esa-sceva/satcom-chunk-collection huggingface dataset:
# Embedded Deep Learning for Ship Detection and Recognition
## 5 Experiment Results
### Algorithm Performance  
To evaluate the performance of DNet, we use a ship data set that has 6000 images collected from Donging port, Shandong, China. We have tested both Yolo Tiny and DNet, running on TX2 and GTX TITAN X. In addition, 4700 images are used for training and 1300 are used for testing. Table 3 shows the test results. We can see that DNet achieves much higher energy efficiency with a little lower accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Versions** & **Jeston TX2** \\ \hline GPU & NVIDIA Pascal 256 CUDA core \\ CPU & 64-bit Denver 2 and A57 CPUs \\ Memory & 8 GB 128-bit LPDDR4 \\ Storage & 32 GB eMMC \\ Video Encode & 4 K \(\times\) 2 K 60 Hz \\ Video Decode & 4 K \(\times\) 2 K 60 Hz \\ Camera & 1.4 Gpix/s 2.5 Gbps per lane \\ Connectivity & 1 Gigabit Ethernet, 802.11 ac WLAN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Jetson TX2.  
Figure 6: System architecture of ESDR-DL.  
YOLOv1 splits an image into 7 \(\times\) 7 grid cells; considering the big target of ship and the limitation of computing capacity of a TX2, we decrease the grid cells to reduce model parameters. As is shown in Table 4 where efficiency is measured by FPS (Frames Per Seconds), a test is made to check this, and DNet splits an image into 6 \(\times\) 6, considering the performance-accuracy trade off.  
The \(\lambda\) for loss L in Equation (3) can be changed based on different scenarios and targets. We adjust \(\lambda\) experimentally and the results is shown in Table 5. Concluding from the tests, we set \(\lambda=\) 0.7.  
We adjust \(\lambda\) experimentally for voting strategy and test its impact on accuracy as in Table 6. We can conclude the weights from it that \(\lambda_{c}>\lambda_{b}>\lambda_{s}\). We set \(\lambda_{b}=\) 0.3, \(\lambda_{c}=\) 0.5 and \(\lambda_{s}=\) 0.2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\lambda_{b}\) & \(\lambda_{c}\) & \(\lambda_{s}\) & **Accuracy** \\ \hline
1 & 0 & 0 & 0.83 \\
0 & 1 & 0 & 0.85 \\
0 & 0 & 1 & 0.80 \\
0.33 & 0.34 & 0.33 & 0.84 \\
0.25 & 0.5 & 0.25 & 0.85 \\
0.3 & 0.5 & 0.2 & 0.86 \\
0.2 & 0.5 & 0.3 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(\lambda\) impacts on recognition accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid Cells Number** & **Detection Accuracy** & **Efficiency** \\ \hline
4 \(\times\) 4 & 0.7642 & 59 \\
5 \(\times\) 5 & 0.8310 & 51 \\
6 \(\times\) 6 & 0.9233 & 43 \\
7 \(\times\) 7 & 0.9251 & 38 \\
8 \(\times\) 8 & 0.9282 & 32 \\
9 \(\times\) 9 & 0.9263 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Grid cells number test for DNet.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Device** & **Accuracy** & **Efficiency (FPS)** & **Power (w)** & **Energy Efficiency (fps/w)** \\ \hline Tiny YOLO & TX2 & 0.9316 & 18.91 & 7.84 & 2.41 \\ DNet & TX2 & 0.9233 & 34.87 & 7.73 & 4.51 \\ Tiny YOLO & GTX TITAN X & 0.9492 & 155.24 & 180 & 0.86 \\ DNet & GTX TITAN X & 0.9297 & 298.43 & 177 & 1.68 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of Tiny YOLO and DNet.
