esa-sceva/satcom-chunk-collection huggingface dataset:
# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Device** & **LUTs** & **DSPs** & **Latency** & **Power** & **PLP** & **CPLP** \\ \hline \multirow{8}{*}{Gutter Oil} & EF2M45 & 1445 & 15 & 11.76 & 492.48 & 5789.10 & 149,937.75 \\  & EG4S20 & 1165 & 25 & 11.40 & 371.82 & 4238.32 & 211,492.13 \\  & GW2A & 1833 & 12 & 11.45 & 451.47 & 5167.12 & 873,243.27 \\  & Cyclone10 LP10 CL & 1795 & 15 & 10.55 & 271.38 & 2863.87 & 142,907.27 \\  & MAX10M50 DAF & 1400 & 25 & 10.17 & 312.53 & 3177.18 & 270,060.30 \\  & ICE40 UP5 & 1898 & 8 & 11.12 & 140 & 1556.10 & 59,131.80 \\  & MachXO2 & 2717 & / & 11.48 & 190.98 & 2192.07 & 65,542.85 \\  & M2S010 & 1398 & 22 & 11.63 & 292.47 & 3402.01 & 203,780.46 \\  & PGL12G & 1780 & 7 & 11.65 & 585.22 & 6820.15 & 354,648 \\  & Artix-7 & 916 & 10 & 10.75 & 589 & 6331.75 & 505,906.83 \\ \hline \multirow{8}{*}{Smart Grid} & EF2M45 & 1371 & 15 & 15.59 & 494.18 & 7704.24 & 199,539.69 \\  & EG4S20 & 1091 & 25 & 11.40 & 381.85 & 4353.48 & 217,238.81 \\  & GW2A & 1779 & 12 & 12.30 & 419.11 & 5155.09 & 871,210.19 \\  & Cyclone10 LP10 CL & 1530 & 15 & 10.62 & 267.11 & 2837.24 & 141,578.40 \\  & MAX10M50 DAF & 1335 & 21 & 9.58 & 311.08 & 2978.90 & 253,206.68 \\  & ICE40 UP5 & 1855 & 8 & 11.12 & 140 & 1556.10 & 59,131.80 \\  & MachXO2 & 2696 & / & 11.48 & 191 & 2192.30 & 65,549.71 \\  & M2S010 & 1263 & 22 & 11.79 & 300.44 & 3542.49 & 212,195.03 \\  & PGL12G & 1116 & 10 & 11.61 & 586 & 6802.87 & 353,749.45 \\  & Artix-7 & 919 & 10 & 10.90 & 588 & 6409.20 & 512,095.08 \\ \hline \multirow{8}{*}{Gutter Oil} & EF2M45 & 2651 & 15 & 12.44 & 542.82 & 6752.71 & 174,895.08 \\  & EG4S20 & 1766 & 29 & 11.13 & 481.85 & 5360.60 & 267,494.11 \\  & GW2A & 3235 & 8 & 12.32 & 579.51 & 7136.70 & 1,206,102.74 \\  & Cyclone10 LP10 CL & 3069 & 15 & 10.36 & 272 & 2818.46 & 140,641.35 \\  & MAX10M50 DAF & 2198 & 25 & 9.69 & 317 & 3072.05 & 261,124 \\  & ICE40 UP5 & 3887 & 8 & 11 & 140 & 1540 & 58,520 \\  & MachXO2 & 5763 & / & 12.05 & 191 & 2301.17 & 68,804.92 \\  & M2S010 & 2022 & 22 & 12 & 305 & 3658.78 & 219,160.92 \\  & PGL12G & 2546 & 7 & 11.97 & 585 & 7002.45 & 364,127.40 \\  & Artix-7 & 2269 & 14 & 11 & 591 & 6501 & 519,429.90 \\ \hline \multirow{8}{*}{Wine Quality} & EF2M45 & 3139 & 15 & 16.37 & 622.83 & 10,195.78 & 264,070.60 \\  & EG4S20 & 2231 & 29 & 12.31 & 491.84 & 6053.59 & 302,074.21 \\ \cline{1-1}  & GW2A & 3696 & 16 & 14.27 & 682.89 & 9741.40 & 1,646,296.15 \\ \cline{1-1}  & Cyclone10 LP10 CL & 3815 & 15 & 11.80 & 279 & 3290.81 & 164,211.17 \\ \cline{1-1}  & MAX10M50 DAF & 2895 & 33 & 9.66 & 322 & 3109.88 & 264,339.46 \\ \cline{1-1}  & ICE40 UP5 & / & / & / & / & / & / \\ \cline{1-1}  & MachXO2 & 6114 & / & 12.73 & 192 & 2444.54 & 73,091.87 \\ \cline{1-1}  & M2S010 & 2560 & 22 & 13.99 & 302.21 & 4226.41 & 253,161.77 \\ \cline{1-1}  & PGL12G & 2929 & 7 & 15.61 & 586 & 9146.87 & 475,637.45 \\ \cline{1-1}  & Artix-7 & 2320 & 14 & 12.47 & 588 & 7332.36 & 585,855.56 \\ \hline \end{tabular}
\end{table} 
Table 2: ANN deployment results on the low-cost FPGAs.
