esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 3 The Knowledge of Underwater Acoustic Sensor Networks
### The Movement of Underwater Sensor Nodes  
Another challenge of the routing protocol design is the continuous movement of underwater sensor nodes. As we know, different motion models have different effect on the same routing protocol, so when we design the routing protocol, we must take the mobility of the underwater sensor node into consideration. There are already many motion models for terrestrial sensor nodes [36], such as the random walk mobility model, the random waypoint mobility model, the random direction mobility model, and the Guess-Markov mobility model. However, these motion models mainly applied in 2-D network architecture, but the network is 3-D in the underwater environment, so these motion models cannot be directly applied in underwater wireless sensor networks. The research on hydrodynamics shows that the movement of underwater objects closely relates to many factors, such as the water current and the water temperature. In different environments the motion models are different. For instance, the movement behaviors are different in a river and near the seashore. However, as discussed in [37; 38], the movement of underwater objects is not a totally random process. Temporal and spatial correlation is inherent in such movement, which makes their mobility patterns predictable. In [39], the authors propose a motion model for underwater sensor networks.
# A Tutorial on Retroreflectors and Arrays Used in Satellite and Lunar Laser Ranging
## 5 Science Applications of SLR Impact the Satellite Array Design
However, one can narrow the temporal response of the larger-diameter satellite while maintaining a high cross-section at low incidence angles by either using hollow rather than solid cube corners (as illustrated in Figure 6) or by recessing solid cubes inside hollow tubes to limit  
Figure 5: Pulse spreading induced by spherical LEO and GEO satellites (**a**). In multiphoton detection mode, the returned light pulse is the sum of the ultrashort (–100 psec) light pulses generated by the individual retroreflectors, which are reduced in intensity at the higher beam incidence angles (**b**). In multiphoton mode, the detector in the ground station sums over the individual retro returns before sending them to the timing electronics, thereby degrading the range precision. In single-photon mode, returns from individual reflectors are received at kHz rates and averaged over time to form “Normal Points”. the response to cubes within a narrower range of incidence angles. The latter approaches effectively eliminate the weaker contributions of the pulses to the right in Figure 5b. Finally, because GNSSs and geosynchronous satellites generally perform a utilitarian function (e.g., Earth observation, communications, navigation, etc. ), the nadir side of the satellite approximately faces the Earth Center of Mass (CoM). For the typical maximum zenith tracking angle of 70\({}^{\circ}\), the beam incidence angles from the array normal can vary from 0 to \(\beta\), where  
\[\beta=asin\bigg{[}\frac{R_{E}}{R_{E}+h}sin110^{\circ}\bigg{]} \tag{9}\]  
For GNSS satellites at \(h\) = 20,000 km, \(\beta\) = 13.1 deg, and for GEO satellites at \(h=36,000\)km, \(\beta\) = 8.2 deg. The smaller range of incidence angles ensures: (1) near-maximum-strength returns from a planar array; and (2) limited pulse spreading, especially if the array is compact in size and the retros are densely packed together to achieve the required cross-section. Nevertheless, the maximum flat-panel-induced laser pulse spreading per linear foot of array due to the zenith tracking angle is still 474 psec (7 cm) and 292 psec (4.4 cm) for GNSS and GEO satellites, respectively. Thus, typical rectangular or square planar arrays can introduce an angle-dependent broadening of the pulse return, resulting in lower-ranging precision. Although a planar circular array with the same area would result in more uniform pulse spreading, the use of a segmented sphere mounted on the nadir face of the satellite would largely eliminate angle-dependent pulse spreading and range biases.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Evaluation
#### 5.3.2 Comparison With Other Algorithms  
There are many well-known state-of-the-art research approaches for RFID-based indoor localization. Table 1 shows a comparison of the proposed method (DNCL) with other five advanced methods (SARFID, STPP, Tagoram, ReLoc, HyLoc) in terms of system deployment and localization performance. Since we focused on RFID-based mobile platforms  
Figure 11: The figurative localization result of the experiment scenario.  
Figure 12: CDF curve of localization error in different axes.  
for automated warehouse management scenarios, the entire complexity on the system ought to be under consideration.  
