esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation Results and Discussion
### Performance Evaluation for User Influence  
For this test, 55,452 users who were evaluated in the restaurant category were extracted to verify the performance of the user influence. For all 55,452 users, the user influence was calculated based on the Yelp data attributes that map to the components of the user influence formula. The \(\alpha\) and \(\beta\) configurable parameters were all set to 1, which means that the weights for each component were equally set to 1 and the components were not normalized, to see how the component data characteristics would be affecting the influence.  
Among these users, the top 10 influential users were selected. Table 2 shows these top 10 influential users as well as some of the lowest 5 influential users. The user ID, calculated user influence, calculated user activity, number of elites, number of fans, number of reviews, and the recent review date are shown.  
These top 10 influential users' evaluations of restaurants were then compared against the average evaluation of all users on the same restaurants. If the evaluations of the top 10 influential users are similar to the average of all user evaluations, the top 10 influential users may be considered to provide credible evaluations.  
Figure 3 shows a comparison graph for each of the top 10 influential users. Each of the top 10 influential user evaluations were compared against the average evaluation of all users for each of the 10 or less restaurants most recently reviewed by each of the top 10 influential users. The restaurants are denoted as items in the graphs. The graphs show that the evaluation given by the top 10 influential users are very similar to the averageevaluations of all users. This shows that the top 10 influential users are providing an evaluation that has a very close pattern to the overall users.  
Figure 4 shows the difference between the evaluations by the top 10 influential users and the average of the evaluations by all users. Figure 4a shows the evaluation difference for each of the restaurants. The evaluation difference is very close to 0 for all the restaurants. This means that even for different restaurants the top 10 influential users do not display a big fluctuation and maintain consistent credibility. Figure 4b shows that the average of the difference for each top 10 influential user is within the +1 or \(-1\) range. This shows that the top 10 influential users can provide reasonably credible evaluations for restaurants.
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 2 State of the Art
### The Spatial and Temporal Structure of Road Traffic Noise in Large Urban Zones
#### 2.3.2 Classification of Roads in Large Urban Zones by Their Traffic Flow  
Mean hourly values of traffic flow in a given road stretch within the city of Milan are available thanks to the data provided by the EMTA, which is in charge of the traffic mobility management of the city (see [11; 65] for further details). The simulation model provides the traffic flow rate, or mean number of vehicles per hour, \(F(1)(t,s)\), at hour \(t\) of the day and road stretch location \(s\). We have found that it most convenient to take the mean daily total traffic flow, \(T_{T}\), as our candidate to classify traffic flow on road stretches. Specifically, we define our non-acoustic parameter, denoted simply as \(x\), according to  
\[x=\log_{10}(T_{T}). \tag{2}\]  
It is interesting to observe that one can also apply a clustering method, as we did for the noise in Section 2.3.1, to the traffic flow using the mean daily hourly flow rates produced by the Cadna-EMTA model. We will not discuss this possibility here, but, for details, we refer to [65]. In addition, other issues related to this model can be found in the literature [69; 70; 82].  
The knowledge of the non-acoustic parameter for each road stretch within the urban zone allows us, in particular, to associate a value of \(x\) with each road within the two clusters, denoted as \(C_{1}\) and \(C_{2}\), found from the analysis of traffic noise in Section 2.3.1. Therefore, we are in the position of obtaining the distribution functions, \(P_{1}(x)\) and \(P_{2}(x)\), within each cluster. The results are shown in Figure 4.  
Since the distribution functions are strongly overlapping, it is more appropriate to consider the probability that a given \(x\) value belongs to either \(C_{1}\) or \(C_{2}\), denoted as \(\beta_{1}\) and \(\beta_{2}\), respectively, i.e.,  
\[\beta_{1}(x)=\frac{P_{1}(x)}{P_{1}(x)+P_{2}(x)},\qquad\beta_{2}(x)=\frac{P_{2} (x)}{P_{1}(x)+P_{2}(x)}. \tag{3}\]  
The idea behind this representation is that one can 'interpolate' between the normalized mean cluster profiles, \(\overline{\delta_{k}}(t)\), \(t=n\tau\), in order to estimate the profile, \(\delta(t,x)\), on a road  
Figure 4: Distribution functions \(P_{1}(x)\) and \(P_{2}(x)\) vs. \(x\), for Clusters 1 and 2 [83; 84; 85; 86]. Here, \(x=\log_{10}(T_{T})\) is the non-acoustic index, with \(T_{T}\) representing the mean daily traffic flow of the given road stretch. The clusters \(C_{1}\) and \(C_{2}\) correspond to those obtained from the analysis of traffic noise displayed in Figure 3.  