## 2 Related Works  
Some good surveys have covered different aspects of ICN research. [PERSON] et al. [2] and [PERSON] et al. [6] outlined the advantages of ICN over traditional networking and also described and compared the features of different proposed ICN architectures. [PERSON] et al. [7] analyzed and compared different ICN naming and routing mechanisms. They also provided detailed descriptions of the rendezvous mechanism, topology, and forwarding strategy of the various ICN frameworks.  
[PERSON] et al. [8] explored ICN caching strategies aimed at minimizing redundancy and maximizing content availability. [PERSON] et al. [9] studied caching for selected ICN architectures and suggested caching methods to minimize total bandwidth consumption, enhance delivery of service, and reduce upward and downward streaming. [PERSON] et al. [10] discussed recent ICN caching techniques in detail, categorized them, and delineated the possible benefits and drawbacks of each method. [PERSON] and [PERSON] [11] surveyed cachingchallenges in ICN, with a focus on on-path caching. They outlined existing caching and forwarding strategies and discussed current trends and challenges. [PERSON] et al. [12] and [PERSON] et al. [13] also surveyed caching strategies in ICN; however, they focused on how ICN caching mechanisms impact the Internet of Things (IoT) domain.  
[PERSON] et al. [14] studied key ICN technologies and their individual approaches to producer, consumer, or content mobility. In particular, the authors focused on two types of mobile networks: cellular networks and Mobile Ad Hoc Networks (MANETs). They presented the advantages of ICN in terms of mobility support and illustrated the new ICN-specific challenges, including source mobility, pairwise path routing, and handling outdated requests. [PERSON] et al. [15] examined how content-centric networking can be applied to wireless environments in general, as well as in specific networks such as MANETs, Vehicular Ad Hoc Networks (VANETs), and Wireless Mesh Networks (WMNs).  
[PERSON] et al. [16] surveyed the recent advancements in the field of ICN and the existing literature concerning multimedia communication in ICN. They described and categorized the different ICN video-streaming solutions and highlighted future research challenges for video communication in ICN. [PERSON] et al. [17] discussed the existing literature on integrating software-defined networking (SDN) with ICN, associated benefits and challenges, as well as open research questions. [PERSON] et al. [18] and [PERSON] et al. [19] have examined the feasibility of deploying ICN over existing IP-based networks and listed the potential complications that may arise during implementation and operation.  
In this paper, we study research works that focus on enhancing the QoS and QoE in ICN. We categorize the relevant works according to their strategy to improve QoS and QoE, analyze and compare the advantages and limitations of the methods, examine the evaluation metrics and experimental environments, determine which ICN architectures and quality measurements are most frequently studied, and list the open research challenges in the domain.
