esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling the Microphone-Related Timbral Brightness of Record Signals
## 4 Model Validation
### Validation of Model  
Figure 6 shows the brightness predicted by the model against the mean listener ratings. The model achieves a Pearson's r of 0.955, but the line of best fit (grey dashed line) shows a different gradient to the ideal line (solid back), leading to under-prediction for high-brightness stimuli. This is most likely due to inconsistency in scale usage between the training and validation tests (the inclusion of just two stimuli common to both might not  
Figure 5: Validation microphone array. (a) AKG C1000S; (b) Cirrus WM7331 (side on); (c) Countryman B3; (d) AKG D12; (e) Schoeps CMC6U with 2H capsule; (f) Cirrus WM7331 (face on); (g) Sony F730; (h) Cirrus WM7132 (side on); (i) Royer R-121; (j) Cirrus WM7132 (face on); (k) Sony ECM670; (l) Hebden HS3000 (omni); (m) WM7138 (side on); (n) WM7138 (face on); (o) Neumann U87 (cardioid); (p) Hebden HS3000 (hyper-cardioid); (q) DPA 4015; (r) Sony C800.  
have been sufficient). For the purposes of audio database searching, however, a gradient mismatch between prediction and perception is unlikely to be of any consequence: if brightness metadata are used to search for sounds with a specific brightness then, since there is no standard unit of brightness perception, the search would require selection of a reference sound with the desired brightness, and the metadata would allow accurate identification of the other sounds having the most similar brightnesses; if brightness metadata are instead used to order search results, then the high value of Spearman's rho indicates that this also will be achieved accurately.  
In Section 3.3, it was shown that the contributions of the \(SC\) and \(\text{Ratio}_{2}\) were similar by performing a multilinear regression with the standardised metrics. Performing the same analysis with the validation data produces coefficients of 12.172 and 6.815, respectively. This indicates that the \(SC\) accounts for more of the variance within the validation dataset than the \(\text{Ratio}_{2}\) metric. However, neither constituent metric accounts for the vast majority of the variance.
# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 2 Methods
### Classification Based on Clustering and Thresholding  
In terms of the railway domain, structured data can be organized hierarchically from fine to coarse granularity as attributes, tables, and systems. Our work firstly focuses on the identification and classification of sensitive data at the attribute level. Furthermore, based on the results of the attribute-level assessment, sensitivity classification is carried out at the table and system levels, thereby enabling multi-granularity classification of data within the railway domain.  
Specifically, we adopted a hybrid classification strategy that combines dynamic and static methods. Firstly, we utilize a clustering-based method to dynamically classify attributes according to their computed sensitivity values, thereby determining the sensitivity level of each attribute. Building on the attribute-level classification, a static weighting-based approach is then employed to determine the sensitivity of each data table, enabling classification based on predefined threshold values. Finally, system-level classification is conducted using a static approach, based on the aggregated sensitivity of the associated data tables.
# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
## 5 Use Case: Shiptec
### Data Sources  
This section describes the data sources, things, external systems, and internal systems as described in Section 4.3 for the specific case of the ship MS Diamant. In the previous section, we introduced four systems forming together the ship as a system of systems. Each of the four systems contains \"things\" in the form of SCPs equipped with sensors. The data generated by these sensors is sent to a private cloud, where the data is further processed and used by the DT. A typical data flow, as seen in Figure 13, can be illustrated by following the data generated by the smart engines.  
The data collected by their sensors is transmitted to the embedded information system of the propulsion system and from there to a gateway, which forwards the data via WLAN or 4G/5G to a private cloud. These data are structured and further enriched in the DT  
Figure 11: Product life cycle adapted to the use case.  
