esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 6 Conclusions  
In this paper, we have shown the advantages of optical interconnects for multi-chip machine-learning architecture by comparing the performance and energy efficiency of an optically connected multi-chip system with those of an electrically connected one on various CNN models. This work was possible due to the following: (1) We observed that inter-accelerator communication accounts for a significant portion of training time in neural networks. (2) By in-depth consideration of current technologies, a reasonable and practical implementation of optical interconnect was assumed. (3) An arithmetic performance and energy model was made because the network topology and the baseline accelerator have simple structures. We show through evaluation that 100 and 400 Gbps optical interconnects improve the training time (and EDP) by an average of 20.6% (22.8%) and 35.6% (44.3%), respectively, compared to the baseline system with 25.6 Gbps electrical ones for nine CNNs.  
Conceptualization, J.A., Y.R. and E.L.; Data Curation, Y.R.; Formal Analysis, E.L.; Investigation, Y.R.; Methodology, E.L.; Project Administration, J.A.; Resources, Y.R.; Software, E.L.; Supervision, J.A.; Validation, Y.R.; Visualization, Y.R.; Writing--Original Draft, Y.R.; Writing--Review & Editing, J.A.  
This research received no external funding.  
This research was supported by the Future Semiconductor Device Technology Development Program funded by MOTIE and KSRC (10044735), and Next-Generation Information Computing Development Program through NRF funded by MSIP (2015M3C4A7065647).  
The authors declare no conflict of interest.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 4 Implementation and Results
### Experimental Procedures and Results
#### Step 2 Vulnerability Discovery  
In penetration testing, port scanning serves as a foundational step to map the attack surface of a target system. Open ports and associated services expose potential entry points for exploitation, making them critical to identify early in the testing process. This aligns with the reconnaissance phase of the penetration testing lifecycle, where attackers gather intelligence to prioritize vulnerabilities.  
Using Nmap, we conducted a full TCP port scan on the Metasploitable target machine (IP address: 192.168.1.10), identifying 23 open ports.
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 5 Experiment
### Numerical Result Comparison
(2) For RANSAC inliers, it is the projective errors from \(\hat{u}\) or \(\hat{u}^{\prime}\) to the corresponding epipolar lines. Usually, the smaller threshold means less inliers and better re-projection errors especially for real image pairs. For example, for the result of \(Zhanglan\) with the five-point method, the re-projection errors decreased from 5.5 to 4.7, on the other hand the number of inliers dropped from 94 to 86. In Section 4.3, the bigger metrical projection error could be produced in Step 3, which optimize \(F\) using algorithm in Section 4.2, because the initialization of global optimization is crucial. Once the initialization of Step 3 is recalculated, the result with smaller result will be obtained in three iterations most of the time. In order to measure the precision of reconstruction in metric space, we add some noise into matching features' position and then reconstruct the scene \(Simu\) to compare the distance between the points with the ground-truth distance simulated out. Because there are 100 points in \(Simu\), \((99+1)*99/2=4950\) distances can be used to compare the error with the ground-truth. We calculate the mean error of 4950 distances and repeat this process 100 times with different noise at the same level to obtain a average error distance. Considering the scene area is a unbounded factor to affect the error of distances, we apply the ratio of the above average error distance to maximum distance of these 100 points. The ratio result is within \(1\pm 0.4\%\) most of the time when the noise onto features is 0.5 pixel with Gaussian distributions. Our method provides a reasonable and high precision for 3D reconstruction.
# Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
## 4 Evaluations
### Simulation Configurations  
The proposed model was evaluated by our in-house trace-driven memory system simulator, which needed memory traces. Hence, we employed Pin-3.0 [8] to gather memory traces by running \(\times\)86 applications directly upon our host environment. Moreover, the generated trace is also suitable for measuring multicore-based environments. Table 3 presents the configuration details of our proposed model. The simulator used in our experiments was equipped with four cores, each running at a frequency clock of 4.0 GHz. The private L1 instruction and data caches had a capacity of 32 KB per core, and were organized as an 8-way set associativity structure with a 64-byte cache-line size. The private L2 unified caches had a capacity of 256 KB per core, and were organized as a 4-way set associativity with a 64-byte block size. Finally, the shared L3 unified caches had a capacity of 8 MB per core, and were organized in a 16-way set associativity with a 64-byte block size.  