## 3 Results and Discussion
### Metabolic Response and Connections with Isotopic, Spectrophotometric, and Monitoring Data
Given that the metabolic pathways of carbon, nitrogen, and phosphorus are interdependent, variations in the availability of one of these nutrients can significantly impact the uptake and transformation dynamics of the others, ultimately shaping the nutrient equilibrium within the culture system [43]. The literature further supports that microalgae of the genus _Galdieria_, like many other species, regulate NH4+ and NO3- uptake in response to external conditions. Ammonium metabolism produces protons (H+) as a by-product, leading to a local decrease in pH, whereas nitrate assimilation increases pH by consuming protons [41]. The interplay between these processes contributes to pH stability in the culture system, depending on the balance of nitrogen sources available in the medium. Here, the implementation of a sensor system on the TL-PBR provides a significant support in the analysis of these data and to better understand these interactions. The results (Figure 7) show that pH levels oscillated around the initial value, maintaining a relatively stable pattern throughout the experimental period. This behaviour suggests that the culture system was able to maintain a chemical equilibrium over time, supporting the hypothesis of self-regulating metabolic processes that counteract excessive fluctuations in nutrient concentrations. Moreover, Figure 7 also provides valuable insight into environmental parameter variations during the experiment. In particular, it highlights the direct influence of LED lighting on temperature dynamics, showing that pH stability was maintained despite daily fluctuations in ambient temperature. Overall, these findings suggest that the lack of significant nutrient depletion or accumulation in the medium does not indicate an absence of metabolic activity but rather the presence of compensatory physiological and chemical mechanisms that dynamically regulate nutrient levels within the culture system.
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 5 Network Model
### Network Topology and System Parameters  
In order to systematically explore the interactions between the AQM-based buffer management and scheduling algorithm, we have to delve deeper to consider mobile network elements and protocols. We implemented our proposed strategy using the LTE module of the ns3 [9] simulator configured with the topology depicted in Figure 6 for single-cell and Figure 7 for the handover scenario in multi-cell topology. We used the LTE/EPC Network Simulator and Analysis (LENA) module [42] to create an end-to-end LTE network. The LENA module has all the major elements of a real LTE system including the Evolved Packet Core (EPC) and air interface Evolved UMTS Terrestrial Radio Access (E-UTRA). The LTE model in ns3 provides a detailed implementation of various aspects of the LTE standard such as adaptive modulation and coding, Orthogonal Frequency Division Multiple Access (OFDMA), hybrid Automatic Repeat Request (ARQ) etc. The ns3 implementation follows the detailed  
Figure 5: The detailed proposed packet discard strategy.  
specification of 3 GPP LTE and various versions of TCP. Hence, the results obtained in the simulation can be representative of what happens in a real system.  
The guaranteed bit rate video traffic is simulated, the remote host on the right side acts as sources, and the UEs on the left side act as sinks. TCP data senders are of the TCP-Cubic type. We used TCP-Cubic as it is the default TCP congestion control algorithm in Linux OS in real networks. The TCP packet sizes are 1000 bytes. We also used the Single Input Single Output (SISO) transmission mode for both UE and the eNB. The remote host is connected to the LTE core network via a wired link with the link capacity of 10 Mbps (1250 packets per second) and the propagation delay on this link is 50 ms. The application data rate is 100 Mbps. The thresholds for the packet dropping function are set as \(Mint_{th}=20\) packets and \(Max_{th}=3\times Min_{th}\) packets and the maximum packet dropping probability is set as 0.1. The eNB RLC buffer size is 100 packets. The total simulation time is 75 s. Table 1 shows the details of some of the important simulation parameters and their values.  
Figure 6: Single-cell LTE network topology.  
Figure 7: Multi-cell LTE network topology.
# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 2 Challenges in System Design of Energy Autonomous Wireless Sensors  
One of the most important challenges in the design of WSN is to ensure sustainable operation of nodes while maintaining a good connectivity and network longevity. For a supply by ambient energy, the profile of the ambient source needs to be considered for deciding about suitable energy converters, optimizing their parameters and size and assessing the expected total energy (see Figure 1). Thereby, not only the amount of energy is important, but also the energy availability and how it is distributed and fluctuating over time, the so-called energy profile.  
