esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Outlook  
The integration of highly damping polymer films as flexible walls within the cavities of Helmholtz resonators exhibits a high potential to improve the damping performance of the acoustic liner. Especially, a design with alternating active and inactive resonator cells results in additional acoustic-structural interaction of the flexible walls and yields a more broadband damping performance. The strip slotting method was used as a manufacturing technology for the square honeycomb core structure, because it enables a simple and reproducible manufacture of different flexible wall structures for the acoustic liner. Using this approach different geometrically identical acoustic liner samples were built and experimentally analysed. The acoustic-structure interaction was verified by measuring the sound pressure level in neighbouring active and inactive cells. On the one hand, the damping is influenced by the Helmholtz resonance, defined by the cavities' geometry and on the other hand by the film's structure dynamics. Therefore, the viscoelastic and physical properties as well as the film's dimensions affect the resulting acoustical behaviour. In this study thermoplastic polyurethane was analysed as a promising material due to its high damping performance and acoustically excitable elastic properties.  
For application in new aero-engine nacelles, a reduction of mass in relation to the tested specimen, an adaption of the core structure's strength and the fulfilment of the comprehensive aircraft requirements in necessary. Further investigations on the description of the influencing factors, such as the arrangement and dimensions of active and inactive resonator cells, dimensioning of pressure compensation holes or the optimal film material and film size are required. However, this study shows the relevance and potential of this novel acoustic liner concept with flexible walls (FlexiS liner)--not only for aero engines, but in the entire field of sound absorbers.  
The individual contributions can be specified as following: Conceptualisation and supervision: M.D. and N.M.; Methodology, structural design and conduction of the manufacturing: E.K. and M.K.; Mechanical characterisation and analysis: M.K.; Acoustical design and investigations: K.K., L.E. and E.S.; Preliminary safety analysis: K.H.; Writing-Review & Editing: M.K., E.K. and M.D.  
This study was financially supported by the German Federal Ministry for Economic Affairs and Energy (Luftfahrtforschungsprogram V) within the framework of LAKS (\"Larmabsorbierende Kunstststoffstrukturen\") project under grant agreement no. 20E1502.  
We thank our colleagues from LAKS consortium who provided insight and expertise that greatly assisted the research.  
The authors declare no conflict of interest.
## 3 Methodology: Extending the Processing Toolbox
### Semantic Accuracy Assessment Model  
The semantic accuracy of data for this current study was measured by assessing tags. The model was developed to analyze the user contribution patterns, e.g., the number of edits and the number of contributors, etc. A processing model (Figure 4) was developed based on intrinsic indicators [18; 74] to answer the following questions:  
* How many contributors have made contributions to OSM data?
* What classes of contributors have edited the area under examination?
* How many distinct users have contributed chronologically to develop a feature?
* How has a feature developed over time, and what is its latest version number?
* How many distinct active contributors edited OSM data?
* Contributor-distribution of created OSM-features and OSM-feature-edits.  
Figure 4: Processing model for identifying user contribution using history data.  
Figure 3: Processing model to compute the class-wise length of features with “name” attributes.  
The history data used for assessment were prepared as discussed in Section 4.1. The model was developed for answering specific questions as outlined earlier. The Python script \"development of contributors\" was developed to find the number of contributors. This script read the history table of line features in the PostgreSQL database and identified and returned a list of contributors date-wise. The classification of contributors was done based on the number of edits made by them. Hence, the script provided more in-depth information about each user. The results were grouped by classifying three types of contributors: \"senior-mappers\", \"junior-mappers\" and \"non-recurring-mappers\", as suggested by [PERSON] and [PERSON] (2018). The calculation was based on that these parameters may be prejudiced, as one mapper could be less active in a certain area while being very active in other areas. Thus, classification provided the rough estimate about the active members in the region. The script \"contributors and their cumulated percentage of contributions\" calculated the total amount and percentage of node-contributions by each contributor.  
