esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Dimensions of Co-Located Multi-Device Audio Experiences
## 5 Design Dimensions
### Synchronisation  
Synchronisation is defined as the extent to which the devices in the ecosystem are synchronised in their reproduction of audio. This dimension appropriates and extends [PERSON]'s _temporal_ dimension [3] for synchronous, multi-device interactions through three categories of synchronisation for audio content.  
1. **Asynchronous** --In this instance, the devices are not synchronised in any form, acting completely independently from each other. The applications are limited, but asynchronous systems can be used for experimental art installations [35] and enchanted object experiences [36].  
Figure 2: Diagram illustrating the methodology of obtaining the design dimensions from the original data. The thematic analysis followed [PERSON] and [PERSON]’s methods [31]. The relevant literature [3, 7, 34] was considered when forming the design dimensions.  
2. **Loose synchronisation--**Loosely synchronised audio can be achieved through artistic composition, without the use of device networking. The audio material is designed to be played at approximately the same time but without the need for precise timing. Many early multi-device audio experiences were limited to this approach, which typically involved listeners pressing play on devices at the same time [37] or other manual triggering of audio sources [38]. The synchronisation accuracy between devices is typically between 100 ms and 1 s.
3. **Tight synchronisation--**This category captures experiences where devices are connected and synchronised using a network. This approach is common in more modern experiences and enables musical performance applications [39] and narrative-driven experiences such as audio dramas [21]. For such applications, experiences in this category require device synchronisation of <100 ms or from word-level accuracy down to approximately frame-level synchronisation [8]. Typical synchronisation solutions for these experiences obtain a sync accuracy of around 20 ms for heterogeneous devices after calibration [40].
## 2 Materials and Methods  
### Study Site and Growth Condition  
The study was conducted under greenhouse conditions in the continental region of Croatia (2022, N45\({}^{\circ}\)09\({}^{\prime}\) E18\({}^{\circ}\)17) in two growing cycles with the same study treatments and methods. The greenhouse area is 240 m\({}^{2}\) (8 m \(\times\) 30 m), positioned in a south-north direction with an automatized ventilation system. Lettuce seedlings (_Lactuca sativa_ L.) were grown from conventional seeds in seedling containers made of styrofoam, dimensions \(537\times 328\times 65\) mm. Containers are disinfected before use. One seed was sown in each section, which enabled uniform germination and sprouting as well. In this part of the study, the plants were watered with the same amount of water to enable the initial growth to be as uniform as possible.  
The containers were filled with commercially available substrate (Klasmann potgrond H, Klasmann-Deilmann GmbH, Geeste, Germany), a mixture of frozen black and fine white sphagmup peat with added water-soluble nutrients and microelements. Peat has a fine structure (\(\leq\)27 mm) with a 6.0 pH reaction (H\({}_{2}\)O) and the following composition: 210 mg/L N, 150 mg/L P2O5, 270 mg/L K2O, 150 mg/L S, and 100 mg/L Mg. Total porosity (82 \(\pm\) 2%) was measured indirectly via saturation during moisture release curve development. Two widespread types of lettuce (G), that is iceberg lettuce (G\({}_{\rm{IL}}\)) and butterhead lettuce (G\({}_{\rm{BL}}\)) were used in the study. Three-week-old seedlings were transplanted into growing pots with a capacity of 3 L, in the first growing cycle on 17 March, and in the second growing cycle on 28 April 2022. After transplanting, the plants in all treatments were watered with equal irrigation rates (0.29 L/pot) to enable good rooting and uniform initial growth. Lettuce was fertilized with organo-mineral foliar with following composition: N (6.6 g/L), P2O5 (13.3 g/L), K2O (13.3 g/L), Ca (2 g/L), Mg (4 g/L), Fe (17.2 g/L), Zn (26.5 g/L), Mn (13.3 g/L) and Cu (13.3 g/L). Lettuce was fertilized on 28 March and 11 and 25 April in the first growing cycle, and 20 April and 30 and 10 May in the second growing cycle. Lettuce protection was achieved by the application of pesticides. During the study, air temperature and humidity were recorded every ten minutes (Data logger, Axiomet AX-DT200).  
