esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges
### Ethical Considerations  
LLMs are trained based on data from the Internet, which includes a tremendous potential for biases, such as historical bias, representation bias, and semantic bias [148]. Therefore, it is necessary to thoroughly evaluate the ethical ramifications of models before putting them into practice. Moreover, users of LLMs will be increasingly in a dilemma-trading situation between privacy and efficiency. On one hand, to guarantee efficiency and performance, users have to upload personal data to the cloud-based LLM services, which poses the threat of further unwanted data collection by service providers. Edge-device-based LLM service paradigms, on the other hand, maintain data locality but are often unable to achieve a satisfying performance. Accordingly, ways to collect, store, process, and share such private data and information should be discussed, which provides guidelines for LLM designers and organizations. Finally, accountability is a critical principle of LLM development, where standards and legal regulations should be available, especially in the context of intelligent transportation system ethics [149, 150, 151]. Regulations like the General Data Protection Regulation (GDPR) will significantly influence the development and deployment of LLMs in ITS. Given that these regulations place increasingly strict requirements on data privacy, security, and user consent, applications of ITS will need to ensure that data are anonymized or encrypted to comply with GDPR. Moreover, LLM developers will need to implement mechanisms that allow users to control their data, including the right to access, modify, or delete personal information, and to opt out of data collection entirely. Compliance with such regulations will not only pose a set of technical challenges concerning updates and temporal information hiding but also have the potential to slow down the pace of LLM deployment, as companies will need to invest in legal and technical infrastructure to ensure adherence. Additionally, transparency requirements might lead to increased emphasis on explainability, ensuring that LLMs can provide clear reasons and use data for decisions made in transportation contexts, such as routing suggestions or traffic predictions. AI-specific regulations, such as the EU's proposed AI Act, will similarly influence the development and deployment of LLMs. In this context, applications of ITS will likely be classified as high-risk, necessitating rigorous safety, reliability, and accuracy standards, along with extensive testing and certification processes. Accountability measures will push organizations to establish governance frameworks, conduct regular audits, and ensure ethical use, while the focus on bias and fairness will drive the need for more rigorous evaluations to prevent discriminatory outcomes.
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 4 Experimentation
In this section, the proposed cryptography method was assessed using a number of parameters, including the time it took to generate each key, the time it took to encrypt files with different file sizes and chunk sizes, the number of keys generated, and the time it took from the generation of keys to the completion of encryption. This also applies to decoding.
# SURROGATES: Virtual OBUs to Foster 5G Vehicular Services
## 5 Evaluation
### Performance Analysis
In our case, the study has been focused on the resolution of requests from the vOBU, but these could be also solved by the same data analytics NFV instance, even improving the results. A deployment in which the edge and cloud planes were set-up at different locations would also imply better results. In general, when using a HSPA-capable cellular network (3.75 G), the time required to access the OBU is more than the double than the time spent by the vOBU to reply the data request. As more users and services access the same OBU, this improvement will be more evident, since the data hit rate is expected to be higher as the rate of requests increase. As compared with other solutions for vehicle data gathering, such as those based on VANET clustering and the use of proxies for V2I communications [5; 6; 7], SURROGATES focuses on downstream data requests and the reduction of their impact by vOBU caching, and moves data aggregation and further pre-processing capabilities to the MEC layer. For continuous monitoring of vehicles, uplink messages are sent using multiple communication technologies [25], following the recent trend of hybridizing data links, and integrating new-generation 5G and IoT technologies such as Low-Power Wide Area Networks (LPWAN) in the vehicular domain [26]. In addition, it is important to bear in mind that the SURROGATES approach makes services and user-level application agnostic of communication failures with OBUs, which is something quite probably to occur due to mobile wireless networks used by vehicles.