# A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition
## 2 Related Work
### Deep Learning-Based Recommender Systems
However, it is difficult to find a deep learning-based recommender system that can recommend in real time by recognizing only a user's facial expression without using pre-registered data or information such as purchase records or images. In terms of existing related studies, as shown in Table 1, most researchers collect user facial expressions and extract features to build dynamic user profiles. However, most studies applied simple heuristic techniques instead of computer vision approaches to extract user facial features. Additionally, most studies applied the CF method in the recommendation phase. However, there are many limitations to this traditional approach because it is critical to capture a user's facial expression and recommend advertisements quickly and accurately in real time. Users can quickly become bored, even with short videos. However, it is difficult to capture changes in user interest in a profile using the user's historical data for a short period. Therefore, it has become necessary to capture a user's facial expressions accurately and efficiently in real-time. In this study, we develop a recommender system through a comprehensive deep learning approach to recognize user expressions and predict how they enjoy the advertisement they are currently watching. This study elaborately extracts user facial expression characteristics in real time by applying SIFT, widely used in computer vision, to reduce the existing gap in such studies. We also applied CNN to accurately predict user preferences based on the extracted facial expression. The proposed methodology can effectively recommend advertising videos without distinguishing between new users, and it does not suffer from the new user problem.
# Development of Intelligent Core Network for Tactile Internet and Future Smart Systems
## 2 Related Works
Since the main requirement of the Tactile Internet system is the ultra-low latency, intelligent controller should be deployed at the core of the network to achieve the desired latency.
# Passivity-Based Control for a Micro Air Vehicle Using Unit Quaternions
###### Abstract  
In this paper the development and practical implementation of a Passivity-Based Control (PBC) algorithm to stabilize an Unmanned Aerial Vehicle (UAV) described with unit quaternions are presented. First, a mathematical model based on Euler-Lagrange formulation using a logarithmic mapping in the quaternion space is introduced. Then, a new methodology: a quaternion-passivity-based control is derived, which does not compute excessive and complex Partial Differential Equations (PDEs) for synthesizing the control law, making a significant advantage in comparison with other methodologies. Therefore, the control design to a system as the quad-rotor is easily solved by the proposed methodology. Another advantage is the possibility to stabilize quad-rotor full dynamics which may not be possible with classical PBC techniques. Experimental results and numerical simulations to validate our proposed scheme are presented.  
Keywords:passivity-based control; micro air vehicle (MAV); quaternion; real-time validation; under-actuated systems +
Footnote †: journal: Applied sciences
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 1 Introduction
The attention mechanism is an effective mechanism to obtain superior results, as demonstrated in image recognition [11], machine translation [12] and sentence summarization [13]. Therefore, the attention mechanism [12] was applied in the AC-LSTM model, used to capture the importance degrees of effects of past feature states at different times on PM\({}_{2.5}\) concentration in this paper. The major contributions of this paper are as follows: (1) by analyzing the spatiotemporal correlation of air quality data, we propose a novel deep learning method that can capture the spatiotemporal dependency of air pollutant concentration, to predict PM\({}_{2.5}\) concentrations in the next 24 h; (2) according to the importance degrees of effects of past feature states on PM\({}_{2.5}\) concentration, the attention-based layer weighs the past featured states in our predictive model to improve prediction accuracy; (3) comparing the performances of six popular machine learning methods in the air pollution prediction problem, we validate the practicality and feasibility of the proposed model in PM\({}_{2.5}\) concentration prediction.
## 3 The Recommendation Pipeline
### Constructing Patient State Vectors
#### 3.3.2 Diagnostic State  
The MIMIC-III table diagnoses_ic provides a list of a patient's diagnostic codes on discharge. To determine a time during a patient's stay when a specific diagnosis was made, we examine the medical staff's notes in the noteevents table. Each note has an entry timestamp. We search each note to find a match for any of the diagnoses listed in the diagnosis_icd table. If found, we assume that the diagnosis was made at the time of the note's entry. This gives us a time-varying diagnostic state for the patient.  
We then map each of the 15,000 possible diagnostic codes to one of 19 higher-level ICD9 diagnostic groups. Thus, the entire diagnostic state is a 19 bit vector: a bit for each diagnostic group. For example, the bit vector [0101 0000 0000 0000 000] indicates that a patient was diagnosed with a disease in groups 2 (the neoplasm group) and 4 (the blood and blood forming organs disease group). An example of such a patient would be one with Hodgkin's Lymphoma (group 2) and an iron-deficiency anemia (group 4). Since there are over 15,000 diagnostic codes, encoding only the top-level ICD9 codes reduces data sparsity, thus enhancing generalizability.  