The 3-factor study was set up in three replications with five plants within the treatment and with the following study factors: G--genotype: G\({}_{\rm{IL}}\) = iceberg lettuce (Great lakes), G\({}_{\rm{BL}}\) = butterhead lettuce (Trocadero Marta), I--irrigation rate: I\({}_{100}\)--control treatment (100% water holding capacity (WHC); I80--80% WHC and I60--60% WHC, and M--mycorrhizaE: NM--control treatment (nonmycorrhizal, without mycorrhizaE), MT--mycorrhizal treatment (added mycorrhizae). In total, there were 180 plants, or containers per cycle (360 in research). Two weeks after transplanting, the plants were watered according to predetermined irrigation treatments.
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
### Role of AI and Related Works in DDoS Defense  
AI may improve classical IDS. Advanced AI models can identify minute changes between regular and malicious communications, warning of forthcoming attacks [7]. These technologies help identify slow-moving DDoSA that imitate genuine traffic. AI models may detect and mitigate risks using rank correlation to find significant disparities between attack traffic and legitimate user activity. Despite advances in AI-based intrusion detection systems, difficulties persist. There is a need for improvement in detecting unexpected data properties and handling open-set settings when new threats develop [8]. Improved AI model training and testing datasets may assist, but the real difficulty is the model's capacity to react to changing threats. DDoSA harm information servers, internet servers, and cloud computing platforms. These extensive attacks and their ability to mix with other cyber threats make identification difficult. Cyber threats include botnets, hacking, malware, phishing, ransomware, and spyware, complicating security and making comprehensive and adaptive solutions necessary [9].  
Increasing cyber threats, especially DDoSA, need a complex cybersecurity strategy. AI can improve cyber threat identification and mitigation [10]. The ever-changing cybercrime scenario requires constant defensive innovation and response. Robust, AI-driven security solutions will safeguard sensitive data and digital infrastructure as cyber attackers improve their methods and exploit new weaknesses. DDoS defensive systems increasingly use adaptive filtering and machine learning to enhance detection and mitigation. To combat application-layer DDoS attacks, which pose particular difficulties since they may imitate valid traffic patterns and elude conventional detection methods, a technique based on reinforcement learning has been suggested [11]. Expanding on this study, more current research has investigated explainable and flexible DDoS detection techniques that improve decision-making transparency and provide flexible models that can handle changing attack patterns in intricate network settings [12]. Furthermore, a framework for adaptive rule generation has been presented for DDoS filtering, emphasizing real-time modifications to filtering rules in reaction to changing DDoS traffic characteristics. This is essential for sustaining strong network security in ever-changing threat environments [13].
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 3 Self-Optimizing Traffic Steering
### Use Cases
#### 3.3.1 Computational Complexity Decrement  
Both intra-network and inter-network traffic steering involve multiple cells from either the same or different spectra. Furthermore, the resource occupation, user association, and channel status should be jointly considered based on the instantaneous user condition and channel information. Therefore, the optimization problem could be a very complicated mixed-integer non-linear program which is hard to solve. Moreover, the mmWave HetNet is usually ultra-densely deployed. Therefore, low-complexity traffic steering schemes are demanded for practical implementation. To tackle this problem, two efforts are made in this article. First, centralized and distributed approaches are combined together to solve the optimization problem. Specifically, SBSs, mmWave APs, and WiFi APs are clustered into groups according to their geographical locations, with each group connecting one subordinate controller. Meanwhile, a superior controller is introduced to control the operation of all subordinate controllers in order to guarantee the whole system performance. Secondly, we introduce the self-optimizing traffic steering strategy to automatically and intelligently make decisions according to a dynamic environment.
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 2 Related Work
When two nodes disconnect with each other, each node forwards an encrypted encounter evidence to the encountered node to enable encounter information collection to calculate the real utility. However, the encountering information collected is real ID-based and will thus leak privacy information. A malicious node can learn other node's mobility patterns from the real ID-based encountering information.
