monitored daily with a specified sensor (Aeroqual Limited, Auckland, New Zealand, model AQL S200). During the aging experiment, the gas flow value varies by \(\pm\)1-1.5 ppm. The paint samples were artificially aged using 15 ppm of SO\({}_{2}\) in 80% relative humidity (RH) for a short-term exposure time (168 h). These specific aging conditions were chosen based on previous studies [22; 23] and to simulate accelerated aging in museum environments. In particular, the RH value was chosen to simulate an extreme condition that can occur in museums when mitigation of moisture levels is difficult due to the characteristics of the museum structure (e.g., historic building) or the specific geographical area in which the museum is located [3; 4]. The selected SO\({}_{2}\) value was chosen by considering the annual mean concentration of SO\({}_{2}\) measured experimentally in several European museums, from Northern to Southern Europe [8; 27; 28]. Specifically, the gas concentration monitored in museums is around 2.4 ppb. If the experimental gas concentration used (15 ppm) for 168 h is converted to one year, the corresponding annual experimental value is 287 ppb. Therefore, the aging exposure time used in the experiment is approximately equal to 120 years of aging in a museum environment [8; 22; 27; 28].  
\begin{table}
\begin{tabular}{c c c} \hline
**Pigment** & **Chemical Composition** & **Colour Index (C.1) Number** \\ \hline Manganese violet & NH\({}_{4}\)MnP\({}_{2}\)O\({}_{7}\) & PV16 \\ \hline
**Binders** & **Chemical composition** & **Commercial name** \\ \hline Oil & Linseed stand oil & Leinöl Stand oil 450 P (Kremer \\  & & Pigmente, Germany) \\ Alkyd resin & Oil-modified polyester-resin & Alkyd Medium 4 (Lukas, Germany) \\ Acrylic emulsion & p(nBA/MMA) & Plextol® D498 (Kremer \\  & & Pigmente, Germany) \\  & & Acronal® S790 (BASF, Germany) \\ \hline
**Samples** & **Composition** \\
**abbreviations** & **composition** \\ \hline Oil\_V & PV16 + stand oil \\ Oil\_V\_COMM & Commercial oil paint tube \\ Alk\_V & PV16 + alkyld resin \\ Acr\_V & PV16 + acrylic emulsion \\ St\_V\_V & PV16 + styrene-acrylic emulsion \\ \hline \end{tabular}
\end{table}
Table 1: Summary of materials employed and samples prepared for the study.
## 6 Discussion  
### Validation of the Method's Theoretical Assumptions for UDRC  
A primary objective of this study was to validate the pull-back method's core assumptions of one-dimensional stress and material homogeneity when applied to a composite system like UDRC. While the Novikov formula has inherent one-dimensional limitations for heterogeneous materials--including the homogenization assumption that treats multi-phase UDRC as an equivalent uniform medium, uniaxial stress assumptions that neglect lateral confinement effects, and simplified interface modeling that cannot capture rebar-concrete bond-slip behavior--our controlled experimental conditions demonstrate its practical reliability for UDRC spall strength measurement.  
Our finite element simulations provide compelling evidence for the method's applicability. The analysis of stress triaxiality shows that regions of significant multi-axial stress are confined to a small area immediately surrounding the rebar, while the bulk of the specimen cross-section, where spalling initiates, remains in a predominantly one-dimensional stress state. Furthermore, despite the high impedance mismatch between steel and concrete, the simulations predicted that the free surface velocity-the key measurement for the pull-back method-would not be significantly altered. This numerical prediction was subsequently confirmed by our experimental results, which demonstrated statistically indistinguishable free surface velocity profiles and spall strengths between plain and reinforced concrete specimens. This convergence of numerical and experimental findings strongly validates the use of the pull-back method for UDRC systems under the tested conditions, proving that despite the theoretical limitations of one-dimensional wave theory, the method provides reliable engineering measurements when experimental parameters are properly designed to approximate the underlying model assumptions.
## 4 Strategies for Separation of AE Impulses from the Background Signals of Various Types  
To develop strategies for isolating AE pulses, we consider the original signal as a composition of different background signals with superimposed AE data recorded in laboratory experiments. We propose two algorithms for separating the useful signal (AE pulses) from the general data stream: frequency filtering and decomposition into empirical modes. The algorithms are used in two typical cases: isolation of a time-localized useful signal from a continuous background signal (Type 3), and isolation of a time-localized useful signal against the background of periodic local acoustic impulses (Type 1).