Another important indicator is the number of distinct users who have contributed in developing a feature. This indicator would provide the status of correctness (positional accuracy) of the feature as per [PERSON]'s law. The script \"number of contributors per feature\" was developed to fetch each feature and listed all of the contributors that edited the respective feature. The latest edit was recorded with the date based on the timestamp. Further, the script \"maximum version per feature\" was added to the model for identifying the development of the feature over time and its valid maximum version. [PERSON] and [PERSON] (2018) and [PERSON] et al. (2016) have suggested that the greater the number of edits per feature (at least 15 edits per feature), the better would be the accuracy of that feature. Hence, this script certainly helped with identifying the number of more complete and correct features lists.
## 1 Introduction  
Precise tracking of laser beams has innumerable applications, including but not limited to lidar, chemical/biochemical microsystems, microscopy, optical tweezers, telescopes, satellite communications, and optical interconnects [1, 2]. Among these applications, in recent years optical interconnects have attracted enormous interests particularly [3; 4; 5]; and, fast on-chip optical beam detection/tracking/steering techniques are expected to play critical roles in the next-generation optical interconnects [6; 7; 8]. The most traditional laser beam detection techniques rely on mechanical steering [9; 10]. Although mechanical steering provides broad range of angle detection, it leads to systems that are bulky, complex, costly, and slow. Various contemporary applications (such as optical interconnects) call for low-cost, fast-responding, and on-chip devices for precise beam detection. Quadrant photodiodes (QPDs) may be the most popularly adopted for the purpose of on-chip beam angle detection [11; 12]. Other approaches for laser beam angle measurement include lateral-effect photodiodes (LEPs) [13] and dual focus Fresnel lens [14]. Nevertheless, the angle detection methods in [11; 12; 13; 14] share one drawback: they can only detect very small angle variations up to 3 mrad or 0.2 degrees.  
In this paper, a novel angle detector is designed based on grating coupling. It consists of a grating layer on top of a silicon-on-insulator (SOI) slab waveguide. The incident light is coupled into guided modes within the waveguide via the grating layer, and then, the incident light's angle can be determined by reading the outputs of light detectors within the waveguide. This device offers fast-responding on-chip detection of laser beam's angle, without requiring any external objective lens. Its detectable angle range can reach a few degrees, which is far greater than those associated with QPDs or LEPs. Performance of the laser angle detector in this paper is demonstrated by full-wave finite-difference-time-domain (FDTD) simulations. Numerical results show that, the detectable angle range can be adjusted by several design parameters and can reach [-4\({}^{\circ}\), 4\({}^{\circ}\)]. It is noted that grating coupling was applied to laser angle measurement in the past. For instance, in [15] laser angle was measured based on the spatial variation of the coupled light. Since the measurement precision is dictated by the number of photodetectors placed along the transverse direction of the waveguide, the device in [15] takes large real estate (around 3.5 mm) in order to measure angles up to 1 degree accurately. As a comparison, the detector in this paper only employs two photodetectors and is highly compact: the width of our coupler could be as small as 20 \(\upmu\)m (depending on the integration density) with measurement range of \(\pm\)4 degrees.  
The rest of this paper is organized as follows. The grating-based angle detector is described in Section 2. In Section 3, some numerical results are presented to demonstrate the device's performance. Finally, Section 4 relates to our conclusions of this study.
# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 2 Model Structure  
### Satellite Model  
To simulate a realistic satellite communication environment, a model comprising three GEO satellites and five MEO satellites was established. Each GEO satellite has a total capacity of 100 Gbps, while each MEO satellite has a total capacity of 30 Gbps. The model further includes MEO and GEO ground stations located on Earth, which communicate with their corresponding satellites. These ground stations are coordinated through a control center to ensure effective system operation and data transmission. Additionally, the model considers various end-users, including aircraft, vehicles, and ships. These terminals receive and transmit data through satellite links to meet their communication needs, as illustrated in Figure 1.  
Due to the simultaneous operation of multiple satellites in orbit, end-users may encounter the issue of multiple coverage, wherein several satellites are within their visible range, as illustrated in Figure 2.  