# 3D Positioning of Drones through Images
## 4 Experimental Setup
### Evaluation Metrics  
Similar to OS-FPI, this study employed two evaluation metrics for the image-based geographic localization task: Relative Distance Score (RDS) and Meter-level Accuracy (MA) to assess the localization performance of the algorithm.  
The Relative Distance Score (RDS) is an evaluation metric that assesses the relative distance between the algorithm's predicted localization result and the ground truth on the satellite image. It is calculated by converting the relative distance between the predicted and true values into a score.  
\(d_{x}\) represents the pixel error in the horizontal direction between the predicted location and the true location, while \(d_{y}\) represents the pixel error in the vertical direction between the predicted location and the true location. (Figure 10).  
\[\begin{array}{l}d_{x}=abs\big{(}X_{p}-X_{g}\big{)}\\ d_{y}=abs\big{(}Y_{p}-Y_{g}\big{)}\end{array} \tag{4}\]  
After obtaining \(d_{x}\) and \(d_{y}\), the normalized pixel relative distance (\(RD\)) can be calculated. The value of \(RD\) ranges between 0 and 1.  
\[RD=\sqrt{\frac{\big{(}\frac{d_{y}}{w}\big{)}^{2}+\big{(}\frac{d_{y}}{w}\big{)} ^{2}}{2}} \tag{5}\]  
The Relative Distance Score (\(RDS\)) is derived by transforming the relative distance (\(RD\)) into a score between 0 and 1 using an exponential function. In this study, \(k\) is a scaling factor set to 10.  
\[RDS=e^{-k\times RD} \tag{6}\]  
This study also employs Meter-level Accuracy (MA@K) as an evaluation metric for spatial distance. MA@K represents the proportion of samples in the entire dataset where the error between the algorithm's predicted geographic location and the true geographic location is within K meters.  
Figure 10: Relative distance map.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
###### Abstract  
In this paper, the Extended Coupled Amplitude Delay Lock Loop (ECADLL) architecture, previously introduced as a solution able to deal with a multipath environment, is revisited and improved to tailor it to spoofing detection purposes. Exploiting a properly-defined decision algorithm, the architecture is able to effectively detect a spoofer attack, as well as distinguish it from other kinds of interference events. The new algorithm is used to classify them according to their characteristics. We also introduce the use of a ratio metric detector in order to reduce the detection latency and the computational load of the architecture.  
Keywords:signal processing; GNSS receiver; spoofing detection; classification algorithm +
Footnote †: journal: Sensors
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 4 Result Analysis
### Ablation Experiment  
The reliability and importance of individual enhanced modules within the model are assessed by utilizing YOLOv5s as a benchmark and gradually integrating improved modules to perform ablation experiments. The assessment criteria used in this investigation comprise Precision, Recall, F1 Score, mAP, Number of parameters, and frames per second (FPS). Table 4 displays the results of the experimental findings. The default version of YOLOv5s exhibits a mAP of 65.91 and comprises 7.06 parameters. Including ECA-Net in the architecture has resulted in a 0.08% improvement in mAP. Additionally, there was an increase of 0.12 million parameters and a reduction of 9 FPS. Incorporating Focal Loss resulted in a 0.27% increase in mAP, while the number of parameters and FPS remained constant. Incorporating an adaptive anchor box by utilizing the K-means++ algorithm resulted in a 0.31% increase in mAP and a 0.97% increase in precision. Reducing parameters by 110,000 resulted in a corresponding increase of 7 FPS. Upon increasing the label smoothing value by 0.01, a notable enhancement of 0.39% was observed in the Recall metric. Ultimately, incorporating an additional detection layer resulted in a 67.81% mAP score, an F1 score of 66.51%, accompanied by a parameter increase of 1.05 million. The obtained FPS value in this experiment is 58, with an increased mAP of 1.9% from the Yolov5s baseline.  