# Bearing Fault Diagnosis Approach under Data Quality Issues
## 2 Experimental Apparatus and Data Sets
### CWRU Vibration Data Sets  
The vibration data sets were collected from 28 bearings under normal and faulty conditions, as shown in Table 2. There were four data sets collected from bearings under normal condition. On the other hand, the remaining 24 data sets were collected from bearings under minor faults, 0.18 mm, and severe faults, 0.53 mm. As explained earlier, the tested bearing faults were ball, inner race, and outer race. In the experiment, the data sets were measured using accelerometers connected to the motor drive end with the aid of a magnetic base. The sampling rate of the data sets was 48,000 samples/s.  
In Table 2, the provided rotational speed is an average speed measured during each test. Regarding the measuring device, the speed was recorded via a tachometer installed on the shaft. In this experiment, using a dynamometer and electronic control system, several motor loads, 0-2.237 kW, were used to apply different torque loads on the shaft. Using the control system, motor load remained approximately constant throughout each test. Moreover, the small changes in motor load did not affect the radial load and they had a minimal effect on shaft speed [33].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Number of Data Sets**} & \multicolumn{1}{c}{**Diameter (mm)**} & \multicolumn{1}{c}{**Depth (mm)**} & \multicolumn{1}{c}{**Motor Speed (rpm)**} & \multicolumn{1}{c}{**Motor Load (kW)**} \\ \hline \multirow{4}{*}{Normal} & \multirow{4}{*}{4} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & 1772 & 0.746 \\ \cline{5-6}  & & & & 1750 & 1.491 \\ \cline{5-6}  & & & & 1730 & 2.237 \\ \hline \multirow{4}{*}{Minor faults} & Ball (4) & & & 1797 & 0 \\ \cline{5-6}  & Inner race (4) & 0.18 & 0.28 & 1772 & 0.746 \\ \cline{5-6}  & Outer race (4) & & & 1750 & 1.491 \\ \cline{5-6}  & & & & 1730 & 2.237 \\ \hline \multirow{4}{*}{Severe faults} & Ball (4) & & & 1797 & 0 \\ \cline{5-6}  & Inner race (4) & & 1772 & 0.746 \\ \cline{5-6}  & Outer race (4) & & & 1750 & 1.491 \\ \cline{5-6}  & & & & 1730 & 2.237 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the different bearing faults.  
Figure 2: Defected bearings with different faults: (**a**) ball, (**b**) inner race, (**c**) outer race. [32].
# Power Management Strategy for an Autonomous DC Microgrid
## 2 Autonomous DC Microgrid Configuration and Component Description
### Load-Shedding Calculation  
The main objective of the electrical power system is to satisfy load power demand. However, the load-shedding case may occur when the power demand is temporarily superior to the power supply. Some non-critical electrical appliances of a building can thus be shut down briefly and be restarted later. Hence, the load power \(p_{L}\) is described as in Equation (8):  
\[p_{L}(t)=p_{L\_D}(t)-p_{L\_S}(t) \tag{8}\]  
where \(p_{L\_D}\) is the load power demand and \(p_{L\_S}\) is the load shed power reference.
## Applications
### Categories Based on Operational Strategies  
#### 3.3.1 Cross-Silo Federated Learning  
Cross-Silo FL typically involves a small number of data silos (e.g., organizations or institutions) with relatively large datasets [52; 53]. These silos collaboratively train a model, ensuring data privacy and security [54]. This type of FL often requires more stable and reliable communication infrastructure compared to cross-device FL [55].  
**Applications**  
* Enterprise collaborations: Multiple companies training a shared model using their proprietary data.
* Inter-institutional research: Academic institutions combining research data to build robust predictive models.
# Dim Staring Debris Targets Detection Method with Dense Long Trailing Star
## 2 Semi-Occluded Object Detection Algorithm Based on FCN  
The staring debris mode in the background of long trailing stars aims to solve two problems in the process of target observation: one is that the debris target is too weak and has to rely on long time exposure to improve its SNR; the other is to reduce the SNR of the fixed stars by the long trailing tail of the background star to prevent the high density of the background fixed star from affecting the detection of the debris target. However, this mode also brings two new problems: one is that the long tail of the star leads to the occlusion of the debris target by the tail, which makes the conventional 3-5 frame association method ineffective; the other is that the tail is too long, causing the tail itself to be split into multiple segments, forming more false alarms.  