Figure 12: Real-world counterpart in the use case - MS Diamant as a system of systems.  
with data from internal and external systems. Regarding internal systems, data from the CRM, the ERP, and the PDM are used and fed into the DT. Furthermore, the data in the DT is enriched with map data, schedules, weather data, and passenger volume on the ships from external systems. The distinction between internal and external data sources is not always evident, as Shiptec and SVG are close partners. Regardless, the overall logic for enriching the sensor data with data from internal and external systems remains the same for all SCPs and systems.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 3 Trust Establishment and Trust-Based Monitor Selection  
The main idea of this work is to establish trust among the communicating UAVs and select the most trusted UAV (called monitor) with high probability of data holding and having enough energy for the rest of its mission. Another goal is to take advantage of the selected monitor to avoid the broadcast storm problem that may be caused by the dissemination of interest packets. When the data producer or a node having a copy of the requested data is found, it sends back the data packet to the requester UAV through the shortest and most trusted path as used in [28]. Finally, the data authentication process is carried out based on how trustful is the producer of this data.  
In the following sections, we first present the proposed inter-UAV trust establishment and management, and then outline a lightweight data authentication based on inter-UAV trust.
## 5 Results
### Simulation Results
Under the first PAM-2 transmission scenario, a \(P_{\mathrm{r}}\) gain SVD-PPE of \(\approx\)3.52 dB is observed in comparison to PE-MMSE. Similarly, the performance improvement of \(\approx\)2.54 dB is marked while solely using the joint-PPE. The joint-PPE scheme achieves an identical BER  
Figure 6: Noise weighting factor with increasing number of pre-equalization coefficients, where a layer-wise comparison between the proposed joint-PPE scheme and the ZF-based PPE is exhibited. performance with only half of the optimized pre-equalizer taps compared to the ZF-PPE. It is evident by the overlapping BER performances of the joint-PPE with \(L_{\text{p}}=2\) and ZF-PPE with \(L_{\text{p}}=4\). Considering the PAM-4 transmission on both MIMO inputs under the second scenario, a slight improvement is noted in comparison to the first scenario. The joint-PPE in conjunction with SVD yields the best system performance, surpassing PE-MMSE by \(\approx\)3.86 dB. The joint-PPE single-handedly achieves a \(P_{\text{r}}\) gain of \(\approx\)2.73 dB compared to PE-MMSE. An improvement in \(P_{\text{r}}\) of \(\approx\)1.13 dB indicates that the proposed joint-PPE enhances the system performance to a higher extent than the conventionally deployed ZF-PPE. Figure 8: BER dependent on simulated received power \(P_{\text{r}}\) for PAM-2 and PAM-4 transmissions. The BER values associated with \(L_{\text{p}}=2\) and \(L_{\text{p}}=4\) are represented with the square and the circle markers, respectively. Figure 7: Measured channel impulse response of a 1.4 km long MMF with respect to the symbol rate \(f_{\text{T}}=1/T_{\text{s}}=5\) GHz per MIMO layer at an operating wavelength of 1550 nm. In summary, the simulated BER performances and the \(P_{\mathrm{r}}\) gains testify to the advantages of using the jointly designed and optimized PPE filter with PAM-2 and PAM-4 formats. Moreover, the hypothesis is established with the analytical proof that, by shifting the post-equalization taps to the transmitter side, the system performance is boosted due to the limited amplification of the noise.
## 1 Introduction
[11] used SIF data derived from OCO-2 and flux tower GPP data to study the relationship between them and found a strong correlation. [PERSON], et al. [34] concluded that SIF can be used to quantify GPP changes in response to meteorological drivers. Taking the Amazon rainforest as the study region, [PERSON], et al. [13] revealed the response of SIF to instantaneous photosynthetically active radiation (PAR). Although a surprisingly linear relationship was found at a regional or a global scale, the mechanistic link between SIF and GPP remains unclear [12; 35] because the former closed relationships were statistically significant. SIF provides a new perspective to estimate instantaneous vegetation and carbon-related processes that are essential for the assessment of carbon budgets of forest ecosystems [18]. This requires continuous SIF data at high spatial and temporal resolutions. However, continuous global coverage of SIF at a high spatial resolution cannot be generated due to the sparse spatial sampling mode of OCO-2 [10]. In this research, we explore the possibility of using MODIS and meteorological data to simulate SIF in a boreal forest of China. Our research provides robust evidence of the relationship between SIF and vegetation indices while demonstrating great potential for SIF simulation in boreal forests based on the enhanced vegetation index (EVI) and temperature.