All of the comparative methodologies listed in the table are based on COTS RFID devices. STPP and HyLoc utilize one antenna, although they all achieve just single-axis-up localization for the item. SARFID, Tagoram, and ReLoc all employ two commercial antennas. ReLoc requires setting the tilt angle and space between the antennas and the shelf, distinguishing the horizontal order on the basis of the RSS difference between the two tilted antennas, and using the phase information for lateral localization. The experimental equipment deployment is relatively complex. The STPP algorithm generates phase profiles for individual tag using mobile antennas and directly estimates the sequence of tags by the exploration of the spatio-temporal correlation of the phase profiles, which requires a comparative reference based on the phase profiles of neighboring tags. Both SARFID and Tagoram are based on coordinate systems. They get absolute locations by employing phase information to create holograms that pinpoint the coordinates of the tags. Tagoram further proposed differential augmented holograms, but they both require multiple antennas for phase reading. HyLoc uses the distance difference obtained from the phase difference to transform the positioning problem into the solution of hyperbolic equations, which can only achieve localization when the tags and antenna are positioned on the same two-dimensional plane. In contrast, our method based on the tag phase information obtained from a simple one-dimensional scan using a single antenna could achieve spatial coordinate localization of tagged objects. To conclude, our method is more suitable for COTS RFID devices in mobile scenarios than other systems.
## 3 Results
### Quantitative Analysis of Observation Space
#### 3.3.2 Pairwise Observation Distribution Comparison  
The Pairwise Total Variation (TV) distance (Table 4) and Maximum Mean Discrepancy (MMD) (Table 5) confirm the qualitative findings. * **Freeway** and **Enduro** show extremely low TV distances (mostly \(<\)0.1 for Freeway, \(<\)0.15 for Enduro) and MMD values (mostly \(<\)0.015 for Freeway, \(<\)0.025 for Enduro), confirming that all agents have statistically very similar observation distributions. * **Boxing_Qbert**, and **KFM** show very high TV distances, often approaching 1.0, indicating that many agent pairs explore almost entirely different sets of state clusters. For instance, the TV distance between Qbert #1 and Qbert #2 is 0.831. In KFM, the low-performing agent #5 has TV distances \(>\) 0.7 against all other agents. The MMD values are correspondingly high, quantitatively demonstrating the stark divergence in exploration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & Alien \#1 & Alien \#2 & Alien \#3 & Alien \#4 & Alien \#5 \\ \hline Alien \#1 & 0.000 & 0.854 & 0.512 & 0.857 & 0.869 \\ Alien \#2 & 0.854 & 0.000 & 0.767 & 0.684 & 0.500 \\ Alien \#3 & 0.512 & 0.767 & 0.000 & 0.783 & 0.809 \\ Alien \#4 & 0.857 & 0.684 & 0.783 & 0.000 & 0.671 \\ Alien \#5 & 0.869 & 0.500 & 0.809 & 0.671 & 0.000 \\ \hline  & Boxing \#1 & Boxing \#2 & Boxing \#3 & Boxing \#4 & Boxing \#5 \\ \hline Boxing \#1 & 0.000 & 0.959 & 0.936 & 0.979 & 0.985 \\ Boxing \#2 & 0.959 & 0.000 & 0.971 & 0.673 & 0.565 \\ Boxing \#3 & 0.936 & 0.971 & 0.000 & 0.979 & 0.984 \\ Boxing \#4 & 0.979 & 0.673 & 0.979 & 0.000 & 0.252 \\ Boxing \#5 & 0.985 & 0.565 & 0.984 & 0.252 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pairwise Total Variation (TV) distance between agent observation distributions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Agent** & \(H\) & \(N_{eff}\) & **Cov** & **G** \\ \hline Enduro \#1 & 4.43 & 84.01 & 0.98 & 0.99 \\ Enduro \#2 & 4.39 & 80.82 & 0.99 & 0.99 \\ Enduro \#3 & 4.42 & 82.96 & 1.00 & 0.99 \\ Enduro \#4 & 4.44 & 84.72 & 0.98 & 0.99 \\ Enduro \#5 & 4.40 & 81.72 & 0.99 & 0.99 \\ \hline Freeway \#1 & 4.55 & 94.51 & 1.00 & 0.99 \\ Freeway \#2 & 4.54 & 94.09 & 1.00 & 0.99 \\ Freeway \#3 & 4.54 & 93.84 & 1.00 & 0.99 \\ Freeway \#4 & 4.54 & 94.05 & 1.00 & 0.99 \\ Freeway \#5 & 4.54 & 94.00 & 1.00 & 0.99 \\ \hline KFM \#1 & 4.26 & 70.70 & 0.96 & 0.98 \\ KFM \#2 & 4.24 & 69.09 & 0.90 & 0.98 \\ KFM \#3 & 4.31 & 74.49 & 0.95 & 0.98 \\ KFM \#4 & 4.26 & 70.71 & 0.93 & 0.98 \\ KFM \#5 & 3.52 & 33.74 & 0.53 & 0.96 \\ \hline Pong \#1 & 4.15 & 63.51 & 0.74 & 0.98 \\ Pong \#2 & 3.99 & 54.04 & 0.69 & 0.98 \\ Pong \#3 & 4.45 & 85.65 & 1.00 & 0.99 \\ Pong \#4 & 4.10 & 60.40 & 0.88 & 0.98 \\ Pong \#5 & 3.91 & 49.71 & 0.93 & 0.97 \\ \hline Qbert \#1 & 3.48 & 32.55 & 0.54 & 0.95 \\ Qbert \#2 & 3.19 & 24.36 & 0.45 & 0.94 \\ Qbert \#3 & 3.49 & 32.73 & 0.54 & 0.95 \\ Qbert \#4 & 2.98 & 19.69 & 0.45 & 0.92 \\ Qbert \#5 & 3.15 & 23.38 & 0.54 & 0.94 \\ \hline \hline \end{tabular} \(H\): entropy, \(N_{eff}\): effective support size, Cov: Coverage Ratio, \(G\): Gini-Simpson Index.  