Our proposed hybrid main memory model is comprised of a small DRAM module (128 MB) and a larger PCM module (2 GB), both based on fully associative organization with a 4 KB page granularity. Both memories adopt an LRU page replacement policy to swap pages between DRAM and PCM devices. A prefetch buffer is present for prefetching data from the hybrid main memory to the last-level cache layer, with the DRAM buffer having a capacity of 16 MB to store prefetching addresses generated by prefetch engines.  
Figure 6: Memory access patterns of graph processing.  
The prefetch table consists of an entry table and an offset table, with 64 entries in the former, each containing an entry memory address, a prefetch address value, and a prefetch hit count. The entry table has a capacity of 6.4 KB, with each entry having a capacity of 100 bits (46 bits + 32 bits + 32 bits). The offset table comprises 256 entries, each with an 18-bit offset bit, and has a capacity of approximately 4.6 KB, resulting in a total prefetch table size of around 11 KB. The entry table uses the LRU replacement policy, while the FIFO replacement policy is employed for the offset table. Table 4 outlines the simulation parameters employed to assess the efficacy of our prefetcher-based model compared to other prefetcher-based hybrid main memory architectures [21; 22].
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 4 Results and Discussion
### Pavement Temperature Prediction Based on Ensemble Models
These findings highlight that although the random forest model performed well within the training region, its generalization ability across regions is limited and highly sensitive to environmental differences. Relying solely on local data for model training makes it difficult to achieve accurate cross-regional predictions. Therefore, it is essential to enhance the modeling approach to improve adaptability in varying climatic conditions.
# Intelligent Monitoring Systems for Electric Vehicle Charging
## 5 Discussion
By embedding battery degradation models and power grid constraints directly into reward functions, such systems could balance immediate charging demands with long-term infrastructure preservation. Hybrid digital twin architectures, combining real-time sensor data with electrochemical battery models, could enable personalized charging protocols that optimize both user convenience and battery health [31; 41]. Privacy-preserving computation techniques will prove essential for maintaining user trust while leveraging sensitive behavioral data. Homomorphic encryption implementations could enable secure analysis of charging patterns across jurisdictions with varying data protection regulations [73; 74]. Federated learning architectures could facilitate collaborative model training between competing charging networks without direct data sharing [3; 50]. These approaches must be complemented by rigorous security frameworks capable of detecting novel attack vectors in increasingly connected infrastructure [21; 52]. Emerging technologies like quantum machine learning and neuromorphic computing could address current limitations in processing latency and energy efficiency [29; 64]. Quantum annealing implementations could potentially solve complex scheduling optimization problems intractable for classical computers, particularly in large-scale deployments with thousands of interdependent charging points [50]. Neuromorphic edge processors could enable real-time anomaly detection through spiking neural networks that mimic biological processing efficiency [49; 79]. The impending proliferation of autonomous electric vehicles introduces additional complexity to charging infrastructure management. Self-driving fleets will likely exhibit fundamentally different charging patterns compared to human-operated vehicles, prioritizing system-level efficiency over individual convenience [6; 28]. Developing hybrid simulation frameworks that combine discrete-event modeling with multi-agent reinforcement learning would help anticipate these behavioral shifts [7; 24]. Such systems must account for vehicle-to-grid integration scenarios where autonomous fleets actively participate in grid stabilization through bidirectional charging strategies [31; 32]. Ultimately, the evolution of EV charging infrastructure demands interdisciplinary approaches combining technical innovation with socio-economic insights. Behavioral economic models could inform incentive structures that promote efficient charging practices without compromising user autonomy [27; 82]. Cultural psychology perspectives may explain regional variations in charging behavior, guiding the development of locally adaptive management systems [25; 26]. By synthesizing these diverse strands of research, next-generation monitoring systems can transcend their current role as passive infrastructure components to become active enablers of sustainable mobility ecosystems.
# Online EEG Seizure Detection and Localization
## 2 Materials and Methods
### Adaptive Threshold  
Each individual's brain activity is different. Therefore, we use an adaptive PBI threshold as opposed to a static threshold [34] to determine whether the activity in the epoch under consideration is abnormal. The adaptive threshold is calculated using the PBI values in three blocks of epochs:The first block consists of the epochs from the beginning of the record to the epoch terminating at 3 min prior to the epoch under consideration.
