esa-sceva/satcom-chunk-collection huggingface dataset:
# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 2 Algorithms and Methods
### Multimodal Large Language Model (MLLM)
The summarized insights also highlight ongoing challenges and research directions related to adaptability, interpretability, and computational efficiency. Figure 21: Overview of CityLLaVA. The method is anchored on the pre-trained LLaVA-1.6-34B [64] equipped with block expansion [65], combining the textual prompt engineering and visual prompt engineering guided by bounding boxes. From [63]. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Method** & **Specifications** & **Advantages** & **Disadvantages** & **Applications** \\ \hline \multirow{4}{*}{ML [13, 14, 16]} & Events by trajectories & Automation & Sensitivity to parameters & vehicle tracking \\  & Feature extraction & Adjustable & Need for large datasets & Traffic flow analysis \\  & Unsupervised learning & Detects subtle anomalies & Complex results & Early warning systems \\  & Anomaly by trajectory analysis & & & Complex algorithm & \\ \hline \multirow{4}{*}{CNN [17, 18, 20, 27, 30, 31, 32, 34, 36, 37, 38]} & Expert architecture & & & \\  & Convolutional layers for local patterns, filter, and feature maps & Robust & Need large datasets & Surveillance video analysis \\  & & Patterns, filter, and feature maps & Machine learning & Complexity & Traffic signal control \\  & Spatio-temporal feature & Versatility & Quality of data affects performance & Accident detection \\  & Anomaly detection from deviations & & & \\ \hline \multirow{4}{*}{GAN [40, 42, 43, 44]} & GAN architecture & & & \\  & Generative makes predictions of normal sequence & Generation of realistic synthetic data for training & & \\  & Problogs, consensus & Detection of variety of anomalies & & \\  & Generative inference for predictions. & Robustness & Complex evaluation. & Data augmentation \\  & Anomaly identified as large difference. & & & \\ \hline \multirow{4}{*}{Transformers [45, 46, 47, 48, 49, 52]} & Self attention mechanism analyze & Capture complex relationships between events & & \\  & Encoder-decoder architecture & Superior performance & Need large datasets & & \\  & Object relationships between patients & Parallel higher efficiency & Complex interpretability & \\  & _Fewer annotations_ & Applicability & & \\ \hline \multirow{4}{*}{MLLM with VLM combination to analyze data.} & LLM with VLM combination to analyze data. & Ability to understand content from multiple sources and deeper analysis & Training and inference computationally expensive & Multimodal urban surveillance \\  & & Analysis & Limitations in understanding the order and complex relationships & Situation-aware traffic reporting \\ \cline{1-1}  & & Helity scalable & MLLs can generate incorrect & \\ \cline{1-1}  & & Helity scalable & Outputs or hallucinations’ & \\ \cline{1-1}  & & & Limited generalization & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the various traffic anomaly detection methods based on insights from reviewed studies.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 3 State Representation
### Problem-Specific  
Certain measurements only make sense considering the problem tackled. Energy management problems care about energy demands [132, 198, 213, 225], energy levels [100, 214, 229] and consumption [201, 203].  
For network problems, many specific metrics are used, for example, signal-to-noise ratio [46]; resource backhaul transmission volume [91]; and resource radio channel quality [68].  
For maintenance-related problems, specific state features include machine maintenance duration [45; 116] or resource degradation level [48; 89].