Figure 1: Challenges for the supply of WSNs with energy harvesting.  
Energy converters need to realize a good yield and a high conversion efficiency where they are subjected to changes of environmental conditions and unavoidable aging effects. As application requirements need to be fulfilled along operation life; only improving energy efficiency of harvesters or using an energy storage unit with a higher capacity is not sufficient. The energy management should fulfill a stable energy availability along the lifetime despite fluctuating, non-stable and not available timely energy income, so that the sensor system operation is not affected by changes of environmental conditions or along time periods with a low energy availability. The energy source may be not sufficiently available or may not be reliably available all the time, so that solutions need be searched to cover these limits.  
The sensor system needs to be designed for low-power consumption and have different operation modes which can be selected depending on the energy availability, so that, if the energy is no longer sufficient for the operation mode with the lowest energy consumption, the WSN can start automatically and come back to operation mode once it obtains enough energy form ambient sources.  
All these aspects are strongly related to energy availability and system design of the wireless sensor node and sensor network. It is therefore important to take care of them during the design of all components and modules in wireless sensors by efficiency improving the energy converters, energy management and energy-aware communication in WSNs. The design process is therefore interdisciplinary and needs special care to realize practicable systems and fulfill the needs of the application in an optimal manner.
# Optimization of a Multi-Type PMSM Based on Pyramid Neural Network
## 3 Prediction Model and HPMSM Optimization
### Optimization Results and Analysis  
The setting of model parameters has a great influence on prediction accuracy. The parameters of the QBA in this paper were set as follows: the initial population was set to 200; the maximum number of iterations max\({}_{gen}\) was set to 1000; the maximum and minimum values of the emission frequency were set to 1 and \(-1\), respectively; and the expected criterion \(\delta\) was set to 0.01.  
QBA method based on PNN model was used to optimize PMSM. In four type of PMSM, the surface mounted type has the best performance after optimization. Its evolution is shown in Figure 8. In Figure 8a, the total loss decreases with the increase of the number of iterations and becomes stable after 200 iterations. In Figure 8b, the output torque increases with the increase of the number of iterations and becomes stable after 100 iterations. It shows that the convergence of the model is good and the optimal solution of the algorithm has been reached at the maximum number of iterations. The optimal solution obtained after multiple iteration optimization is shown in Table 5.  
Figure 7: Flow chart of the QBA.  
According to the comparison of the magnetic dense cloud map, output torque, loss data and the waveform of the back EMF, it could be seen that the QBA optimization method adopted in this paper based on the PNN model could significantly improve the performance of HPMSMs and achieved the purpose of the optimization design.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Stator Core Loss** & **Copper Loss** & **Eddy Current Loss** & **Wind Friction Loss** & **Stray Loss** \\ \hline Before & 483 & 53.1 & 44 & 311 & 47 \\ optimization & 345 & 49.8 & 31 & 300 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of loss value of HPMSM.  
Figure 11: No load back electromotive force.  
Figure 10: Output torque of the HPMSM. (**a**) Before optimization (**b**) After optimization.
# Push Attack: Binding Virtual and Real Identities Using Mobile Push Notifications
## 1 Introduction
### Privacy Implications  
The proposed attack is collocated in the wide area of privacy threats on encrypted traffic, often referred to as side-channel information leaks [8]. This area and the related privacy implications have been extensively investigated for the desktop scenario (e.g., [9]). It is recently attracting increasing attention for the mobile scenario. Several works addressed privacy threats and implications because of specific technical and usage characteristics of mobile devices. These include the typical \"one app at a time\" use [10], the possible privacy leakage from the use of sensors [11], location privacy [12] or, more in general, privacy in mobile environments [13].  
This paper, to the best of our knowledge, is the first to deal expressly with using mobile push notifications to bind real and virtual identities.  
As already expressed by the authors of [14], the disclosure of virtual and real-world identities could raise severe privacy concerns. Linking them can cause even more damage to an individual's privacy.  