2. The second block consists of the epochs from 3 min prior to the current epoch to 90 s (one and a half minutes) before the current epoch.
3. The third block consists of the epochs from 90 s prior to the current epoch to the beginning of the current epoch.  
These three blocks represent the background, recent, and current brain activities, respectively. We compute the average PBI values in each block denoted by PBI\({}_{1}\), PBI\({}_{2}\), and PBI\({}_{3}\). The adaptive threshold (\(\tau\)) is computed as a weighted average of these three values, further weighted by an adaptation coefficient (\(\alpha\)), which can be used to adjust the sensitivity of the algorithm.  
\[\tau=\alpha[0.5\;\mathrm{PBI}_{1}+0.25\;\mathrm{PBI}_{2}+0.25\;\mathrm{PBI}_{3 }]. \tag{4}\]  
After 3 min from the beginning of a record, the adaptive threshold is adjusted for each epoch. In Equation (4), the weight of the first PBI block is two times higher than the second and third, as the average of the first PBI block carries more information about the background activity of the brain. The second and third blocks are equally important for capturing activity during transition, hence they are weighted equally.  
Figure 2 contains an example of the PBI values for a complete EEG record and the corresponding \(\tau\) values for each epoch. The seizure period is marked by the red dashed line (between epoch 400 and epoch 450). If the threshold was set to a fixed default value (the black dashed line), there would be three events sensed as abnormal activities around the 50 th epoch, however, the PBI is greater than the adaptive threshold (orange) only during the seizure periods. When the PBI is greater than the adaptive threshold, we declare the epoch to be a candidate for a seizure epoch. As described in the next section, we then verify that a seizure has indeed taken place by examining the network connectivity measures.  
Figure 2: An example of PBI and the dynamic PBI threshold. The blue and orange lines illustrate the PBI for each epoch and the PBI dynamic threshold, respectively.
# The Chromosome Number and rDNA Loci Evolution in _Onobrychis_ (Fabaceae)
## 2 Results  
### Chromosome Number  
Chromosome number was analysed for 30 accessions representing 29 _Onobrychis_ species. The analysed species revealed two different basic chromosome numbers, \(x=7\) (17 species) and \(x\) = 8 (12 species). Ten \(x=7\) species were diploids with the chromosome number \(2n=2x=14\) and seven were tetraploids with \(2n=4x=28\). Eleven \(x=8\) diploids showed chromosome number \(=2x=16\), and one was a tetraploid with \(2n=4x=32\) (Table 1; Figure 1A-Z5). The chromosome number was newly determined for two species: _O. stenothiza_ (\(2n=2x=14\)) and _O. vaginalis_ (\(2n=2x=16\), Table 1; Figure 1A,T).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Taxon** & **2n** & **rDNA** **Locl** **Number and Localisation * \\ \hline  & & 355 **rDNA** & 55 **rDNA** \\ \hline
**Subgenus** _Onobrychis_ & & & \\ \hline _O. alkn_ subsp. _laconica_ & 14 & 2I & 1T, 1I \\ _O. caput-galli_ & 14 & 1T & 1I \\ _O. crista-galli_ & 16 & 3T & 2I \\ _O. gracilis_ & 14 & 1I & 2I \\ _O. humilis_ & 14 & - & - \\ _O. ibrica_ & 16 & 1I & 1T \\ _O. megatapirros_ & 14 & 2I & 2I \\ _O. persica_ & 16 & 2T & 1I, 1T \\ _O. stenothiza_ & 14 & - & - \\ _O. supina_ & 14 & 1I & 2I \\ _O. altissima_ & 28 & - & - \\ _O. ibbersteinii_ & 28 & 2T & 2T, 2I \\ _O. vicitifolia_ 1 & 28 & 2T & 2T, 2I \\ _O. vicitifolia_ 2 & 28 & 2T & 2T, 2I \\ _O. transcausica_ & 28 & 2T & 2T, 2I \\ _O. arenaria_ & 28 & 2T & 2T, 2I \\ _O. inermis_ & 28 & 2T & 2T, 2I \\ _O. cyi_ & 28 & 2T & 2T, 2I \\
**Subgenus** _Sisysrosema_ & & & \\ _O. chorossamica_ & 16 & 1I & 1T \\ _O. grandis_ & 14 & 1I & 1I \\ _O. gathue_ & 16 & - & - \\ _O. hpurgyna_ & 14 & 1I & 1I \\ _O. hachteia_ & 16 & 1I & 1T \\ _O. michauxii_ & 16 & - & - \\ _O. sintenisii_ & 16 & 1I & 1T \\ _O. nussilczenkoi_ & 16 & 1I & 1T \\ _O. nugianis_ & 16 & 2I & 1T \\ _O. nudita_ & 16 & 1I & 1T \\ _O. ptolemaica_ & 14 & 1I & 1T \\ _O. suhaculis_ & 32 & 1T & 2T, 1I \\ \hline \hline \end{tabular}  
* T–subterminal locus, I– interstitial locus.  