In the figure, the GEO satellites remain stationary relative to the Earth, with the green circles representing their coverage areas. The MEO satellites orbit the Earth at a 60\({}^{\circ}\) inclination, with the yellow lines indicating their trajectories and the blue circles  
Figure 1: Simulated actual satellite communication scenario.  
representing their coverage areas. The situation of multiple coverage may increase the system's complexity, but it also contributes to enhanced communication reliability and continuous coverage.
# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
## 5 Real-Time Experiment on NAO Humanoid Robot
### Discussion  
After obtaining results of the multi-binary classifiers in Table 3 we expected to get the best results with the most distinguishable rooms, i.e., bathrooms & kitchens, on phase 3. The results were as expected with bathrooms, but not with kitchens. The reason is the short height of NAO, which captured scenes that are totally different from what was learnt from the dataset. In the kitchen case, the robot captured the cabinets and drawers rather than capturing the top view of stoves or other appliances. Thus, the robot's first prediction was mostly bathrooms instead of kitchens. The ideal solution is to have a room dataset for robotics in a height range of 0.5-1.0 m, but this would be an expensive process. The other solution, the approach adopted in this work, is to use an existing dataset for training and validating a learning model, and then to design a method for real time experiments with the robot. For this work, the improvement was shown in the second decision of the robot, so how the robot can make its decision? The simple way is that the robot can select the second prediction only when the first prediction probability is below a threshold. The other solution is to make a decision based on a combination of multiple images from the same room.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\# _Images_} & \multicolumn{2}{c}{\# _False Images_} & \multirow{2}{*}{_bath_} & \multirow{2}{*}{_belt_} & \multirow{2}{*}{_din_} & \multirow{2}{*}{_kit_} & \multirow{2}{*}{_liv_} \\ \cline{3-3} \cline{5-8}  & _bath_ & & & & & & \\ \cline{2-8}  & _bed_ & 13 & & 4 & 9 & & \\ \cline{2-8}
**Top-1 with CNN** & _din_ & \(6\) & & 2 & 4 & & \\ \cline{2-8}  & _kit_ & 13 & & 10 & & 2 & 1 \\ \cline{2-8}  & _liv_ & 12 & & 4 & 2 & & 6 \\ \hline \multirow{4}{*}{**Top-1 with CNN-ECOC**} & _bath_ & \(0\) & & & & & \\ \cline{2-8}  & _bed_ & 4 & & 4 & & & \\ \cline{2-8}  & _din_ & \(2\) & & 1 & 1 & & \\ \cline{2-8}  & _kit_ & 11 & 1 & 4 & 6 & & \\ \cline{2-8}  & _liv_ & 6 & & 2 & 1 & & 3 \\ \hline \multirow{4}{*}{Top-1 with CNN-ECOC} & _bath_ & _12_ & & 12 & & & \\ \cline{2-8}  & _hot_ & \(4\) & & 4 & & & \\ \cline{2-8}  & _din_ & \(2\) & & 1 & 1 & & \\ \cline{2-8}  & _kit_ & 11 & & & 11 & & \\ \cline{2-8}  & _liv_ & \(7\) & & & 1 & 6 \\ \hline \multirow{4}{*}{Top-1 with CNN-ECOC-REG} & _bath_ & _12_ & & 12 & & & \\ \cline{2-8}  & _bed_ & _13_ & & 4 & 9 & & \\ \cline{2-8}  & _din_ & \(6\) & & 2 & 4 & & \\ \cline{2-8}  & _kit_ & 13 & & 9 & & 4 & \\ \cline{2-8}  & _liv_ & 12 & & 4 & 1 & 1 & 6 \\ \hline \multirow{4}{*}{Top-2 with CNN-ECOC-REG} & _bath_ & \(0\) & & & & & \\ \cline{2-8}  & _bed_ & \(4\) & & 4 & & & \\ \cline{2-8}  & _din_ & \(2\) & & & 2 & & \\ \cline{2-8}  & _kit_ & \(9\) & & & & 9 & \\ \cline{2-8}  & _liv_ & \(6\) & & & & & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix of testing images with top-1 & top-2 for five classes.