# An Automatic Embedded Device Registration Procedure Based on the OGC SensorThings API
## 3 Methodology
### Device Description Data Model and Encoding  
#### 3.1.1 OGC SensorThings API Data Model  
In the OGC SensorThings API, devices can be described by a standard data model, which is extended from the OGC O&M model. The O&M defines a complete data model of an observation event by identifying necessary classes/properties and their relationships. The SensorThings API includes the SW-IoT concept into the O&M data model by introducing additional classes. Figure 2 is the UML (Unified Modeling Language) diagram of the SensorThings API data model. An SW-IoT device can be modeled as a Thing entity. A Thing uses Location entities to introduce its current location and uses HistoricalLocation entities for its past trajectory. A Thing can have one or more Datastreams, where each Datastream is a logical aggregation of sensor Observations of an ObservedProperty produced by the same Sensor. Observations may observe different FeaturesOfInterest. As the O&M data model has been applied in many fields for more than a decade, this SensorThings API data model is also considered complete and general for the SW-IoT.  
Figure 2: The SensorThings API UML diagram [14].
## 3 Results and Discussion  
### Carbon Chain Analysis in Crude Oil and Gasoline  
The test is done to find out the composition of the liquid fuels used for experimental analysis. The results of the crude oil CHNS test are in Figure 2.  
According to this observation, the carbon content is the maximum, with much less sulfur. The CHNS results justify that the crude sample taken is a sweet crude and is of high usage in the market. Since sweet crude oil has a high economic impact, which is used rigorously for processing, the possibility of its transportation is high. Later, to explain the number of carbon chains in the system, the HPLC test is carried out for the same sample. The results observed are in Figure 3.  
Figure 2: CHNS analysis of the crude sample.  
Similarly, the gasoline sample is collected from the nearest filling station. CHNS test is done to analyze the carbon composition in the gasoline used. The test result is mentioned in Figure 2.  
Based on the results, the sulfur content should be negligible, as it is collected from the nearest filling station. The results shown are highly in accordance with the technical details. So, there is a high possibility of the same fuel to be used for transportation in pipelines. Since gasoline is known for its high volatility, the HPLC test is quite challenging to perform. Therefore, a gas chromatography and mass spectroscopy (GCMS) study is conducted and analyzed to find the composition of the gasoline sample used for experiments. The results from the GCMS study are in Figure 4.  
Figure 3: Carbon chain characterization in the crude sample.
# GNSS Multipath Detection Using Continuous Time-Series C/N0
## 4 Testing and Results  
In this section, the three urban scenarios selected for the experiments to evaluate our proposed method are described, and the results are discussed in detail. The first scenario was a dense urban area close to the previous test location described in Section 2.1. The characteristic of this location was that several NLOS signals were always received from tall buildings on one side. The second scenario was also a dense urban area surrounded by many high-rise buildings, with fewer LOS signals than in the first scenario. The third scenario was a location surrounded by two medium-rise buildings on both sides on our campus. The characteristic of this location was that several NLOS signals were always received from medium-rise buildings on one side. The raw data of the GNSS were post-processed using our algorithm mentioned above. The processing is rapid and can be used in real time. The base station on the rooftop of our building was used for correction data. The baseline length was less than 5 km in each test.
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
## 6 Discussion
### Controllability  
From the results of the first experiment, we found that both the proposed hands-free method and the virtual joystick achieved low path dissimilarity scores when comparing the target trajectory to the achieved trajectory. The joystick produced a better score with no significant difference observed, which shows that the two interfaces have similar control precision for the target trajectory. Comparing the robot/human trajectory under the human-guided interface and joystick in Figure 15, it can be observed that the robot trajectories under the two interfaces are similar. Due to the distance-shift steering control method, the participants chose a larger turning radius when steering the stroller using the hands-free interface than the joystick. Also, the participants did not consistently stay directly behind the stroller while maneuvering using the joystick, especially when walking in the opposite direction, they wanted to turn during the steering process. This distance-shift behavior is similar to that in the proposed system, demonstrating that the operation method in the proposed system was intuitive to the participants. This result is consistent with our findings in the preference following the relative position survey (Section 2.1) that it's acceptable for humans to walk with the smart stroller in a proximity front area. This phenomenon may be due to natural human behavior or the need for humans to maneuver a human-guide mobile device around a corner to gain a larger viewing angle through distance adjustment. Still, in the interview, some of the male participants reported that the joystick is more sensitive and easy to direct compared to the human-guided system. Males gave the joystick a higher score on the smoothness item in the additional questionnaire. On the contrary, the females gave the opposite score. This observation is consistent with the literature on sex differences in visuomotor tracking, showing an advantage for males in eye-hand coordination, such as in using a joystick [42].