stretch at site \(s\) characterized by a non-acoustic parameter \(x=x(s)\), which is given by the relation,  
\[\delta(t,x)=\beta_{1}(x)\overline{\delta_{1}}(t)+\beta_{2}(x)\overline{\delta_{2 }}(t). \tag{4}\]
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 3 Antimicrobial Activities of D-Limonene  
### Antibacterial and Antifungal Activity of D-Limonene  
D-limonene is a monocyclic monoterpene with excellent antimicrobial properties and has received considerable attention from researchers. Numerous studies have reported the inhibitory activity and mechanisms of action of D-limonene against bacteria and fungi. The MICs and minimum bactericidal/fungicidal concentrations (MBCs/MFCs) of 11 bacteria and 14 fungi are listed in Table 2 (colony forming unit, CFU), indicating that D-limonene has a relatively broad antimicrobial spectrum, and it can be found that the antimicrobial activity of D-limonene is correlated with microbial species, number of viable microorganisms and strains, and D-limonene concentration. In addition, a study found that D-limonene reduced the D-value (Decimal reduction Times) of _L. monocytogenes_ in carrot juice, but lipids and fibers led to significant changes in the D-value, suggesting that the food matrix interfered with the antimicrobial activity of D-limonene, but the mechanism is still unclear [69]. Other than the antimicrobial activity studies listed in Table 2, the inhibitory effect of D-limonene on multiple drug resistance (MDR) strains and its synergistic effect with antimicrobial drugs are also important.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Categories** & **Species** & **Strain** & **CFU/mL** & **MIC** & **MBC/MFC** & **Ref.** \\ \hline \multirow{8}{*}{Bacteria} & _Aeromonas hydrophilla_ & MF 372510 & \(1\times 10^{5}\) & 6.4 mg/mL & 6.4 mg/mL & [21] \\ \cline{3-6}  & & \(1\times 10^{6}\) & 16 \(\mu\)L/mL & 32 \(\mu\)L/mL & [70] \\ \cline{3-6}  & & \(1\times 10^{6}\) & 10 mg/mL & 40 mg/mL & [71] \\ \cline{3-6}  & & \(1\times 10^{8}\) & 1 \(\mu\)g/mL & - & [72] \\ \cline{3-6}  & & \(1\times 10^{8}\) & 12.5 \(\mu\)L/mL & - & [23] \\ \cline{3-6}  & & CIP 54127 & \(1\times 10^{6}\) & 12.5 mg/mL & - & [73] \\ \cline{3-6}  & & O157:H7 & - & 50 \(\mu\)L/mL & - & [74] \\ \cline{3-6}  & & CIP 4.83 & \(1\times 10^{6}\) & 12.5 mg/mL & - & [73] \\ \cline{3-6}  & & ATCC 6538 & \(1\times 10^{8}\) & 1 \(\mu\)g/mL & - & [72] \\ \cline{3-6}  & & \(1\times 10^{8}\) & 7.81 \(\mu\)g/mL & - & [23] \\ \cline{3-6}  & & ATCC 43300 & \(5\times 10^{5}\) & 3 mg/mL & 8 mg/mL & [75] \\ \cline{3-6}  & & ATCC 25923 & \(5\times 10^{5}\) & 3 mg/mL & 3 mg/mL & [75] \\ \cline{3-6}  & & \(5\times 10^{5}\) & 10 mg/mL & - & [76] \\ \cline{3-6}  & & ST30-t019 & \(5\times 10^{5}\) & 15 mg/mL & - & [76] \\ \cline{3-6}  & & ST5-t311 & \(5\times 10^{5}\) & 20 mg/mL & - & [76] \\ \cline{3-6}  & & - & \(5\times 10^{5}\) & 7.9 mg/mL & 12.9 mg/mL & [75] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The antimicrobial activity of D-limonene.