## Materials and Methods
### Correlation between Land Use and Land Cover and Water Quality  
Correlations were calculated between land use classes and water quality parameters. Initially, the Overall Correlation was calculated considering the entire MRW, and subsequently, individual correlations were calculated considering each of the 17 Influence Areas. For each IA, the total area (ha) value of the respective land use, for each period (month-year), was compared with the water quality values. The monitoring stations have the respective IA, according to the water quality data and the land use. To determine the correlations between water quality variables and land use and land cover classes, the Shapiro-Wilk normality test was applied to the water quality dataset. A significance level of _p_-value < 0.05 was adopted for the hypothesis that the dataset follows a normal distribution. Upon analyzing the dataset using the Shapiro-Wilk method, it was found that the parameters did not exhibit a normal distribution around their means. Thus, the Spearman's Rank Correlation Coefficient (\(\rho\)) was adopted to measure the degree of correlation between the multitemporal land use and land cover data and the water quality data (Equation (3)).  
\[\rho=1-\frac{6\sum d^{2}}{n(n^{2}-1)} \tag{3}\]  
where \(d\) is the difference between the positions of corresponding values of X and Y; \(n\) is the number of pairs of data values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Analytical Method** & **Class 2 Limit Value** \\ \hline pH & - & 4500-H [19] & 6–9 \\ Dissolved Oxygen (DO) & mg/L & ASTM D888-12 [20] & \textgreater{}5.0 \\ Biochemical Oxygen Demand (5-day BOD Test) & mg/L & 5210-D [19] & \textless{}5.0 \\ Fecal coliforms & MPN. 100 m/L & 9221-E [19] & \textless{}200 \\ Total Nitrogen (TN) & mg/L & NBR 13796 [21] & - \\ Total Phosphorus (TP) & mg/L & 4500 P–B, E [19] & \textless{}0.1 \\ Total Solids (TS) & mg/L & 2540–C [19] & - \\ Turbidity (Tu) & NTU & 2130-B [19] & \textless{}100 \\ Water Quality Index (WQI) & - & IMASUL [16] databaseby & - \\  & & CETESB [17] equation & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quality parameters and reference values for Class 2 classification Deliberation CECA N\({}^{\circ}\). 36/2012 [18].  
The Spearman's Rank Correlation Coefficient measures the degree of linear correlation between two non-parametric variables. The value of \(\rho\) ranges from \(-1\) to \(+1\), where values closer to \(+1\) indicate a perfect positive correlation between two variables, meaning that the units of the variables increase together; values closer to \(-1\) indicate a perfect negative correlation, as one variable increases, the other decreases; and values closer to \(0\) indicate that both variables are not linearly dependent on each other.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 2 Related Work
### Current Detection and Prevention Methods  
Ransomware defenses are categorized into prevention, detection, and prediction. Prevention uses predefined rules and procedures to block attacks in real-time, but is static and vulnerable to human error due to reliance on manual configurations [54]. Detection models identify threats with similar behaviors by relying on known attack patterns, forming a proactive defense. Prediction enhances these approaches by anticipating future attacks. However, traditional prediction models fail to account for ransomware's dynamic nature and obfuscation techniques like polymorphism and metamorphism [26; 27].