## 1 Introduction
Knowledge of traffic routes is a useful input to situational awareness and helps in understanding seasonal variations in traffic patterns. Besides traffic densities, the extracted routes provide useful information on daily patterns and transit duration differentiated by vessel types. Further, extracted routes enable realistic simulations of traffic, which are useful to test and evaluate target tracking performance, the effectiveness of surveillance technologies and other decision support frameworks. Generated contextual maritime knowledge can also be used to perform rule-based and low-likelihood _anomaly detection_. Rule-based anomaly detection approaches refer to the generation of alerts based on a set of rules [7], such as maximum speed allowed in a port, presence in areas restricted to navigation or inconsistencies between ship claimed and actual activity. Conversely, low-likelihood anomaly detection aims at detecting deviations from \"normality\" of vessel traffic patterns derived in the learning phase (see, e.g., [8] and references therein) and is illustrated via an example provided in the present work. Behaviors that differ from \"normality\" do not necessarily mean they are \"anomalies\" in an operational context, but they are highlighted as _unusual_ for further analysis. The vessel traffic and motion information, once extracted, can be alternatively exploited to perform ship route prediction at a given time. This is the process of predicting ship movements well beyond any available positioning data, based on behaviors of past vessels on the same route. This is useful, for example, in counter piracy applications to identify risk areas associated with the joint predicted presence of white shipping density (e.g., commercial merchant traffic) and Pirates Action Groups (PAG) [9]. Backward and forward tracking of vessels can also be significantly improved using the learned maritime traffic patterns, which are particularly useful when attempting to fuse AIS and space-based optical or Synthetic Aperture Radar (SAR) information (e.g., [10]). The distribution and characterization of traffic can also be used for augmenting remote sensing tracking and classification performance, enabling knowledge-based tracking and classification (e.g., [11]). Specifically, the knowledge of vessel patterns can be used for (i) connecting tracks originated by the same target and broken by gaps in coverage or reduced observability and/or (ii) providing _a priori_ knowledge about the vessel type for classification purposes. In Section 2, we give a brief review of related work on traffic characterization and route knowledge extraction. We discuss the traffic knowledge discovery methodology in Section 3. This is followed by some examples of route knowledge exploitation in Section 4: the route classification is given in Section 4.1. Two specific applications (_i.e._, route prediction and anomaly detection) are provided in Sections 4.2 and 4.3, respectively, to illustrate the potential of the derived knowledge. Finally, concluding remarks are given in Section 5.