By focusing on these problems, this section presents an object detection method based on a single-frame image, which integrates traditional multi-frame association to realize object tracking. It mainly includes four parts: simulation scene and parameter description, trailing aggregation based on rectangle fitting, semi-occluded target clustering recognition based on FCN, and multi-frame association tracking.
## 3 Results and Discussion
### SDX Transformation Products
#### 3.4.3 Desulfonation  
The TP247 (C\({}_{12}\)H\({}_{15}\)N\({}_{4}\)O\({}_{2}\)\({}^{+}\), \(m/z=247\),1190) is an important product in the SDX degradation pathway, as it acts as a parent molecule for other TPs. It is proposed that its formation is caused by the removal of the sulfate center and the connection of the benzene ring to a N of the diazine. This mechanism has already been reported before [54; 56]. This pathway includes three additional TPs, TP263 (C\({}_{12}\)H\({}_{15}\)N\({}_{4}\)O\({}_{3}\)\({}^{+}\), \(m/z=263.1139\)), TP180 (C\({}_{8}\)H\({}_{10}\)N\({}_{3}\)O\({}_{2}\)\({}^{+}\), \(m/z=180.0769\)), and TP148 (C\({}_{8}\)H\({}_{10}\)N\({}_{3}\)\({}^{+}\), \(m/z=148.0869\)).
## 3 Performance Analysis
### Analysis for Multipath Fading Channel in the Presence of In-Band Narrow Band Interferences
Thus, the average probability of bit error can be expressed approximately as:  
\[E\{\left|\alpha_{k,l}\right|^{2}\}=\Omega_{l}\frac{1}{\gamma_{l}[(1-\beta) \lambda_{1}+\beta\lambda_{2}+1]}\text{exp}(-\tau_{k,l}/\tau_{l}), \tag{11}\]5  
Usually, the received hybrid wireless signal is subject to the aggregation of the original UWB radio suffering from multi-path propagation, multiple access interferences, narrow band interferences from the pre-existing wireless communication system and additive white Gaussian noise. If one only considers the AWGN case, the optimum receiver could consist of a matched filter (correlation) receiver, where the hybrid received wireless signal is correlated with the transmitted ultra-wide band pulse \(p_{tx}(t)\). Once multi-path propagation is considered, an ideal receiver will be replaced by the well-known Rake receiver [51] and channel equalizer [23], since the huge transmission bandwidths introduce a high degree of diversity at the receiver due to a large number of resolvable multi-path components. Commonly, the multiple access interferences (MAI) can be mitigated by designing and choosing the proper time-hopping sequences and direct spread spectrum codes [37; 38] or utilizing multi-user detection (MUD) techniques [57]. In this paper, we focus on the in-band interferences from the other pre-existing narrow band wireless signals. However, the proposed method can be used combined with any other interference mitigation approaches and digital reception techniques.
###### Abstract  
This paper proposes a novel multi-searcher optimization (MSO) algorithm for the optimal energy dispatch (OED) of combined heat and power-thermal-wind-photovoltaic systems. The available power of wind turbine (WT) units and photovoltaic (PV) units is approximated with the probability density functions of wind speed and solar irradiance, respectively. The chaos theory is used to implement a wide global search, which can effectively avoid a low-quality local optimum for OED. Besides, a double-layer searcher is designed to guarantee fast convergence to a high-quality optimal solution. Finally, three benchmark functions and an energy system with 27 units are used for testing the performance of the MSO compared with nine other frequently used heuristic algorithms. The simulation results demonstrate that the proposed technique not only can solve the highly nonlinear, non-smooth, and non-convex OED problem of an energy system, but can also achieve a superior performance for the convergence speed and the optimum quality.  