We treat diagnoses in a cumulative manner. At time \(t\) from admission, if a patient was diagnosed with a disease in group 2, then at all times \(t^{\prime}\geq t\), the diagnostic bit vector of the patient's state \(\mathcal{S}[p,t^{\prime}]\) will continue to have the second bit of the diagnostic state set to one.
# Slope Stability Prediction Based on Incremental Learning Bayesian Model and Literature Data Mining
## 4 Discussion
### Case Validation  
To validate the ILB model's applicability, 15 slope cases were referenced from the study of [PERSON], et al. [55]. These cases comprise 11 collected by the authors from various papers and 4 examples from the Lala Copper Mine in Sichuan Province, China, numbered 12, 13, 14, and 15. Situated in Lushui Township, Huili County, Sichuan Province, as depicted in Figure 10, the Lala Copper Mine encompasses an eastern and western open-pit mining area, with the latter containing the East, West, South, and North slopes of the eastern open-pit. The yellow dashed lines in Figure 10 represent the approximate positions of the slope profiles, which were determined based on the descriptions provided by the original authors.  
The Bayesian model not only provides predictions for class labels (0 or 1) but also offers the probability distribution for each class, which helps us better assess the model's performance. The trained ILB model was used to evaluate the stability of the 15 slope cases, and the outcomes are detailed in Table 6. Analysis revealed that the model accurately predicted stability in 12 cases and inaccurately in 3 cases, yielding an 80% prediction accuracy, indicating a high degree of precision. Slopes 1, 2, and 11 were misjudged among the inaccurately predicted cases. Slope 1, despite being stable, was predicted as unstable with a probability of 9.07%. The possible cause of this misclassification is the scarcity of cases with a slope height of around 4 m in the training set. Conversely, Slopes 2 and 11 were unstable but predicted as stable, with probabilities of 64.68% and 56.76%, respectively. The likely reason is that these two slopes lie near the boundary between stable and unstable categories, making it difficult for the model to make a clear decision. These misclassified cases help us better understand the limitations of the ILB model. Interestingly, the ILB model's stability predictions for the four slopes at the Lala Copper Mine in Sichuan Province were  
Figure 10: The top view of Lala copper mine.  
above 97%, aligning with findings of [PERSON], [PERSON] and [PERSON] [55] and accurately reflecting the slopes' actual stability states.
###### Abstract
For instance, in the Sihui subway station in Beijing, thousands of bikes were parked around the station during rush hours, which occupied all the non-motorized space and an entire motorway, resulting in a large waste of public space and idle bikes. Conversely, suburban areas far from the city center often suffer from a short supply. Rebalancing shared bikes in the system is essential to fulfill commuter demand at different locations at different times. Compared with SBBS, the rebalancing operation of FFBS can be more challenging, as numerous scattered stations rather than fixed stations exist in this system, leading to higher operations costs [18]. These problems may influence bike-sharing users' safety and satisfaction, and challenge urban planning and transportation management. To improve the service quality and operation efficiency of FFBS, it is important to identify the causes of these problems by considering the following questions: Why does the imbalanced usage of bike-sharing as a feeder mode of rail transit occur? What are the spatial and temporal characteristics of these bike-sharing trips? What factors may influence the distribution of bike-sharing flows to rail stations? How can we improve the management of the bike-sharing system based on these revealed characteristics? This paper investigates and seeks to provide answers to these researchquestions. In China, the use of the mobile app-based smart lock equipped with GPS in the FFBS system makes it possible to acquire the entire sample data. Large amounts of data can be sensed and analyzed to uncover the operation patterns of FFBS and offer methods of smart management [19]. This study analyzed the operation characteristics of FFBS as a feeder mode to 277 subway stations in Bejing for a week. In the following sections, we proceed with a literature review of previous research on the connection between bike sharing and rail transit. Data collection and cleaning are described in Section 3. Based on a detailed GPS-Data analysis for the Mobike, temporal and spatial patterns are identified in Sections 4.1 and 4.2. Section 4.2 presents the results of cluster techniques and ANOVA analyses used to investigate how usage patterns of FFBS as a feeder to rail transit are shared across urban morphology and geographically distributed of subway stations in the city. The relationships between urban morphology, spatial layout, and accessing distance are also explored in Section 4.3. Finally, strategies for FFBS planning, parking, rebalancing and management around rail transit in Beijing are discussed in Section 5. This research revealed not only the operation characteristics of FFBS as a feeder mode to rail transit, but also how these patterns relate to and reflect the city's culture and spatial layout. The research results can contribute to a reasonable rebalancing of bike sharing and rational spatial layout around rail transit, integrating bike sharing and rail transit in an efficient way.
# Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination
###### Abstract  
The intensity of the reflected measuring beam is greatly reduced for laser-Doppler vibrometer (LDV) measurements on rough surfaces since a considerable part of the light is scattered and cannot reach the photodetector (laser speckle effect). The low intensity of the reflected laser beam leads to a so-called signal dropout, which manifests as noise peaks in the demodulated velocity signal. In such cases, no light reaches the detector at a specific time and, therefore, no signal can be detected. Consequently, the overall quality of the signal decreases significantly. In the literature, first attempts and a practical implementation to reduce this effect by signal diversity can be found. In this article, a practical implementation with four measuring heads of a Multipoint Vibrometer (MPV) and an evaluation and optimization of an algorithm from the literature is presented. The limitations of the algorithm, which combines velocity signals, are shown by evaluating our measurements. We present a modified algorithm, which generates a combined detector signal from the raw signals of the individual channels, reducing the mean noise level in our measurement by more than 10 dB. By comparing the results of our new algorithm with the algorithms of the state-of-the-art, we can show an improvement of the noise reduction with our approach.  
speckle; laser-doppler vibrometry; LDV; diversity reception; signal diversity; laser speckle 2021  
D
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Comparative Discussion and Trade-Offs
For example, eICIC is suitable for cost-sensitive SC deployments, CoMP is ideal for capacity-driven urban networks with strong infrastructure, and AI-based techniques offer long-term scalability and automation for UD or SONs. These comparative insights complement the analysis previously summarized in Table 7 (Section 3.4) and provide a contextual understanding of each technique's strengths and limitations for B5G applications. Figure 10 summarizes the trade-offs between major IMI techniques across key performance metrics. As illustrated, AI-based methods score high in scalability and SE but are challenged by complexity and data requirements [156; 157; 158; 159; 211; 212; 213; 214]. At the same time, techniques like eICIC and POC offer simpler, infrastructure-friendly solutions at the cost of adaptability or precision [156; 120; 164; 171; 186]. Figure 9: Conceptual pipeline of AI-based IMI in B5G networks, illustrating data flow from model training to RT adaptation using techniques like RL, DL, and FL. To further clarify applicability, Table 10 provides a deployment-based evaluation of IMI techniques across key scenarios, including urban dense, rural, infrastructure-limited, and aerial/NTN environments. The table highlights how techniques like CoMP thrive in well-connected urban settings [188; 209; 210; 211] but face barriers in rural deployments, while POC and eICIC remain viable in cost-constrained or low-infrastructure contexts [186; 120; 164; 171]. AI-based approaches offer versatile adaptability but are limited by high system demands [150; 152; 156; 215; 216; 217; 218; 219; 220].
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 3. Experimental Apparatus and Testing Methods
### Measuring Parameters of PDPA Experimental System  
The coordinate origin was established at the nozzle outlet, and the test area was designated between 5.0 cm and 15.0 cm along the Z-axis, as shown in Figure 5. This layout enabled the collection of detailed spatial and temporal data that are critical for analyzing dust-mist coupling behavior. The nozzle pressure was adjusted to control the mist's velocity and size distribution, while the suppressant materials and mass concentrations were varied to study dust and mist particle interaction laws.  
Figure 4: The fundamental elements of the PDPA system.  
A control variable methodology was applied during the experiments, wherein a single parameter was modified while others remained constant to isolate its impact [16]. The selected measuring parameters are shown in Table 2.  
This approach provided a rigorous framework for examining the influence of factors such as spray pressure, suppressant material, and mass concentration on the dust-mist coupling process. At each measuring site or point, at least 10,000 data points were gathered to provide a consistent amount of data and guarantee the accuracy of subsequent data processing [16; 31].