# QRS Detection Based on Medical Knowledge and Cascades of Moving Average Filters
## 2 Materials and Methods
### Methods
#### 2.2.2 Derivative Filter Selection
Let \(\overline{\text{D}}^{0}\) be the trimmed mean of the derivative maxima during the initialization phase. Let M be the maximum value of the derivative signal corresponding to the last successful detection. Let us consider \(\overline{\text{D}}^{n}\) as a variable that reflects the average of the local derivative maxima at detection \(n\). Sudden rising spikes in M could interfere with the detection process, and therefore changes to M are limited  
\[\text{M}_{b}=\text{max}\Big{(}\text{min}\Big{(}\text{M},\ 1.5\ \overline{\text{D}}^{0} \Big{)},0.5\ \overline{\text{D}}^{0}\Big{)} \tag{2}\]  
Figure 6: Stages of the detection process. (**a**) Raw input ECG signal, plagued by baseline wander and network interference. (**b**) Noise rejection with the application of MAC(0.02, 0.008)–MAC(0.12, 0.024). (**c**) Derivative of the signal resulting from the application of the selected dMAC. (**d**) Rectification of the derivative. The threshold T is updated in the following fashion:  
\[\overline{\text{D}}^{n+1}\ =\max\Big{(}0.2\ \overline{\text{D}}^{0}\,\min\Big{(}\ 0.97\ \overline{\text{D}}^{0}\ +(1-0.97)\text{M}_{b},\ 2.5\ \overline{\text{D}}^{0}\ \Big{)}\ \Big{)} \tag{3}\]  
\[\text{T}^{n+1}=\text{c}\ \overline{\text{D}}^{n+1} \tag{4}\]  
where c is a control parameter operating on the detection threshold. The term \(\ \overline{\text{D}}^{n}\) estimates the local average of the QRS complex derivative maxima at detection \(n\). The value 0.97 that multiplies \(\ \overline{\text{D}}^{n}\) determines the memory factor of recursive updating. Additionally, the 0.2 \(\overline{\text{D}}^{0}\) limit prevents \(\overline{\text{D}}^{n}\) from diminishing to zero while the 2.5 \(\overline{\text{D}}^{0}\) limit prevents excessive overshoots. After each successful detection, the value of T is set to 2.4 times its new value to prevent detecting more than once the same heartbeat. This value decreases as the time elapsed from the last QRS detection increases. Occasionally, the threshold T\({}^{n+1}\) could get excessively high and prevent detection of the following QRS complexes. For example, when a premature ventricular contraction is followed by normal sinus beats of lower amplitude. To avoid this occurrence, the threshold value T continues to decrease as the time with no detections increases. These temporal thresholds are dependent on the mean interval between two consecutive R peaks, the mean RR. As the mean RR decreases, the threshold evolves faster. Figure 7 shows the decreasing dynamic of the detection threshold for a sample mean RR interval of 1000 ms. The gray area denotes a refractory period, where no detection is registered. The time signature or fiducial point of the detected QRS complexes is placed on the maximum signed derivative of the QRS complex. During the initialization phase, the algorithm chooses the positive or negative derivative depending on their maximum amplitude. This choice is kept throughout the record.
###### Abstract  
To manage noise pollution, cities use monitoring systems over wireless acoustic sensor networks. These networks are mainly composed of fixed-location sound pressure level sensors deployed in outdoor sites of the city for long-term monitoring. However, due to high economic and human resource costs, it is not feasible to deploy fixed metering stations on every street in a city. Therefore, these continuous measurements are usually complemented with short-term measurements at different selected locations, which are carried out by acoustic sensors mounted on vehicles or at street level. In this research, the application of artificial neural networks is proposed for estimation of the long-term environmental acoustic pattern of a location based on the information collected during a short time period. An evaluation has been carried out through a comparison of eight artificial neural network architectures using real data from the acoustic sensor network of Barcelona, Spain, showing higher accuracy in prediction when the complexity of the model increases. Moreover, time slots with better performance can be detected, helping city managers to deploy temporal stations optimally.  
supervised learning; artificial neural networks; big data; wireless sensor network data; knowledge discovery; urban acoustic environment; environmental noise assessment Article
## 4 Methodology
### Domain Clustering Based on Multi-Path Transmission
#### 4.2.5 Cooperative Resource Allocation Game  
Resource allocation in multi-user, multi-node satellite networks is formulated as a cooperative game to address resource competition and collaboration, ensuring fair and efficient distribution among users and nodes. The cooperative game model enhances the overall network utility by balancing global and individual optimality through shared resources.  