\end{table}
Table 1: The number of chromosomes and the number and localisation of rDNA loci in analysed _Onobrychis_ species.
## 1 Introduction
For example, deep learning and conventional machine learning techniques are effective at detecting the behaviour of unseen malware attacks [4]. However, these are computationally expensive to implement in IoT devices. AIS is proven to be compatible with IoT device limitations and, hence, is selected in this work to mitigate the data leakage risk at the edge (i.e., IoT devices/gateway) [5]. The _second_ work examines an IoMT use case concerned with energy theft detection (ETD) using smart meter data. Prior work [2] proposed a deep learning approach for detecting ETD with promising results, albeit using a centralised server, thus being vulnerable to MITM attacks. This work explores the utilisation of federated learning (FL), which enables efficient learning over decentralised private data without the need to transfer to the central server [6]; thus, the data are less exposed to MITM attacks and related risks of data leakage. In this context, this work presents one of the first FL-based AI methods for energy theft detection based on IoET-enabled energy consumption data. The _third_ work investigated differential privacy, an alternative effective countermeasure against MITM attacks, to unlock the potential of IoMT-based tests detection reported in [1]. Differential privacy is a mechanism in which signals carrying private data are contaminated with additive noise to alleviate the risk of data leakage in the event of an attack. We first present two leading HIIW solutions: IoET and IoMT in Section 2, where we demonstrate the unmatched potential of combining forces between IoT and AI to enable intelligent, scalable, and affordable solutions. Next, we present an overall summary of security threats in the IoT Section 2.3, in particular, malware and MITM attacks are discussed. Section 3 exposes the HIIW privacy risks under malware attacks and presents AIS, a state-of-the-art IoT-specific malware detection solution. Section 4 elaborates on the HIIW privacy risks that might result from MITM attacks on data transferring to the server and presents two solutions. The first is based on an IoET use case and is concerned with ETD using federated learning to avoid sensitive data transfer. The second examines an IoMT use case of remote stress detection and leverages differential privacy for data protection whilst being transferred to the server. In this work, we argue that security threats and adequate measures are not blanket solutions that apply to any IoT system. Instead, a contextual and application-specific solution is needed to address security and data privacy concerns. To this end, in Section 5, we propose an evaluation framework that comprises generic performance metrics as well as application-specific metrics to assess the efficacy of AI for IoT data. In Section 6, we discuss the remaining challenges related to leveraging IoTsystems in HIW, particularly related to data privacy concerns, and we present emerging trends to mitigate these risks.