multi-searcher optimization; chaos theory; double-layer searcher; optimal energy dispatch; complex energy system +
Footnote †: journal: Applied Sciences
# Affordable Thin Lens Using Single Polarized Disparate Filter Arrays for Beyond 5G toward 6G
## 3 Fabrication and Measurement  
Figure 16 shows the top view of the fabricated lens and the alignment keys used for precise alignment among the substrates during fabrication. Figure 17 shows the measurement setup for the proposed lens mounted by a jig. The antenna array designed in Section 2.3 is fabricated by adding a feed line that includes power dividers and phase shifting lines for each antenna element. The feedline is soldered by a connector with the model number of 18S102-40 ML5 in Rosenberger company, for measurement. The manufactured \(4\times 4\) patch antenna array and lens are used as transmitters and the standard gain horn antenna of WR-15 type operating at 60 GHz is used as a  
Figure 14: Simulated 3D radiation patterns for the input phase offset of (a) 0\({}^{o}\), (b) 45\({}^{o}\), (c) 90\({}^{o}\), and (d) 135\({}^{o}\), along \(y\)-axis for lens applied to \(4\times 4\) antenna array.  
Figure 15: Simulated radiation patterns for \(4\times 4\) antenna array combined with the lens in case of each input phase offset.  
receiver. Finally, the antenna array is connected to the Anritsu MS4647B Vector Network Analyzer to measure radiation patterns of the proposed lens antenna. The manufactured antenna and lens are mounted using a specially designed zig for fine alignment of the distance and roll between them.  
The gain enhancement value obtained by the fabricated lens is extracted by comparing the received powers before and after mounting the lens in front of the antenna array. Figure 18 shows the simulated and measured results obtained by using a \(4\times 4\) patch antenna array before mounting the lens. The peak beams are respectively measured at 0, \(-9\), \(-21\), and \(-29^{\circ}\) with phase offsets of 0, 30, 60, and \(90^{\circ}\), and the gain values are 16.57, 16.46, 16.32, and 16.16 dBi, respectively. Figure 19 shows the simulated and measured results after mounting the lens. Setting the same phase offsets as the case of the antenna only, the peak beams of the proposed lens antenna are measured at 0, \(-1\), \(-2\), and \(-3^{\circ}\), respectively, and the gain values are 32.88, 31.87, 31.31, and 29.66 dBi, respectively. As a result, the measurement results appear to be in good agreement with simulation results.  
Figure 16: Top view of the fabricated sample of the proposed lens.  
Figure 17: Measurement system and jig for the proposed lens.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 4 Vibration Analysis of Subway Tunnel Construction
### Summary and Analysis of Results
#### 4.3.4 Vibration Attenuation Trend and Spatial Dynamics  
This study gathered extensive vibration attenuation data by setting multiple monitoring points along the diaphragm wall and central pillar. The results show a clear attenuation trend in PPV values as the distance from the blast source increases, which confirms the inverse relationship between vibration impact and distance. Comparative analysis of structural responses reveals that the central pillar experiences the highest vibration impact, followed by the diaphragm wall, and the roof experiences the least. This finding fills a gap in the existing literature by offering new insights into the spatial dynamics of blast-induced vibrations.  
Moreover, using FLAC3D simulations, the maximum predicted vibration velocity at the tunnel roof was 1.537 cm/s, which is higher than the 1.482 cm/s observed at the central pillar and the 1.424 cm/s at the diaphragm wall. These results, consistent with the stress cloud simulation in the figures, further verify the spatial distribution of tunnel vibrations.
# Research on Residual Life Estimation Method for KMN Steel Based on Nonlinear Ultrasonic Testing
## 3 Results
### Microscopic Morphology Analysis  
The dangerous position of the KMN-I bending fatigue specimen was dissected, and its microscopic appearance was observed. The study found that, as the number of fatigue cycles increases, the microstructure of the specimen gradually deteriorates, as shown in Figure 5.  