## 3 Methods
### Field Data Sets  
Model performance was assessed using two aggregate data sets. The first is a high quality data set of 23 stations from the Ligurian Sea, waters around the Marine Optics BuoY (MOBY) west of Lanai, the southern California coast, and the New York Bight, collected in 2008 and 2009 as part of NASA Spectral Ocean Radiance Transfer Investigation and Experiment (SORTIE) and NASA Ocean Color Validation (OCVAL) exercises. Radiometric closure was assessed in detail for these data by [PERSON] et al. [29]. [Chl] ranged from 0.24 to 23.85 mg m\({}^{-3}\). The VSF was directly measured using the custom Multi-Angle SCattering Optical Tool (MASCOT) [26; 36] and radiometric uncertainties were rigorously assessed by [PERSON] et al. [68]. Here we use WET Labs ac-9 absorption measurements corrected for scattering using independent VSF measurements (i.e., the VSF98P correction [29]; also see [PERSON] et al. [49] for detailed evaluation of this correction). The second data set is the NASA NOMAD database, where 80 data records were identified containing the parameters needed for performance assessments here, i.e., \(a\), \(b_{b}\), \(\widetilde{b}_{bp}\), \([\text{Chl}]\), and \(r_{rs}\). \([\text{Chl}]\) ranged from 0.23 to 10.68 mg m\({}^{-3}\) in this data set.
## 4 Discussion
### Evaluating Model Strengths and Weaknesses  
We hypothesized that closed-source models would outperform open-source models due to their access to extensive proprietary datasets and optimized architectures. This was largely confirmed, with models like GPT-01 and GPT-4o achieving the highest accuracy rates (88% and 83%, respectively), while open-source models such as LLaMA 3 70B struggled with lower accuracy (60.5%). However, Deepseek-R1 proved to be highly competitive, achieving an accuracy of 86% and narrowing the performance gap between the open- and closed-source models.  
These results suggest that performance is not solely dependent on access to proprietary data but also on architectural optimization and alignment strategies. The high Rasch-based ability estimates for GPT-01 and Deepseek-R1 reinforce that well-optimized models, even when open-sourced, can compete with their closed-source counterparts under certain conditions.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 4 DTN Routing Protocols in Disjointed Satellite Networks  
As discussed in [44; 46; 47; 48; 49; 50; 51], DTN routing protocols are categorized into three groups, namely: (i) flooding-based protocols in which an unlimited number of replicas for each generated bundle can be disseminated throughout the network; (ii) quota-based protocols in which the number of replicas is limited and fixed for every generated bundle; and, (iii) forwarding-based protocols where a single copy of messages/bundles is forwarded onto contacts until it reaches its destination or it has been dropped by the message dropping police. This final group implies that, at any given time, there is only one copy of each generated bundle throughout the network. Although forwarding-based protocols are considered to be resource-friendly protocols at the cost of producing long delays, flooding and quota-based protocols can provide better performance in terms of minimizing delay and increasing throughput. However, flooding-based protocols acquire high overheads because of the high rate of bundle replication and dissemination. This section discusses the features and limitations of these generic protocols and considers their application in CubeSat constellation and swarm environments. Table 4 shows the analysis of DTN routing protocols, which are discussed in [48].