Indeed, apparently shielded by pseudonyms and ciphered traffic data (as reported in [2]), people express themselves more freely on politics, religious or personal topics. Linking their virtual to their real identity could then be embarrassing or even harmful (in 2016, Saudi Arabia sentenced a Twitter user to 10 years in prison for publishing 600 tweets \"which spread atheism\").  
Finally, the recent IETF Internet Draft [4] on push notifications also expresses concerns about privacy implications even if from the application's point of view.
## 3 Results and Discussion
### Correlation between Chlorine Removal and Mass Loss  
To study the correlation between chlorine removal and the mass loss, the results of the organic chlorine removal efficiency were plotted using the correlation developed above. Figure 3 shows that the efficiency increased as the mass loss increased, and all the chlorine from PVC was released after the mass loss reached \(\sim\)40%. The behavior in Figure 3 was unique to the specific waste blend, i.e., the chlorine removal efficiency vs. mass loss would depend on the waste composition. As noted above, chlorine removal depended only on temperature and residence time. However, for a given blend, the results of Figure 3 were useful as they provided a predictive behavior that could be used for design considerations.
# Design and Experimental Study of Potato-Soil Separation Device for Sticky Soils Condition
## 2 Structure and Working Principle  
To address the mentioned problems (e.g., soil with high moisture content, large viscosity and poor separation efficacy) in the harvesting process for winter idle paddy fields of Southern China, a new type of potato harvester was designed. Figure 1 shows the 3D model built with Solidworks.  
The potato-soil separation device is the most important part of the harvester which mounted at the rear of the digging shovel, comprising a soil-crushing mechanism, a first-stage lifting chain, a debris-cleaning mechanism, as well as a second-stage lifting chain. A rotating roller filled with a hard tooth was mounted on the first-stage lifting chain, and the direction of rotation was counterclockwise. On the whole, the mechanism of the lifting chain with first-stage and second-stage lifting chains comprised chain rods with a particular gap. A support wheel was provided on the bottom of the first-stage lifting chain to form a bulge of chain face, and the second-stage lifting chain was located at the back end of the potato-soil separation device. The debris-cleaning mechanism was mounted in the middle between the first-stage and second-stage lifting chains, which comprised a rubber roller and smooth roller with relative rotation. Furthermore, the spiral-shaped prism was provided on the rubber roller.  
Figure 1: 3D model of potato harvester. 1—Digging shovel; 2—transmission system; 3—frame; 4—gear box; 5—traction frame; 6—soil-crushing mechanism; 7—first-stage lifting chain; 8—rubber roller; 9—smooth roller; 10—second-stage lifting chain; 11—walking wheel; 12—lifting chain support wheel.  
Figure 2 is a schematic diagram of the separation device abstracted from the 3D model by using AutoCAD (Autodesk Inc., San Rafael, CA, USA), which illustrates the operating principle of the potato-soil separation device.  
By complying with the different action modes of the machine on the potato-soil mixture, the functional areas can fall into five types, i.e., shearing, bending, throwing, cleaning, and conveying. The potato-soil mixture was dug by digging shovel and then transferred to the potato-soil separation device. In the shearing area, the potato-soil mixture was broken initially by the shearing and collision of a rotating roller filled with a hard tooth. In the bending area, when the potato-soil mixture moved to the bulge of the chain face, bending deformation and failure could be generated by gravity action. In the throwing area, the potato-soil mixture was thrown up as impacted by the movement of the first-stage lifting chain, and it was collided and then dispersed when falling. In the cleaning area, the gripping force was determined by the relative rotation between the rubber roller and the smooth roller to clean the inclusion of the flexible haulm and mulching film. Moreover, the potatoes and large soil blocks were continuously transported backwards. In the conveying area, the potatoes, large soil blocks, and stones were transported backwards and fell on the field through a second-stage lifting chain.