# Effect of Wing Corrugation on the Aerodynamic Efficiency of Two-Dimensional Flapping Wings
## 4 Concluding Remarks  
In this work, the effects of wing corrugation on the aerodynamic performance were investigated to identify a proper wing corrugation to enhance the aerodynamic efficiency of the insect-like flapping-wing MAV called KUBeetle. A series of two-dimensional CFD analyses based on ANSYS-Fluent were performed for various wings. The computed time courses and cycle average lift and drag coefficients of the three leading-edge corrugated wings and the zigzag wing (Z) were compared with those of the non-corrugated smooth wing (S) at the spanwise locations of \(r=(0.375\) and \(0.75)R\). The results indicate that the corrugations placed on the entire wing chord were not beneficial to enhance the aerodynamic performance at both spanwise locations at \(r=(0.375\) and \(0.75)R\). For the three leading-edge corrugated wings, it was found that the leading-edge corrugation was beneficial in enhancing the lift coefficient and reducing the drag coefficient when the flapping wings operate at a relatively higher AoA, based on the two-dimensional wing kinematics at \(r=0.375R\). In particular, the LE3 wing with the largest corrugation had the highest lift coefficient (around 4% higher than that of the S wing) and the lowest drag coefficient (around 4% lower than that of the S wing). However, the aerodynamic performance of the corrugated wings was poorer than that of the S wing when they operate at relatively lower angles of attack, based on the wing kinematics at \(r=0.75R\) (6% lower in lift coefficient of the LE3 wing, compared to that of the S wing). Therefore, the use of the leading-edge corrugated wing in the inboard wing is a good choice to improve aerodynamic efficiency, because the AoAs in the inboard wing of KUBeetle are relatively high (\(\sim\)60\({}^{\circ}\)). Note that the leading-edge corrugation can also be structurally beneficial, especially in the inboard wing, where bending stress is higher than that in the outboard wing.  
CFD computation, data collection and analysis, writing original draft preparation, T.T.D.; 3D wing kinematics generation, T.K.L.A.; Data analysis, S.H.P.; Project administration, writing review and editing, funding acquisition, H.C.P. All authors have read and agreed to the published version of the manuscript. This work was supported by a National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (NRF-2018R1A4A1024191). H.C.P. appreciates Konkuk University's research support program for its faculty on sabbatical leave in 2020. The authors declare no conflict of interest.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 3 The Proposed e-RLRP Algorithm
### Algorithm Used in the e-RLRP to Reduce the Overhead
#### 3.2.2 Adjustment Factor
Whether when a new neighbor comes within range of a given node or when a neighbor leaves within range of that node  
\[M_{\text{counter}}=NewNeighbors_{\text{counter}}+LostNeighbors_{\text{ counter}}. \tag{16}\]  
Belonging to a family of statistical approaches used to analyze time series data in the area of finance and technical analysis [61], the Exponential Moving Average (EMA) can be used to estimate values [61; 62; 63]. The EMA is used to calculate if the occurrence of mobility tends to increase or decrease according to the Equation (17). The EMA is applied in a series of 10 values \(M_{\text{counter}}\). \[EMA_{\text{k}}=(M_{\text{counter}}-EMA_{\text{k}-1})(\frac{2}{(N+1)})+EMA_{ \text{k}-1}. \tag{17}\]  
If \(EMA_{\text{k}}<EMA_{\text{k}-1}\), the number of mobility events has a tendency to decrease, then the \(AF\) value will be increased. The period \(N\) of 10 values was chosen precisely because it is the number of times that \(AF\) must be increased until reaching \(AF_{\text{ul}}\). The scheme of the \(AF\) adjustment algorithm is shown in Figure 3.  
Thus, the \(BI\) is adjusted according to the mobility of the network, making possible to reduce overhead.
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.4 Chroma CQT  
The Chroma CQT (Chroma Constant-Q Transform) is designed to extract chroma features from an audio signal using a logarithmically spaced frequency axis. The Constant-Q Transform is unique in that it provides a constant ratio between the center frequencies of adjacent filters, making it particularly suited for musical/non-stationary signals, where pitches are often geometrically spaced.  
The formula for the Constant-Q Transform is given by  
\[CQT_{k}(t)=\sum_{n=-\infty}^{\infty}x[n]\cdot e^{-i\omega_{k}n}\cdot w_{k}[n-t] \tag{3}\]  
where  
* \(x[n]\) is the audio signal.
* \(\omega_{k}\) is the center frequency of the \(k\)-th filter, determined by the geometric spacing.
* \(w_{k}[n-t]\) is the window function for the \(k\)-th filter, which is typically designed to have a constant \(Q\) value, where \(Q\) is the ratio of the center frequency to the bandwidth.  
The Chroma CQT then maps the resulting spectrum from the CQT onto 12 chroma bands corresponding to the 12 pitch classes. This is achieved by summing the energies in the CQT bins that correspond to the same pitch class, regardless of the octave.  
The logarithmically spaced frequency axis of the CQT ensures that each octave is represented with an equal number of filters, aligning well with the perception of pitch in human hearing. This makes the Chroma CQT an effective tool for capturing the harmonic and melodic content, providing a meaningful and computationally efficient representation.