Our newly added prediction layer helps to locate tiny objects, thus improving the detection performance. Figure 8 represents our model's effectiveness in detection performance. This implies that this model is reliable in detecting and classifying road pavement damage in real-time scenarios. Figure 9 demonstrates our proposed model detection results. We thoroughly analyzed the training and validation curves for class_loss, box_loss, and object_loss and presented the results in Figure 10 of the paper. Our findings indicate no overfitting, as there was neither an increase in validation loss nor any substantial fluctuations after the initial decrease.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Attention** & **Precision** & **Recall** & **F1 Score** & **[EMAIL_ADDRESS]** & **No. of** & \\
**Mechanism** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Params. (M)** & **GFlops** \\ \hline CBAM & 66.34 & 63.02 & 65.15 & 64.81 & 8.35 & 18.41 \\ SE & 65.56 & 62.31 & 63.89 & 64.72 & 7.41 & 17.56 \\ CA & 66.67 & 62.57 & 64.68 & 65.57 & 7.27 & 17.10 \\ ECA & 66.85 & 63.93 & **65.35** & **65.99** & **7.18** & **16.00** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of different attention mechanisms with YOLOv5s architecture.
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 4 The Propagation Law of Landslide-Induced Surges
### The Formula of Landslide-Induced Wave Height  
In this paper, by means of a large-scale physical model test, multiple regression analysis is carried out on the relevant parameters that affect the height of the first surge. The main parameters of the landslide are the width \(b\) of the landslide, the angle \(\beta\) of the slip surface, the thickness \(c\) of the landslide, and the depth \(h\) of the reservoir area. In this test, by changing the width of the landslide (0.5-1.5 m) and the thickness of the landslide (0.2-0.6 m), a dimensionless calculation was conducted on the height \(H\) of the landslide surge and its influencing parameters, respectively, obtaining \(h/b\), \(h/b\), \(c/b\), and \(\beta\).  
The calculation formula of the landslide-induced surge height is as follows [34]:  
\[\frac{H}{b}=0.2135(\frac{h}{b})^{-0.5}(\frac{c}{b})^{0.62}\beta^{0.77}. \tag{4}\]  
This formula is applicable to the calculation of the first wave height of a landslide-induced surge generated by a large amount of landslide body entering water in the Three Gorges Reservoir area. This formula can reasonably estimate the height of the landslide-induced surge and effectively analyze the risk of such a surge.  
Figure 6: The curve of the water level at monitoring points.
## 4 Results
### Photogrammetric Variant A (with All Images) vs. TLS  
The biggest differences between TLS and the photogrammetric point cloud were in areas with degraded lighting conditions and in occluded areas. For example, when measuring from some positions, the laser beam did not reach the gaps under the masonry (the red areas at the foot of the wall in Figure 10), and these areas were filled in the mesh using interpolation. On the contrary, it was not possible to photogrammetrically model spaces with an indistinct texture, such as gaps between some bricks. The difficult-to-access area under the terrain of the square (left part in Figures 10 and 11) also proved to be problematic, and the photogrammetric point clouds had considerable noise.  
Figure 10: Deviations in comparison of TLS and photogrammetry with all images active (TLS vs. A) (**left**), and histogram of the deviations (**right**).  
Figure 9: Positions of the identical points in the structures around the chapel (corners of concrete retaining walls and metal window frames). Z error is represented by ellipse color. X and Y errors are represented by ellipse shapes.  
In Figure 12, the principal differences between the two technologies are visible, especially on the wall with bones and skulls--the laser beam reached inside the eye sockets, which were filled in photogrammetry. During the comparison between TLS and photogrammetric variant A, a standard deviation of 7 mm was achieved, which for a chapel length of 12 m represents an overall relative accuracy of 1:1700.