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 4 Discussion
### Outlook of Pig Posture Detection Methods with Deep Learning  
Promising development and latent directions in pig posture detection methods can focus on the improvement of the algorithm and model themselves, full utilization of diverse data, and the connection with real-world issues to address current challenges and advance the feasibility for the broader application. Firstly, high accuracy and speed remain to be crucial factors to be considered in the future. Consideration of new deep learning optimization techniques, such as the genetic algorithm-based cuckoo search algorithm that has been proven to be highly effective in the classification of fish [94], may be needed in the future. Then, the model's computation complexity and resource demand should be reduced, making it applicable for low-computation-power platforms, such as mobile devices. Additionally, future research should focus on the identification and tracking of multiple pigs throughout monitoring while accurately detecting the posture of each pig, and occlusion handling must be considered for tracking continuity and temporal information analysis to allow deeper insights into the postures each pig shows. Furthermore, to support the advancements of research within or even beyond this field, a large-scale, standardized, and comprehensive database should be constructed. On the one hand, unified datasets can conduce to the validation and benchmarking for evaluating and comparing different deep learning models. On the other hand, an all-inclusive database should encompass diverse attributes related to pigs, such as pig breeds, pig physical condition, pig movement history, and breeding conditions, in addition to the pig images or videos. To leverage these different types of information, multi-modal learning technology should be introduced and adopted to current deep learning models to strengthen the evaluation results.
## 3 Basic Concepts
### K-Harmonic Means  
K-Harmonic means (KHM) was proposed by the authors in [18] as a new clustering method based on k-means. In this algorithm, the harmonic means are used instead of the Euclidean distance to solve the initialization problem of the KM algorithm.  
The objective function of the KHM algorithm is named KHM, and it calculates the harmonic mean of the distance from each point to all centers. KHM investigates two functions, soft membership and weight. The weight function assigns a higher weight to data pointsthat are far away from every center, defining the impact of each data point on computing new components of the cluster center. Parameter \"\(\rho\)\" is a user-defined input parameter in KHM, typically equal to or greater than 2. It influences the fuzziness of cluster assignments. A higher value of \"\(\rho\)\" results in smoother membership distributions and allows data points to have more evenly distributed memberships across multiple clusters. Conversely, a lower value of \"\(\rho\)\" leads to sharper cluster boundaries and more distinct memberships.  
The algorithm continues until a predefined number of iterations is reached, or until the output of the KHM objective function does not change significantly.
# Structural Crack Detection and Recognition Based on Deep Learning
## 2 Experimental Data Preparation  
### Image Acquisition  
Although there is much research on crack detection in academia, there is no unified crack image dataset. Therefore, to train the crack detection and recognition model, it is necessary to manually collect crack pictures. The data in this experiment are based on camera collection, and the type of cracks targeted is concrete cracks. The camera is used to manually take pictures on roads and bridges to collect crack pictures. A total of 2000 crack pictures were collected, including four types of strong light, moisture, distortion, and darkness. The picture resolution is 1024 \(\times\) 1024 pixels in the format of JPG.
# Buffeting Response Prediction of Long-Span Bridges Based on Different Wind Tunnel Test Techniques
## 4 Test Arrangement
### Aerodynamic Admittance Identification Test  
The traditional buffeting response calculation adopts the 2D one-wavenumber aerodynamic admittance combined with the correlation of the fluctuating wind. According to the research of [PERSON] et al. on the influence of the three-dimensional effect, the commonly used aerodynamic admittance identified through sectional model tests is the generalized 3D one-wavenumber aerodynamic admittance. It is necessary to use the correlation of the buffeting force to calculate the buffeting response of long-span bridges. In recent years, based on studies by [PERSON] et al. [26] and [PERSON] et al. [27; 28; 29], it was found that, in addition to the ratio of the turbulence integral scale to the structure width, the aspect ratio is also an important parameter that controls the calculation accuracy of the buffeting response of long-span bridges. Even if the integral scale is close to or even slightly smaller than the structure width, as long as the structural aspect ratio is sufficient, the influence of the three-dimensional effect is very small, the strip assumption is valid, and the calculation of the buffeting response will also have high accuracy. At this time, the correlation of the fluctuating wind can be used instead of that of the buffeting force. Therefore, to minimize the influence of the three-dimensional effect, in the test identification of the aerodynamic admittance in this section and the integrated transfer function in the next section, under the premise that the test was conducted in the spire turbulence field with a large integral scale, a sectional model with a relatively large aspect ratio was used. The model was 1.35 m long and 0.15 m wide.  
Figure 8: Three-component force test of sectional model.  
Figure 9: Aerodynamic derivative identification test.  