Consider a cooperative game with \(N\) participants. The utility function \(U_{i}\) for participant \(i\) is defined as  
\[U_{i}=f(P_{i},B_{i},S_{i},R_{i}) \tag{42}\]  
where \(P_{i}\) is the power allocated to participant \(i\), \(B_{i}\) is the bandwidth allocated to participant \(i\), \(S_{i}\) is the signal-to-noise ratio (SNR) for participant \(i\), and \(R_{i}\) is the throughput for participant \(i\).  
The interference between participants \(i\) and \(j\) is denoted by \(\epsilon_{i,j}\). The resource allocation strategy aims to maximize the total network utility, while ensuring each participant's utility meets a minimum required level:  
\[\max\sum_{i=1}^{N}U_{i}\]  
s.t.  
\[U_{i}\geq U_{\min,i},\quad\forall i\]  
\[\sum_{i=1}^{N}P_{i}\leq P_{\text{total}}\]  
\[\sum_{i=1}^{N}B_{i}\leq B_{\text{total}}\]Revenue sharing is implemented using the Shapley value, to ensure a fair distribution of resources based on each participant's contribution. The Shapley value is a widely recognized method for fair allocation in cooperative games, calculated as  
\[\phi_{i}(v)=\sum_{S\subseteq\overline{N}\setminus\{i\}}\frac{|S|!(|N|-|S|-1)!}{| N|!}(v(S\cup\{i\})-v(S)) \tag{43}\]  
where \(v(S)\) is the utility of coalition \(S\), \(|S|\) is the number of participants in coalition \(S\), and \(\phi_{i}(v)\) is the Shapley value for participant \(i\).  
The Shapley value ensures that each participant receives a fair share of the total utility based on their marginal contribution to all possible coalitions.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities
#### 4.2.5 Natural Disasters  
These disasters can be the result of severe weather conditions as a result of severe weather conditions such as extreme temperatures, heatwaves, ice storms, sand/desert storms, and high winds or catastrophic events caused by natural forces such as geological changes from seismic activities, such as earthquakes (Chile 2010 [109] and Turkey-Syria 2023 earthquakes [110]), hurricanes (Katrina 2005 [111] and Maria 2017 [112]), tornadoes (Kissimmee 1998 [113], 2008 Super Tuesday tornado [114], and the tornado outbreak of April 2014 [115]), tsunamis (Samoa tsunami 2009 [116], Indian Ocean 2004 [117], and 2011 Tohoku Tsunami [118]), floods (East Africa 2018 [119], North Korea [120], India [121], South East Asia 2011 [122], and China [123]) and wildfires (Victorian Black Saturday bushfires 2009 [124] and Attica Wildfires 2018 [125]). Such events severely affect the smart grid's functional and operational activities, significantly impacting the grid infrastructure's resilience, such as physical damage to power lines and substations, underground cables and equipment, transmission and distribution of electricity, and public safety. This can be mitigated using enhanced monitoring and early-warning systems, advanced equipment design and construction, proactive maintenance and vegetation management, integration of microgrids and renewable energy sources, and efficient coordination and communication between grid operators and first emergency/incident responders. However, with the presence of various smart-grid-related vulnerability types, numerous attacks are likely to occur, with some being more frequent and more damaging to the integration of smart grids in IoT. As a result, they will be presented in the following section.