\end{table} 
Table 3: _Cont._
# Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link
## 3 Results and Analysis
### Bandwidth of the LED Receiver Circuit  
The bandwidth of the LED receiver circuit with the proposed TIA is practically investigated to identify the maximum data rate and its simulated Bode plot is shown in Figure 4. The Bode plot suggests that the TIA circuit acts as a low pass filter with a 3 dB bandwidth of around 140 kHz. For a non-return-to-zero (NRZ) OOK signal with one level per bit, this bandwidth allows a maximum bit rate of 280 kbps. To verify this result practically, the rise time \(t_{r}\) of the received signal is measured (calculated between 10% and 90% on the rising voltage edge) [23]. \(t_{r}\) is found to be 3.8 us, which translates to a bandwidth \(BW_{3 dB}\) of 90 kHz according to the equation [23]:  
\[BW_{3 dB}\ =\ \frac{0.35}{t_{r}} \tag{3}\]  
This 90 kHz bandwidth allows OOK data transmission at speeds up to 180 kbps. For that reason, data rates of only up to 200 kbps are investigated. At higher frequencies, the TIA fails to saturate to the upper voltage rail, hence acting as a low pass filter/integrator and turning the received square wave close to a triangular shaped waveform.
## 5 Experimental Results
### Results of the Proposed Packet-Level IDS
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Agent** & **Discount Factor** & **Validation Accuracy** \\ \hline \multirow{4}{*}{**DQN-1 DCNN**} & **0.1** & **98.78\%** \\  & 0.5 & 95.21\% \\  & 0.9 & 87.12\% \\  & 0.1 & 96.07\% \\
**DQN-CNN** & 0.5 & 93.22\% \\  & 0.9 & 86.24\% \\  & 0.1 & 96.17\% \\
**PG-1 DCNN** & 0.5 & 90.15\% \\  & 0.9 & 79.67\% \\  & 0.1 & 96.03\% \\
**PG-CNN** & 0.5 & 87.12\% \\  & 0.9 & 70.69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performances of different discount values on four agents. \begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Agent** & **Traffic** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{DQN-1 DCNN} & Normal & 99.32\% & 100\% & 99.66\% \\  & PortMap & 98.04\% & 98.95\% & 98.49\% \\  & NetBIDS & 98.87\% & 99.26\% & 99.06\% \\  & LDAO & 98.78\% & 98.85\% & 98.37\% & 98.61\% \\  & MSSQL & 98.47\% & 97.30\% & 97.86\% \\  & UDP & 98.95\% & 99.62\% & 99.26\% \\  & UDP-Lag & 98.02\% & 98.31\% & 98.61\% \\  & SYN & 98.64\% & 97.83\% & 98.23\% \\ \hline \multirow{8}{*}{DQN-CNN} & Normal & 96.91\% & 99.68\% & 98.27\% \\  & PortMap & 95.67\% & 93.53\% & 94.59\% \\ \cline{1-1}  & NetBIOS & 97.35\% & 95.75\% & 96.55\% \\ \cline{1-1}  & LDAO & 95.76\% & 96.95\% & 96.35\% \\ \cline{1-1}  & MSSQL & 95.06\% & 95.06\% & 96.03\% \\ \cline{1-1}  & UDP & 95.32\% & 96.00\% & 95.66\% \\ \cline{1-1}  & UDP-Lag & 95.05\% & 94.92\% & 94.99\% \\ \cline{1-1}  & SYN & 97.14\% & 94.87\% & 95.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Validation results of DQN-1 DCNN with discount factor value = 0.1. Finally, we evaluate our intrusion detection system on the test set with \(\gamma\) = 0.1 and \(\lambda\) = 0.7. Results are shown in Table 13. Due to the existence of anomaly traffics, the general accuracy has declined greatly to 84.27% compared with the validation accuracy at 96.07%. With respect to all classes, the DQN-1D-CNN agent can attain relatively high performances on those known types. With regard to anomaly types, although these anomaly types are completely blind to our system, 71.05% of them can still be detected.