# CFD Analysis on the Heat Dissipation of a Dry-Lubricated Gear Stage
## 4 Conclusions  
This study introduced and applied a consecutive simulation methodology for thermal CFD based on the FVM to investigate the heat dissipation of a dry-lubricated gear stage. Using the methodology developed, it was possible to investigate the influence of gear geometry and circumferential speed on the heat dissipation under atmospheric conditions. The sliding mesh method was used to model the gear kinematics, which was accompanied with a slight increase in the center distance. For sake of comparability, the same temperature boundary conditions were considered for both gear variants. The following conclusions can be drawn:  
* A circulating flow is formed in the gearbox due to the displacement of air by the gears and backflow to the gears.
* Spur gears show a distinct radial displacement and a symmetrical axial backflow of air to the teeth. Helical gears show a distinct axial air flow due to conveying effects.
* High heat transfer coefficients are particularly present on surfaces that interact strongly with air, e.g., on the leading tooth flanks.
* Surface-averaged heat transfer coefficients show higher values for helical gears compared to spur gears.
* The simplified Nusselt correlation can predict the order of magnitude of the simulated heat transfer coefficient as well as its trend over the circumferential speed.
* The specific influence of gear geometry and the fluid flow on the heat transfer coefficient cannot be addressed by simple correlation analysis.  
Future work may include iterative parameterization of thermal boundary conditions based on coupled finite elements calculations to determine component temperatures of dry-lubricated gearboxes and experimental validation. Furthermore, geometrical optimization measures can be investigated to improve heat dissipation and consequently to avoid thermal load limits. The developed simulation methodology can also be adapted to multiphase thermal CFD simulations to investigate oil-lubricated gearboxes.  
Conceptualization, L.H., C.P. and T.L.; methodology, L.H. and T.L.; software, F.D. and L.H.; formal analysis, L.H., C.P. and T.L.; investigation, F.D. and L.H.; resources, K.S.; writing--original draft preparation, L.H.; writing--review and editing, C.P., T.L. and K.S.; visualization, F.D. and L.H.; supervision, T.L.; project administration, K.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the research project STA 1198/17-2 of the priority program SPP 2074 II supported by the German Research Foundation e.V. (DFG). The authors would like to thank the DFG for the sponsorship and support received.  
Not applicable.  
The authors gratefully acknowledge the LRZ for hosting the numerical calculations and [PERSON] for aiding the theoretical investigations and discussion.  
The authors declare no conflict of interest.
## 4 Coexistence Solutions for Sync-FT Operation  
In what follows, we describe the details of the proposed solutions. The approach of the proposed solutions is to assign a free-riding link a penalty for its subsequent channel access following a free-ride transmission so that each link of an MLD will experience a similar amount of backoff time as a legacy device does in the same channel. We propose four features for assigning penalties to free-riding links. Then, various combinations of these features are considered as solutions, as listed in Table 1. The proposed architecture of an MLD is depicted in Figure 9 where the proposed solutions are implemented by the interaction between the backoff processes of individual links through the backoff coordinator of the UMAC. The entire procedure for channel access by a link is illustrated  
Figure 5: Synchronous operation (Sync) scheme for multi-link channel access.  
Figure 8: Issue concerning the coexistence of EHT MLDs with legacy devices.  
Figure 6: Sync-PL operation.  
Figure 7: Sync-FT operation. (**a**) STR MLD, (**b**) Non-STR MLD.  
in Figure 10 and its pseudocode is given in Algorithm 1, where a new flag variable named Scheme indicates the features to be activated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Solution** & **Repick** & **Switch** & **Double + Repick** & **Double** & **Repick + Comp** \\ \hline Repicking a backoff count & ✓ & & ✓ & & ✓ \\ \hline Doubling CW & & & ✓ & ✓ & \\ \hline Switching to another CW & & ✓ & & & \\ \hline Backoff count compensation & & & & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature combinations considered in the proposed solutions.  
Figure 10: Flowchart of the channel access procedure of a link under the Sync scheme with the proposed solutions (black-outlined boxes + black and red arrows: legacy operations, all boxes + black and blue arrows (excluding red ones): new operations).  
Figure 9: Proposed architecture of an MLD.