###### Abstract  
In order to study the effect of carbon nanotubes (CNTs) on the flame retardancy of carbon fiber (CF)/epoxy resin (EP) composites, CF/EP and CNTs/CF/EP composites were prepared by solution blending. The flame retardancy and thermal stability were studied by cone calorimetry and thermogravimetric analysis. It was found that CNTs and CF had a certain synergistic effect on improving flame retardancy and thermal stability of EP. The peak heat release rate of F7N7, which represents the EP composites with 0.7 wt % CF and 0.7 wt % CNTs, was minimal. The total smoke production of F5N5 which represents the EP composites with 0.5 wt % CF and 0.5 wt % CNTs was the smallest, which was decreased by 43.04% more than the EP. The initial decomposition temperature of F7N7 was about 14 \({}^{\circ}\)C higher than that of F7, and the mass loss at T\({}_{\text{max}}\) was greatly reduced. The apparent activation energy of F7N7 is 2.7 kJ-mol\({}^{-1}\) more than EP. Finally, the tensile and flexural strength of the composites were also improved, so it could be applied to a high-performance matrix of CF/EP composites, which are usually used as the advanced composites in the aerospace field.  
carbon nanotubes; carbon fiber; epoxy resin; flame retardancy; thermal stability +
Footnote †: journal: Applied Physics Letters
## 5 Thermal Damage
### Multivariate Regression Model  
The imputed dataset is used to derive an empirical equation (\(\rm TD_{\rm emp}\)) that can correlate thermal damage with its influencing factors in a more comprehensive way. The correlation between the parameters \(\rm P1\)-\(\rm P10\) and \(\rm TD_{\rm emp}\) was determined using SPSS (version 24), and after several trials, the following equation was obtained. The coefficient of determination \(\rm R^{2}\) was 0.66. The equation can be described as follows:  
\[\rm TD_{\rm emp}=0.09+0.0003\cdot P1+0.002\cdot(P2+P3+P4)+0.024\cdot P7-0.0003 \cdot P9-0.036\cdot P10 \tag{2}\]  
The equation indicates that \(\rm TD_{\rm emp}\) is directly proportional to the temperature, mineral content, and porosity of granite specimens, while higher UCS and tensile strength have an opposing effect. By observing the parameters' coefficients, the mineral contents (quartz, feldspar, and biotite) may have almost the same impact on the overall TD, while porosity has the most influence on TD among all parameters. Finally, UCS and BTS have negative effects on TD. In other words, strong granite specimens tend to have lower TD, which makes more sense.  
Figure 4 shows a good correlation between the actual (defined in Equation (1)) and predicted TD. Hence, the proposed equation can estimate the thermal damage accurately,  
Figure 3: The correlation between the predicted thermal damage (\(\rm TD_{\rm in}\)) and (**a**) P-wave velocity and (**b**) tensile strength.  
where R2 is 0.59. Since a fair agreement of the coefficient of correlation R = 0.77 was reached, in the next section, machine learning algorithms are implemented to enhance the results.
## 2 Materials and Methods
### Experiment on the Separation of Raisins from Adhered Foreign Object
#### 2.1.4 Analysis of Experimental Results

Table 2: Average hardness of raisins after rapid freezing (g). \begin{table}
\begin{tabular}{c c c c c} \hline
**Quick-Freezing Temperature (\({}^{\circ}\)C )** & **0-Day** & **5-Day** & **10-Day** & **15-Day** \\ \hline
0 & 1079.53 & 1073.17 & 1058.13 & 1041.18 \\ \(-\)10 & 1081.45 & 1077.33 & 1061.47 & 1046.09 \\ \(-\)20 & 1084.78 & 1082.89 & 1064.08 & 1049.45 \\ \(-\)30 & 1085.59 & 1080.21 & 1068.76 & 1055.72 \\ \(-\The experiment employed two separation methods: tapping and rolling extrusion. The results of separation rate, damage rate, and residue rate are presented in Figure 6, Figure 7 and Figure 8, respectively. At \(-40\)\({}^{\circ}\)C, the rolling extrusion method achieved a separation rate of 88.02%, which was significantly higher than the 72.36% obtained by the tapping method. As temperature decreased, separation rate gradually improved, indicating that rapid freezing enhances separation effectiveness. The rolling extrusion method demonstrated a lower average damage rate (7.32%) and residue rate (3.58%) compared with the tapping method (12.55% and 6.91%, respectively), suggesting reduced damage to raising and fewer foreign object residues. The tapping method's concentrated impact force leads to excessive localized stress, thereby increasing the risk of material damage. Figure 5: Average tensile force required for separating different foreign materials adhered to raising surfaces under varying quick-freezing temperatures. Figure 6: Separation rate of materials subjected to different quick-freezing temperatures using various separation methods. In summary, the optimal approach is the combination of quick-freezing treatment at \(-40\)\({}^{\circ}\)C and rolling extrusion separation. This method ensures the integrity of raisins while achieving efficient separation of adhered foreign objects. After restoration to ambient temperature, it exerts minimal impact on the raisins themselves, thereby preserving their quality.