## 3 Discussion
### Technical Variables  
Thermographic images need to be taken at the same distance and viewing angle from the subject, since the variation of these parameters affects the temperatures recorded [42]. If things become complicated, as pigs are living, moving subjects, it could be useful take infrared videos instead of single images.  
The proper use of a professional infrared camera is necessary and only a routinely calibrated device can be used. Currently, a largely range of commercial models are available, but a _microbometric sensor_ with no less than 320 \(\times\) 240 pixels is recommended [38]; both spatial and thermal resolutions suitable for applications on livestock can be guaranteed by this kind of tool.  
As seen in the previous paragraphs, many variables can impact temperature relief, with the creation of low-quality infrared images. As a consequence, a great number of thermal images are usually collected beforehand in experiments and need to be processed and categorized before the temperature analysis [43].  
Furthermore, the thermocamera records the skin temperature in various parts of the animal's body in the same frame, so the specific region of interest, ROI, needs to be determined and selected. Finally, we have to decide which temperature is better to analyze within the observed ROI. When using infrared thermography in animal husbandry, special attention is often paid to the maximum temperature value detected and not to the average value [38]. That's because the lower and average values could be affected by the presence of dirt and liquid, instead the maximum temperature should more accurately describe the actual skin temperature.  
As a result, recent works are mainly focusing on the development of procedures for managing a great number of infrared images, using up-to-date tools, such as computer vision technologies [38], convolutional neural networks [44], and deep learning techniques [45], in order to enhance spatial resolution, reduce noise, average images over time, detect ROI, and identify the needed temperature changes in a completely automated way [41,46].
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 4 Construction of Our System
### Atomic Deal
#### 4.4.2 Transaction and Verification Phase  
After successful negotiation, the transaction and verification phases begin. Then, chainB uses resolve (domain.info) to convert the domain name information into its IP address. It creates and sends a transaction packet, with this IP address (domain.res), to the Relay Platform.  
The relay platform receives the transaction and forwards it to chainA. Then, chainA verifies its authenticity using is.valid.response \((tx,\text{domain.res},B.ID)\). This dual-phase approach ensures the atomicity of the transaction, requiring a successful negotiation phase before further action.
## 2 Concepts and Structure  
To achieve modularization and standardization in ORS satellite design, the satellite is composed of modular cabins that have certain computing ability. Wire connections are eliminated except for the power line. All the data exchanges during the modules are accomplished via a wireless network. The structure of the ISWN is shown in Figure 1 below.  
As shown in the figure, the satellite is made up of dozens of RF communication nodes. The base node consists of a central processer CPU and RF communication chips (RFIC), which complete both radio communications and failure diagnosis. Compared to AISC circuits, the application of COTS devices can effectively reduce the time and cost during the AIT phase of the satellite design, which meets the flexibility requirement of the ORS mission. As shown in Figure 2, a simplified model of the seven layers OSI is presented, where the intra-satellite wireless sensor and data transmission network is separated into three layers. In this article, we mainly focus on the physics layer and the MAC layer.