The identification of the 2D aerodynamic admittance was carried out in a spiere-generated turbulent flow using the high-frequency force-balance measurement technique. The spires were 0.198 m and 0.160 m in width at the base and top, respectively, and 2.0 m in height. The distance between the spires was 0.8 m. The characteristics of turbulent wind field were measured using the TFI Cobra Probe. The three section models were supported on a steel platform approximately 4.5 m downstream of the spires, as shown in Figure 10. The test model was divided into three parts, namely the force model, two end plates (0.4 m \(\times\) 0.3 m) and their connected pseudo-models (0.1 m in length). The gap between the pseudo-models and the force model was 1 mm. In addition, the balance and connections were covered by a fairing to prevent the influence of the incoming flow. The force measurement model was horizontally mounted at a 0-attack angle on the high-frequency dynamic balance.
###### Abstract  
Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel deep-reinforcement-learning-based approach for DASH streaming, with the primary focus of maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes quality of experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate our approach through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. We also conduct a comparative analysis with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.  
DASH; video streaming; wireless networks; QoE; deep learning; ABR; reinforcement learning algorithms; deep reinforcement learning; bandwidth estimation +
_Applied_  
Footnote †: journal: _Applied_
## 3 Results and Discussion
### Cybersecurity and Privacy in Energy Management Systems
#### 3.5.2 Secure Communication Protocols and Privacy Techniques  
To reduce cybersecurity risks, several communication protocols and privacy protection methods have been proposed. Encryption protocols, including Transport Layer Security (TLS) and Internet Protocol Security (IPsec), help secure data exchanges between DERs and control centers [15; 23].  
Additionally, privacy protection strategies such as differential privacy and homomorphic encryption allow the analysis of aggregated data without exposing individual consumption patterns or sensitive operational information [31; 32].
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 6 Model Test Result
### Prediction Performance  
For the above network with four structures, the prediction accuracy of four threat levels is analyzed. The prediction error is set to be different between the actual predicted value \(\mathit{u}_{i}\) and the target value \(\mathit{u}_{i}\). Figure 9 shows the boxplot of prediction error under different EMI threat levels, which is used to compare the distribution interval and dispersion degree of the prediction error. The interval of 25-75% shows 50% of the error distribution, indicating the concentration of the error distribution. Similarly, the range of 5-95% shows 90% of the error distribution, indicating the main distribution range of prediction error.  
Figure 8: Confusion matrix.  
From the perspective of different EMI threat levels, the error distribution of the lose lock level is the most concentrated and the prediction accuracy is the highest. 50% of the error is distributed in [\(-0.2\) dB, \(0.3\) dB] and 90% in [\(-1.6\) dB, \(0.6\) dB] with less deviation compared to the other three cases. The distribution of errors in the risky level is more dispersed with relatively large deviation values, more than 3 dB. The double-input network with the inputs of VPP and constellation diagram has larger outliers, and the 90% error distribution of the three-channel network with the inputs of VPP, constellation diagram, and STFT is relatively wide. In contrast, the error distribution of the network using NDC is more concentrated, of which the mean and median are closer to 0, and 5-95% of the error is concentrated in [\(-1\) dB, \(1\) dB]. Among them, the three-input network with NDC has the best prediction performance.  
### Prediction Performance  
In order to compare the performance of the proposed MIMT-CNN and the existing state-of-the-art model under multi-task networks, we replace the part of feature fusion processing in Figure 6 with four classical CNN structures (AlexNet, GoogLetNet, VGG, ResNet), and use the same data set to train the model. The performance of MIMT-CNN and classical CNN structures on the training set is shown in Figure 10. Although the threat prediction RMSE of MIMT-CNN is slightly worse than that of AlexNet, MIMT-CNN has the highest classification accuracy of EMI among the classical models. On the whole, MIMT-CNN achieves the best balance between classification and prediction performance.  
Figure 9: Prediction error under different EMI threat levels.  
Some typical areas of interference identification and threat prediction models are listed in Table 5. It can be concluded that CNN is a commonly used model in the field of interference identification and classification evaluation and has good results under single-task application. In the case of multi-task, the accuracy and RMSE of MIMT-CNN reaches a good level.