## 4 Conclusions  
In this paper, a non-common-view axis alignment system is designed to meet the alignment requirements of airborne laser communication. Among them, the coarse alignment of the ground transmitter can ensure that the transmitted beam covers the drone, and the airborne terminal can control the beam to achieve alignment by adjusting the pitch angle and azimuth angle of the two-dimensional mirror, and the sending and receiving parties do not need to transmit data back. Field experiments were carried out at link distances of 10 m and 20 m respectively, the experimental results show that the tracking accuracy of the system is 13.98 \(\upmu\)rad, the signal amplitude at the receiving end is 23.0 mV, and the relative error of spot position detection is 0.97%, which is 36.6% higher than the traditional positioning algorithm. The feasibility of applying the non-common-view axis alignment method to the airborne laser communication system was verified.  
Conceptualization, X.K. and R.C.; methodology, X.K., M.H., R.C. and Y.S.; software, R.C. and M.H.; validation, C.K., Y.S. and X.K.; formal analysis, C.K.; investigation, Y.S. and R.C.; resources, C.K. and Y.S.; data curation, C.K. and Y.S.; writing--original draft preparation, C.K. and Y.S.; writing--review and editing, C.K., R.C. and Y.S. All authors have read and agreed to the published version of the manuscript.  
FundingFunding was received from the following: The Key Industrial Innovation Chain Project of Shaanxi Province [grant number 2017 ZDCXL-GY-06-01]; the General Project of National Natural Science Foundation of China [grant number 61377080]; the Xi'an Science and Technology Plan (22 GXFW0115); and the Scientific Research Team of Xi'an University (D202309).
## 2 Materials and Methods
### Cell Viability  
LIVE/DEAD assays were performed using Calcein-AM (cAM) and Propidium Iodide (PI) to label viable and dead cells, respectively. Briefly, spheroids were cultured for one week and were then transferred into glass-bottomed support, where they were stained for 2 h with cAM (4\(\mathrm{\SIUnitSymbolMicro g}\)/mL) and PI (2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). Images were then collected with the STELLARIS 5 Confocal System (Leica Microsystems, Wetzlar, Germany), equipped with White Light Laser (WLL) and Diode UV Laser, in combination with HyDS spectral detectors and equipped with a DMi8 inverted microscope. cAM was excited with WLL at 494 nm and emission spectral recorded in the range from 499-544 nm and PI with WLL at 549 nm and emission spectral from 577-720 nm. Images of confocal serial planes (Z STACK) were acquired using an HC PL APO 10\(\times\)/0.30, processed through the LAS X software, and shown as a \"Maximum Intensity\" projection. For each sample, three spheroids were assayed.
## 5 Performance Evaluation
### An Overview of the ARMOR  
ARMOR [26] is one of the most recent mobility solutions integrated within RPL, where it shows better performance compared with MA-RPL [23]. Thus, we picked ARMOR as the benchmark to compare with SDMob. Authors of ARMOR argue that the mobility solutions that are based on the RSSI routing metric fail to select the most stable routes. Hence the key idea behind ARMOR is in introducing a new routing metric, called Time-to-Reside (TTR) that estimates for how long each candidate parent is available. Each node embeds its GPS position and velocity in a DIO packet and broadcasts it. The neighbors exploit this information to calculate the TTR. Using this metric, ARMOR also proposes a parent selection mechanism. ARMOR modifies the Trickle algorithm to have short and constant intervals between DIO packets. ARMOR also allows mobile nodes to advertise the DODAG and act as parents, which is a reasonable design choice for a network with many mobile nodes. The fact that mobile nodes can be set as parents is inevitable in a topology with many mobile nodes and it may require more frequent DIO intervals for all the nodes. In the same way, ARMOR allows MNs to serve as parents with the cost of transmitting frequent update packets. The interval for sending the control beacons in SDMob is tunable according to the physical speed of the mobile node.  
ARMOR, like some of its counterparts, assumes that nodes are equipped with GPS modules, by which the location of the nodes is fed to the routing protocol. First, GPS systems are not practical in many applications because they perform poorly in an indoor environment. It is also possible to integrate other localization methods such as fingerprint  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Network Simulator & Cooja under Contiki-ng \\ Radio model & UDGM \\ Number of anchor nodes & 10, 20, 30, 40, 50 \\ Simulation Area & 20 \(\times\) 20 m \\ Transmission range & 5 m \\ Simulation time & 300 s \\ Initialization time & 300 s \\ Beacon Interval & 1 s \\ \(I_{min}\) & 212 \\ \(I_{max}\) & 220 \\ Buffer Timer & 500 ms \\ Congestion Timer & 0–50 ms (uniform distribution) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters used in the simulations.  
ing or triangulation, but the impact of its overhead and accuracy on the routing protocol needs to be investigated.  
Figure 8(a) highlights a motivational scenario for ARMOR where it can do better than baseline RPL. Using the TTR metric, node \(M\) selects node \(S2\) as its parent since it provides a long-lasting connection. On the other hand, in Figure 8(b), ARMOR neglects the more stable links provided by \(S1\) and \(S3\) and sticks to the \(S2\) with long-lasting but lower quality link.