Figure 5a is the original specimen. The material matrix is relatively flat and has no obvious defects. Corresponding to the material in Figure 4, the non-linear coefficient \(\beta\) is low. As the number of fatigue cycles increases, we can see from Figure 5b that there are small defects, such as holes and pits inside the material, corresponding to the slight increase in the nonlinear coefficient \(\beta\) at this time in Figure 4. From Figure 5c we can see that there are micro-cracks in the material matrix. Corresponding to Figure 4, the nonlinear coefficient \(\beta\) of the material at this time has obviously increased and reached its peak value. With a further increase in fatigue cycles, the internal cracks of the specimen continue to grow, as shown in Figure 5d. The occurrence of cracks increases the attenuation coefficient of the material, resulting in a decrease in the nonlinear coefficient. Correspondingly, the nonlinear ultrasonic coefficient \(\beta\) begins to decrease at this time in Figure 4. It is proven [22,24] that there is indeed a relationship between the material's nonlinear coefficient \(\beta\) and the material's internal damage, and the material's nonlinear coefficient \(\beta\) can be used to reflect the fatigue damage degree and fatigue life of the material.
# Multi-Attention Recurrent Neural Network for Multi-Step Prediction of ChlorophyllI Concentration
## 1 Introduction and Problem Definition
### Problem Definition  
We formulate the prediction of chlorophyll concentration as a multivariate time series forecasting problem, aiming to accurately forecast chlorophyll values at multiple future time steps based on historical chlorophyll and exogenous factors data. In this work, we use symbols \(\{\mathbf{x}_{t}\}_{t=1}^{T}=\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{T}\}\) and \(\{y_{t}\}_{t=1}^{T}=\{y_{1},y_{2},\ldots,y_{T}\}\) to denote the observed data of exogenous factors and chlorophyll in the previous \(T\) time slices, respectively. Here, \(T\) is called the observation period or time window. \(\mathbf{x}_{t}=\{x_{1}^{\top},x_{t}^{2},\ldots,x_{t}^{n}\}\)\(\in\mathbb{R}^{1\times n}\) is a vector composed of n exogenous elements at timestamp t(1 \(\leq\) t \(\leq\) T), and the symbol \(y_{t}\) denotes the value of chlorophyll. Therefore, we describe the multi-step prediction of chlorophyll concentration as the process of selecting the optimal nonlinear mapping function from the hypothesis space. The input of the optimal function is historical data \(\{\mathbf{x}_{t}\}_{t=1}^{T}\) and \(\{y_{t}\}_{t=1}^{T}\), while the output is data on chlorophyll for the next \(\mathbf{\tau}\) time steps. The above process can be defined by Formula (1):  
\[\left[\{\mathbf{x}_{t}\}_{t=1}^{T},\{y_{t}\}_{t=1}^{T}\right]\overset{F(\cdot)}{ \rightarrow}\{\hat{y}_{t}\}_{t=T+1}^{T+\tau} \tag{1}\]  
where \(F(\cdot)\) is a nonlinear mapping function that maps historical sequence data \(\{\mathbf{x}_{t},y_{t}\}_{t=1}^{T}\) into the values of chlorophyll for the next \(T\) time steps \(\{\hat{y}_{t}\}_{t=T+1}^{T+\tau}\).
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### MNNFast  
In 2019, [PERSON] et al. proposed MNNFast to accelerate large language models [9]. MNNFast accelerates large language models using three optimizations. First, to reduce memory bandwidth consumption, MNNFast introduces a new column-based algorithm with streaming, which minimizes the size of data spills and conceals most of the off-chip memory access overhead. Second, to decrease high computational overhead, MNNFast implements a zero-skipping optimization that bypasses a significant amount of output computation. Lastly, MNNFast introduces a dedicated embedding cache designed to efficiently cache the embedding matrix.  
MNNFast has been implemented across various platforms (CPU, GPU, and FPGA). MNNFast improves the overall throughput by up to 5.38\(\times\), 4.34\(\times\), and 2.01\(\times\) on the CPU, GPU, and FPGA, respectively. The implementation that has been ported to FPGA achieves 6.54\(\times\) higher energy efficiency.