## 4 Analysis of DoF and User Scaling
### User Scaling Law
Since the term \(\sum_{i=1}^{M}\left|h_{\pi j}\right|^{2}\) corresponds to the uplink scheduling metric \(\gamma_{j}\) with no path-loss component and is the chi-square random variable with \(2M\) degrees of freedom for \(j\in\mathcal{S}^{(u)}\), Equation (21) can further be lower-bounded by  
\[P_{u} \geq 1-\lim_{SNR\rightarrow\infty}\sum_{i=0}^{M-1}{N-M\choose i}F \left(\frac{\epsilon_{2}\text{SNR}^{-1}}{M}\right)^{i}\cdot\left(1-F\left( \frac{\epsilon_{2}\text{SNR}^{-1}}{M}\right)^{i}\right)^{N-M-i}\] \[= 1-\lim_{SNR\rightarrow\infty}\sum_{i=0}^{M-1}{\frac{\left(N-M \right)!}{i!\left(N-M-i\right)!}}\frac{F\left(\frac{\epsilon_{2}\text{SNR}^{-1 }}{M}\right)^{i}\cdot\left(1-F\left(\frac{\epsilon_{2}\text{SNR}^{-1}}{M} \right)\right)^{N-M}}{\left(1-F\left(\frac{\epsilon_{2}\text{SNR}^{-1}}{M} \right)\right)^{i}}\] \[\geq 1-\lim_{SNR\rightarrow\infty}\sum_{i=0}^{M-1}{\frac{\left\{(N-M )\cdot F\left(\frac{\epsilon_{2}\text{SNR}^{-1}}{M}\right)\right\}^{i}\cdot \left(1-F\left(\frac{\epsilon_{2}\text{SNR}^{-1}}{M}\right)\right)^{N-M}}{ \left(1-F\left(\frac{\epsilon_{2}\text{SNR}^{-1}}{M}\right)\right)^{i}}}\] \[\geq 1-\lim_{SNR\rightarrow\infty}\sum_{i=0}^{M-1}{\frac{\left\{(N-M )\cdot C_{u2}\text{SNR}^{-M}\right\}^{i}\cdot\left(1-C_{u1}\text{SNR}^{-M} \right)^{N-M}}{\left(1-C_{u2}\text{SNR}^{-M}\right)^{i}}},\]  
where  
\[C_{d,2} = \frac{2^{-(M-1)}}{M\cdot\Gamma(M)}\cdot\epsilon_{1}^{M}. \tag{18}\]0  
and  
\[C_{d,2} = \frac{2^{-(M-1)}}{M\cdot\Gamma(M)}\cdot\epsilon_{1}^{M}. \tag{18}\]1  
It is not difficult to show that if \(N=\omega\left(\text{SNR}^{M}\right)\), then two terms \(\left(1-C_{d,1}\text{SNR}^{-M}\right)^{N}\) and \(\left(1-C_{u,1}\text{SNR}^{-M}\right)^{N-M}\) decrease exponentially with respect to SNR, whereas other two terms \(\left(NC_{d,2}\text{SNR}^{-M}\right)^{i}\) and \(\left\{\left(N-M\right)C_{u,2}\text{SNR}^{-M}\right\}^{i}\) increase polynomially for any \(i>0\). In consequence, as SNR goes to infinity, both \(P_{d}\) and \(P_{u}\) tend to one. Hence, from Equation (14), \(\text{DoF}_{d}\geq M\) if \(N=\omega\left(\text{SNR}^{M}\right)\), which completes the proof of this theorem. Our main result is now compared with the achievability result in [26] with respect to the user scaling law. **Remark 2**. : _In the multi-antenna full-duplex system consisting of a full-duplex BS having \(2M\) antennas (\(M\) transmit and receive antennas each) and a set of \(N\) half-duplex MSs with a single antenna each, it was shown in [26] that the optimal DoF is achievable by using opportunistic scheduling at the downlink MSs and random selection of the uplink MSs, provided that \(N\) scales faster than SNR\({}^{2M-1}\). In this work, we have proposed the hybrid opportunistic scheduling method such that both the uplink and downlink MSs are opportunistically selected, thereby resulting in the reduced number of MSs required to achieve the optimal sum DoF (i.e., 2M DoF). Note that our scheduling method does not utilize any further CSI at the transmitters, compared to that of [26].