# The Concurrent Validity of Mobile Application for Tracking Tennis Performance
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Zone**} & \multicolumn{2}{c}{**VA**} & \multicolumn{2}{c}{**SV**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Zone**} & \multicolumn{2}{c}{**VA**} & \multicolumn{2}{c}{**SV**} \\ \cline{3-3} \cline{5-10}  & & **Count** & \% & & & & **Count** & \% & **Count** & \% \\ \hline \multirow{5}{*}{**Place\_DE**} & **Zone 1** & 19 & 12.67 & 16 & 10.67 & \multirow{5}{*}{**Place\_AD**} & **Zone 1** & 28 & 18.67 & 26 & 17.33 \\ \cline{2-10}  & **Zone 2** & 49 & 32.67 & 54 & 36.00 & & \multirow{5}{*}{**Zone 2**} & 32 & 21.33 & 38 & 25.33 \\ \cline{2-10}  & **Zone 3** & 22 & 14.67 & 20 & 13.33 & & & & & & \\ \cline{2-10}  & **Zone 4** & 60 & 40.00 & 60 & 40.00 & & \multirow{5}{*}{**Place\_AD**} & **Zone 3** & 18 & 12.00 & 19 & 12.67 \\ \cline{2-10}  & **ICC** & & 0.83 & & & & & & & & \\ \hline \multirow{5}{*}{**Place\_DE**} & **Zone 1** & 19 & 12.67 & 16 & 10.67 & \multirow{5}{*}{**Place\_AD**} & **Zone 1** & 28 & 18.67 & 26 & 17.33 \\ \cline{2-10}  & **Zone 2** & 49 & 32.67 & 54 & 36.00 & & & & & & \\ \cline{1-1} \cline{2-10}  & **Zone 3** & 22 & 14.67 & 20 & 13.33 & & & & & & \\ \cline{1-1} \cline{2-10}  & **Zone 4** & 60 & 40.00 & 60 & 40.00 & & & & & & \\ \cline{1-1} \cline{2-10}  & **ICC** & & 0.83 & & & & & & & & \\ \hline \hline \end{tabular}  
* Placement_DE—serve placement on the deuce side of the court; Placement_AD—serve placement on the advantage side of the court; SVs—software-gained data; VA—data gained with analysis of recorded video; Zone— number of the zone; Count—number of serve shots per zone, %—the percentage of serve shots in a zone; ICC—correlation between measurements.  
\end{table} 
Table 2: Shot frequency and validity measures for placement of serve shot. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **VA** & & **SV** \\ \hline \multirow{2}{*}{**Forehand**} & **Count** & **\%** & **Count** & **\%** \\ \hline
**Forehand** & 224 & 44.80 & 223 & 44.60 \\ \hline
**Backhand** & 143 & 28.60 & 142 & 28.40 \\ \hline
**Serve** & 127 & 25.40 & 128 & 25.60 \\ \hline
**FH Volley** & 3 & 0.60 & 3 & 0.60 \\ \hline
**BH Volley** & 2 & 0.40 & 2 & 0.40 \\ \hline
**Overhead** & 1 & 0.20 & 2 & 0.40 \\ \hline
**ICC** & & 0.97 \\ \hline \hline \end{tabular}  
* SV—software-gained data; VA—data gained with analysis of recorded video; Count—number of serve shots per zone; %—the percentage of serve shots in a zone; ICC—correlation between measurements.  
\end{table} 
Table 3: Shot frequency and validity measures for observed stroke shots. lowest number of shots was played via kick rotation (VA = 7.40%/SV = 6.80%), and this applied only to playing serve shots. The ICC value showed good validity (0.76) between the VA and SV analysis of the spin shots.
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### Evaluations of FDI Attacks
#### 3.2.5 Total Reward  
The total reward metric is an effective measure of the performance of the RL charging coordination system, as higher rewards indicate better achievement of design goals. Figure 6a,b show the system's total reward with no attackers, and with two and four attackers for Attack #1, across two levels of available charging power (\(C_{\text{max}}=6\) and \(C_{\text{max}}=10\)) and two levels of attack severity (\(\alpha=0.1\) and \(\alpha=0.7\)). Tables 2 and 3 provide the change in the power usage for the five FDI attacks.  
The results demonstrate that FDI attacks lower the total reward, making it harder for the system to meet its objectives. This conclusion aligns with the findings of previous subsections, where reductions in the fairness metric and SoC values of honest consumers and increased power usage led to a lower total reward. The figures also reveal that the total reward decreases as the number of attackers increases and as the severity of the attacks grows (i.e., \(\alpha=0.7\) versus \(\alpha=0.1\)). This is because the attacks hinder the system's ability  
Figure 5: Charging power usage under Attack #1 for two and four malicious consumers. (**a**) Two malicious consumers. (**b**) Four malicious consumers.  
to maintain fairness, reduce energy consumption, and keep the SoC values of consumers within predefined limits.