## 4 Tool Application to Evaluate Pollutant Emissions  
For marine engines, pollution production is strictly related to their working. Indeed, pollutant substances such as NOx, CO, CO\({}_{2}\), THC, O\({}_{2}\), and SO\({}_{2}\) are a natural consequence of the combustion process. However, their production can be mitigated through more efficient and high-performing engines and combustion process. In this regard, the use of a lower quantity of fuel, without affecting the output power, is one of the first measures to apply. Furthermore, also a cleaner combustion  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field of Interest** & **Output** & **Symbol** & **Unit** \\ \hline Engine & Effective Compression Ratio & ECR & / \\ \hline \multirow{6}{*}{Turbocharger} & Compressor efficiency & \(\eta_{SV}\) & / \\  & Turbine efficiency & \(\eta_{T}\) & / \\  & Overall efficiency & \(\eta_{TI}\) & / \\  & Compressor pressure ratio (total to total) & \(\Pi_{V}\) & / \\  & Turbine pressure ratio (total to total) & \(\Pi_{T}\) & / \\  & Corrected turbocharger speed & \(n_{298}\) & rpm \\ \hline \multirow{3}{*}{Fuel} & Brake Specific Fuel Consumption & _BSFC_ & g/kWh \\  & ISO Correction Brake Specific Fuel Consumption & \(\mathit{BSFC}_{\mathit{corr}}\) & g/kWh \\  & Total Brake Specific Fuel Consumption & \(\mathit{BSFC}_{\mathit{total}}\) & g/kWh \\ \hline \multirow{6}{*}{Emissions} & NO\({}_{\text{X}}\) emissions & NO\({}_{\text{X}}\) & g/kWh \\  & CO emissions & CO & g/kWh \\ \cline{1-1}  & CO\({}_{2}\) emissions & CO\({}_{2}\) & g/kWh \\ \cline{1-1}  & THC emissions & THC & g/kWh \\ \cline{1-1}  & O\({}_{2}\) emissions & O\({}_{2}\) & g/kWh \\ \cline{1-1}  & SO\({}_{2}\) emissions & SO\({}_{2}\) & g/kWh \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identified output.  
process ensures a significant reduction of pollutant emissions. As a result, in order to achieve the best performance from a TDE, it's fundamental to keep a continuous monitoring and control of the engine while running.  
As regards the paper's subject, the evaluation of emissions will be thoroughly examined hereafter in terms of both the formulation's explanation and Emission Routine's logic. As a reference for the thermodynamic quantities, the WIT (Wartsila Italia) Convention (in Appendix A) is used.  
To define the polluting values in terms of g/kWh, the exhaust mass flowrate must be calculated. As mentioned above, two methods have been identified:  
1. By means of the Air Nozzle approach, from which the incoming air mass flowrate to the compressor can be measured, and then the exhaust mass flowrate can be calculated by simple mathematical steps; and
2. By means of the Carbon Balance method, from which the exhaust mass flowrate can be directly evaluated.
# Algorithmic Matching Attacks on Optimally Suppressed Tabular Data
## 2 Background
### Overview of the Cell Suppression Problem (CSP)  
We protect sensitive information in tabular data by suppressing the values of unsafe cells that are determined by applying some sensitivity rule [9] to each cell of the table. For example, we apply the minimum frequency rule to a frequency table such that each cell will be suppressed if its value  
Figure 1: Disclosure scenarios with example frequency and magnitude tables.  
is less than a given threshold for the minimum frequency. We call this process primary suppression and call suppressed cells in primary suppression primary suppressed cells accordingly. However, to perform primary suppression on a table is not sufficient to protect sensitive cell values because it is usually trivial to restore the original values by utilizing linear relationships among cell values concerning marginal sums. We, therefore, perform secondary suppression that additionally suppresses the values of non-sensitive cells to prevent an adversary from recomputing the cell values of primary suppressed cells. We call non-sensitive cells that are suppressed in secondary suppression secondary suppressed cells.  
We illustrate this two-stage cell suppression procedure with an example of a frequency table in Figure 2. We first determine sensitive cells using the minimum frequency rule with a threshold value of 10. In this example, we determine that cell (\(M_{1}\), \(P_{1}\),) is sensitive and suppress that cell value in the process of primary suppression. Since it is trivial to restore the suppressed value by considering a linear relationship either on the row sum or the column sum, we additionally suppress three non-sensitive cells (\(M_{1}\), \(P_{2}\)), (\(M_{2}\), \(P_{1}\)), and (\(M_{2}\), \(P_{2}\)).  
It is, however, not clear whether this particular choice of secondary suppressed cells adequately protects sensitive information in the primary suppressed cell. Also, we need to minimize information loss due to secondary suppression while protecting the value of the primary suppressed cell. We therefore define CSP at a high level as follows:  
**Definition 1** (Cell Suppression Problem (CSP)).: _Given a secondary suppressed table \(T\) and a set \(S\) of primary suppressed cells in \(T\), CSP determines a set \(S^{\prime}\) of secondary suppressed cells with the minimum information loss under the constraint that the value of each primary suppressed cell \(p\in S\) is properly protected._  
We next describe the security definition on a primary suppressed cell in Section 2.3 and formulate the optimization problem of minimizing information loss in the framework of integer linear programming (ILP) in Section 2.4.