## 4 Data
A following paper will detail all the information on the CCMAR dataset, which will be publicly available, including the ground-truth annotated by a marine biologist, the classification of frames that are overwater, in transition, and underwater, as well as the resulting labeling provided by the present method. Finally, it is important to stress that for the initial transfer learning process applied in the method (see Section 5), the OzFish dataset [12] was used, which is a BRUV public dataset of the Australian marine fauna. Although this data reflects antipode species to our targeted application, which would result in an unreliable object detection model, the use of a contrasting and noisy dataset is a fundamental part of our presented method to demonstrate the knowledge transferability and applicability in the generation of an automatically local labeled dataset. Figure 2: Top row: sample of CCMAR unlabeled footage. Middle row: ground-truth annotations of CCMAR videos. Bottom row: negative sample images obtained from the video classification step. The object detection fraction of OzFish was evaluated initially through an exploratory data analysis of the remaining 1753 frames; some images with annotations are presented on the top row in Figure 3. One important remark about this dataset is the outsourcing nature of the available annotations, which introduces noise to the dataset either by wrong or missing annotations or bounding box labels, e.g., the existence of 935 annotations with an area smaller than 1% of the mean bounding box area, including an annotation with an area of 9 pixels. Some examples can be observed in the bottom row of Figure 3. Albeit the existence of inconsistencies in the available data for the object detection task, as mentioned, such as wrong annotations, multiple bounding boxes for the same object, and miniature boxes with no recognizable target inside, we decided against cleaning and preparing the data for a traditional object detection model training pipeline, focusing on automating the learning stage to filter and minimize the effects of noisy data on the training process instead of filtering the data a priori. Furthermore, employing the method on noisy data without the curating step significantly reduces the overall time spent on an object detection project. Nevertheless, although part of the data is unreliable, the remaining data still contain relevant information and should not be discarded. Figure 3 shows in the top row samples of OzFish dataset; in the bottom row, examples of erroneous annotation; from left to right, overlapped annotations over the same target, and a negligible small bounding box, reef, feeder structure, starfish, and floating rope, all annotated as fish. In the next section, the developed method is going to be presented.
# Resource Allocation in 5G Cellular IoT Systems with Early Transmissions at the Random Access Phase
## 4 Performance Evaluation Framework
### Two-Dimensional Markov Chain Model  
The model described in the previous section can be represented by a two-dimensional Markov chain \(X_{n}=(R_{n},D_{n})\). Here, \(R_{n}\) is the number of active EDs in the RA phase, and \(D_{n}\) is the number of packets queued in the DT phase in the \(n\)th frame. We now parameterize this process by providing the transition probabilities between states.  
As the new active EDs arrive according to a Poisson process with intensity \(\lambda_{r}\), the number of active EDs in the RA phase at each frame follows a Poisson distribution with parameter \(\lambda_{r}\Delta\), where \(\Delta\) is the frame duration. We denote by \(\alpha_{k},k=0,1,\ldots\), the probability that \(k\) new EDs arrive within a single frame.  
The number of EDs that successfully passed the RA phase depends on the number of active EDs in the frame. Assume that the system is in state \((r,d)\). Hence, the probability \(\beta_{k}(r)\) that \(k\) out of \(r\) EDs will successfully pass the RA phase is provided in [12]:  
\[\beta_{k}(r) =\frac{1}{L^{r}}\binom{L}{k}\sum_{i=0}^{\min(L-kr-k)}(-1)^{i} \binom{L-k}{i}\frac{r!}{(r-k-i)!}(L-k-i)^{r-k-i}, \tag{3}\] \[0 \leq k\leq\min(L,r),\]  
implying that the mean number \(B(L,r)\) of EDs that successfully pass the RA phase in a single frame with \(r\) active EDs and \(L\) preambles is calculated as  
\[B(L,r)=r\bigg{(}\frac{L-1}{L}\bigg{)}^{r-1}. \tag{4}\]  
The state-space of the system for realistic values of the number of preambles \(L\) and the number of active EDs is expected to be large; we propose to approximate probabilities \(\beta_{k}(r)\) by a binomial distribution with parameters \(m=min(L,r)\), and \(p=B(L,r)/m\). This approximation is accurate even for small values of \(L\) and improves as \(L\) increases, as shown in Figure 4. With these parameters, we ensure that the expected number of EDs passing the RA phase in a time slot coincides with the initial distribution for all values of \(L\) and \(r\). In addition, it can be shown that variances of the initial distribution and the binomialdistribution with the specified parameters converge to the same value for \(r=L\) and large values of \(L\).  
By accounting for batch arrivals in the downlink direction in the DT phase and for the number of EDs that successfully pass the RA phase, we can derive the transition probability from state \((r,d)\) to state \((s,g)\) in the most general cases as  
\[p_{(r,d),(s,g)}=\sum_{i=0}^{\min(L,r)}\beta_{i}(r)\alpha_{s-r+i}\sum_{j=0}^{g- d-i-W}\gamma_{j}l_{g-d-i-W}^{(j)}. \tag{5}\]