## Appendix A Detailed Results
Figure A3: Performance metrics for the models running on the UNSW-NB15 dataset. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & **F1 Score** & **Accuracy** & **Precision** & **Recall** & **FPR** & **ROC AUC** & **Elapsed Time (s)** \\ \hline OCCSVM\_P & 0.145989 & 0.393467 & 0.127333 & 0.17105 & 0.509807 & 0.330621 & 27,085.11 \\ OCCSVM\_L & 0.242523 & 0.618004 & 0.303909 & 0.201769 & 0.200981 & 0.500394 & 15,992.11 \\ LOF & 0.355704 & 0.477056 & 0.283843 & 0.476289 & 0.522617 & 0.476839 & 106,6684 \\ ISOF & 0.6403636 & 0.779059 & 0.635898 & 0.647229 & 0.162778 & 0.742225 & 1148,8583 \\ EE & 0.647826 & 0.782862 & 0.637112 & 0.658818 & 0.163191 & 0.748712 & 179,6919 \\ Ens\_MV & 0.466815 & 0.686527 & 0.428524 & 0.449736 & 0.207482 & 0.781 & 0.4403,538 \\ Ens\_LR & 0.710133 & 0.831363 & 0.765918 & 0.646284 & 0.057061 & 0.796213 & 420,092.45 \\ Ens\_NB & 0.659011 & 0.793043 & 0.666283 & 0.651942 & 0.143957 & 0.753992 & 44,028.93 \\ Ens\_NN & 0.703398 & 0.83355 & 0.773203 & 0.645153 & 0.08341 & 0.780871 & 44,002.26 \\ Ens\_DT & 0.703398 & 0.83355 & 0.773203 & 0.645153 & 0.08341 & 0.780871 & 44,028.88 \\ Ens\_SVM & 0.703398 & 0.83355 & 0.773203 & 0.645153 & 0.08341 & 0.780871 & 46,415.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics for CICIDS2017 dataset using supervised ensemble framework.
## 3 Results and Discussion
### Laboratory Experiment
#### 3.2.3 Ergonomic Evaluation  
The ergonomic analysis of the reaching was carried out in the DHM environment of the Siemens Jack. We imported CAD models of manual assembly workstations into the virtual environment, added a predefined avatar and designed the entire assembly process as in a real environment. Figure 10 shows a graphical representation of the distribution of forward reaches per work cycle at a traditional assembly workstation and a CAD model of a traditional workstation with an avatar in a virtual environment. The traditional manual assembly workstation is designed according to the standards so that we can compare the distribution of the forward reaches with the smart manual assembly workstation. The results show that the worker reaches 12 times too far, 16 times within acceptable range and only once in close range. At close range, the entire product is assembled, so there is no storage space. However, it is ergonomically and medically less appropriate for a worker to reach too far in 41% of the 29 assembly operations (according to ergonomic regulations [38]).  
In the case of the smart manual assembly workstation, we already considered ergonomics when designing the workstation and positioned the grab containers and considered their functionalities more appropriately from an ergonomic point of view using a smart algorithm and empirical equations based on the gender and height of the worker. Figure 11 shows that during the assembly of the same products as at the traditional assembly workstation, the worker never enters the danger zone (far reach). By implementing the adjustable grab containers in this way on the smart manual assembly workstation, we have reduced reaches into the danger zone from 12 to 0 compared to the traditional manual  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Product Name** & **Traditional Manual Assembly Workstation** & **Smart Manual Assembly Workstation** \\ \hline P1 & 11 & 3 \\ P2 & 11 & 4 \\  & 22 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of errors occurred at traditional and smart manual assembly workstation.  
Figure 10: (**a**) Distribution of forward reaches per work cycle on traditional assembly workstation; (**b**) computer-aided design (CAD) model of a traditional workstation with an avatar in a virtual environment.  
assembly workstation. The initial position of the grab containers was identical for the traditional and smart assembly workstation. The difference is in the adjustable grab containers, which can change their position to the worker's moderate zone during the assembly process if necessary. In this case, the position of the container is programmed to change its position according to the reach of the worker's hand and the position of the part. With a smart manual assembly workstation, the worker mainly reaches the acceptable zone, which does not affect the deterioration of the worker's health in the long term.