## 5 Discussion and Conclusions  
We presented a cross-layer queuing model for packets with priority and deadlines that is applicable in an MU-MIMO downlink operating over a single band. We described the RPS scheduling policy that combines maximizing the reward per second approach with ZFBF precoding to design jointly the beamformer and the scheduling algorithm. Simulations demonstrated that the proposed technique performed significantly better in this context compared to existing techniques. Thus, designing a cross-layer, joint scheduling, and precoding can significantly improve the performance of the transmission of packets with deadlines and priorities. Extension of the proposed technique to OFDM systems (here, beamforming was done independently at each frequency) is an interesting research direction. However, the insights provided by the current paper allow one to consider joint scheduling and beamforming in the downlink of OFDM systems such as 5G and WLAN.  
Figure 16: CDF of cumulative rewards (\(\lambda_{a}=66\),000). FIFO; EDF; Greedy; RPS.  
Figure 15: CDF of cumulative rewards (\(\lambda_{a}=42\),000). FIFO; EDF; Greedy; RPS.  
**Author Contributions:** Conceptualization, A.L. and L.-o.R.; methodology, A.L. and L.-o.R.; software, L.-o.R.; validation, L.-o.R.; formal analysis, L.-o.R.; investigation, L.-o.R.; resources, L.-o.R.; data curation, L.-o.R.; writing-original draft preparation, L.-o.R.; writing-review and editing, L.R and A.L.; visualization, L.-o.R.; supervision, A.L.; project administration, A.L.; funding acquisition, A.L..  
**Funding:** This paper was partially supported by the Israeli Innovations Authority as part of the HERON 5G Magnet Consortium and ISF-NRF Research Grant No. 2277/2016.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Using Statistical Shape Models to Optimize TKA Implant Design
## 3 Results
### Generation of the SSM: Validation  
* For the SSMs used in this study 99% of the Vectorization Errors were less than 0.5 mm and the assumption of normally distributed populations was not rejected.
* Convergence Test: Results of the Convergence Test are exemplified for the Asian femur SSM. The effect of increasing the population size (to up to 112 patients) on the resulting average model is depicted Figure 3. Convergence is not achieved, as the mean RMS error does not reach zero before the populations begin to overlap (dotted line). However, with 80 patients (thus 24 overlapping scans), the error is less than 1.5 mm.  
Leave-One-Out Analysis  
Results of the leave-one-out study are exemplified for the Asian femur SSM. With 112 patients it was possible to predict patients with an RMS error below 1.5 mm using 15 modes of variation (Figure 4). Using all modes of variation, the model is able to predict patients with a mean accuracy of about 1 mm.  
Figure 3: Mean RMS deviation between two Asian femur SSMs computed with different input data, for an increasing number of input patients (set size).