esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 5 Conclusions  
This paper investigates the hybrid sensor deployment problem for highways. To fulfill this goal, combination types of sensors are considered as variables, and an arc-based mathematical model is proposed. In the optimization model, the real-time traffic data processing is elaborated by considering the constraint of the MEC server's capacity. Due  
Figure 11: The accuracy rate and coverage rate curves.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Equipment**} & \multicolumn{8}{c}{**Ratio**} \\ \cline{2-10}  & **0** & **0.05** & **0.1** & **0.15** & **0.2** & **0.25** & **0.3** & **0.35** \\ \hline Camera & 20 & 24 & 25 & 25 & 23 & 23 & 25 & 24 \\ RSU & 3 & 1 & 0 & 0 & 1 & 1 & 0 & 1 \\ Server & 6 & 5 & 5 & 6 & 6 & 5 & 6 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of required sensors and servers.  
to the limitation of computation time, a two-step heuristic search algorithm is adopted to find optimal solutions. This method is tested on the background of a highway in Jinan. The results show that the combination type {camera, camera} is more favored to be selected. The optimal deployment plan is affected by the budget, communication radius, and road shapes. When the budget is given, the total accuracy rate will be decreased by extending the length of sloped roads. Although this paper simultaneously considers the locations of sensors and servers, the connection between servers and sensors is simply stated via the capacity constraint. In future research, the packet loss rate, latency, and other factors could be added to enrich this research. Furthermore, the operating costs of servers and sensors, e.g., energy costs and labor costs, should be considered for evaluating the feasibility of deployment plans. Long-term planning would be more valuable for the hybrid sensor deployment problem, which could be taken into consideration in future studies.  
Conceptualization, X.T.; Methodology, X.T., M.L., Z.C.; Validation, X.T., M.L., Z.C.; Investigation, X.T., M.L., Z.C.; Writing--original draft, X.T., Z.C.; Writing--review and editing, M.L., Z.C.; Supervision, Z.C., X.T. All authors have read and agreed to the published the version of the manuscript.  
This research was partially supported by the Natural Science Foundation of Shaanxi Province under Grant 2023-JC-QN-0778, the Foundation Research Funds for the Central Universities, CHD (No. 300102323101), and partially supported by the Key Science and Technology Projects in the Transportation Industry of the Ministry of Transportation (No. 2021-ZD2-047); the Shandong Transportation Science and Technology Planning Project (No. 2021B49).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Analysis of the Accessibility of the Swinoujscie-Szczecin Fairway for Deep-Draught Ships
## 1 Introduction
### Strategic Role of the Fairway in Maritime Transport
When all these factors are of a constant value, the UKC is greater, which can lead to a vessel's draught being reduced more than necessary. As a result, ships carry less cargo, which reduces the economic benefits for ports and shippers. The permissible draught of a ship depends on the available amount of water under the keel (Figure 1) and is determined by the ship's speed on a waterway with certain parameters [9]. The study determines the permissible draught of the \"maximum ship\" on the basis of the parameters of the upgraded Swinoujscie-Szczecin fairway with a depth of 12.5 m [10], using the assumptions of the dynamic keel water reserve determination system on this track [11]. The permissible draught of the \"maximum ship\" will be determined based on the parameters of the upgraded Swinoujscie-Szczecin fairway dredged to 12.5 m using assumptions for a system for the dynamic determination of the water reserve under the keel in this fairway using the component method and the three-factor method. The component method involves summing individual safety margins associated with key navigational and environmental factors, such as: dynamic under-keel clearance reduction due to vessel movement (squat) [12; 13; 14], wave-induced motions [14; 15], heel and trim [12], uncertainties in water level measurements [16], and a static safety allowance [17; 18]. This approach enables a detailed and conservative assessment of the required water depth along a fairway. In contrast, the three-factor method is a simplified approach in which the total UKC margin is determined based on three aggregated groups of factors: vessel characteristics [12; 17], environmental conditions (e.g., wind, waves) [14; 15], and the geometry of the waterway [17; 18]. Both methods are applied in engineering practice and are recommended in international guidelines (e.g., PIANC - Permanent International Association of Navigation Congresses) [17]. The component method offers a more precise analytical assessment, while the three-factor method is useful for quick operational evaluations. Figure 1: Components of under-keel clearance and dynamic factors affecting vessel draught in shallow water.
# PK-Judge: Enhancing IP Protection of Neural Network Models Using an Asymmetric Approach
## 5 Materials and Methods
### Neural Network Watermarking as a Noisy Channel  
Traditional approaches treat watermarking in neural networks as a data hiding problem, where arbitrary bits are injected into the model space. However, this simplistic view fails to account for the dynamic noise landscape inherent in real-world deployments and the necessary security requirements for a trustworthy verification.  
We formulate the watermarking as a noisy communication channel through [PERSON]'s noisy channel coding theorem [57]. The model parameters \(\theta\in\mathbb{R}^{d}\) act as the communication medium, the watermark is the encoded message, and noise arises from both inherent and malicious post-deployment operations. For instance, fine-tuning [27] introduces parameter shifts analogous to signal attenuation, while pruning and quantization induce structured noise akin to packet loss. Additional noise sources arise from the stochastic nature of neural network training, limited training data, the inherent randomness of optimization processes (e.g., stochastic gradient descent) [58], which destabilizes embedded watermarks. Adversarial attacks introduce deliberate distortions to erase ownership signals. This multifaceted noise landscape requires a watermarking framework that integrates robustness at cryptographic levels.  
Many watermark verification schemes [11; 12] rely on a threshold \(\tau\) of bits of the extracted watermark versus what was known by the owner when it was embedded, but this approach of relying on threshold of bits (characterized by Bit Error Rate (BER)) lends itself to several limitations. First, threshold ambiguity arises because different applications impose varying Bit Error Rate (BER) tolerances; for instance, high-security domains may require \(\tau<10^{-6}\), whereas non-critical applications may tolerate \(\tau\approx 0.2\), complicatingthreshold selection. Second, this threshold dependency enables adversaries to manipulate just enough bits to fall below the required threshold, thus invalidating ownership claims.  
The focus on threshold and BER is due to the noise inherent to a watermarking scheme in a neural network, in order to alleviate the concern of noisy watermark processes, some watermarking schemes employ small watermarks (\(k\leq 32\) bits) this design choice introduces vulnerabilities. First, computational tractability, small key spaces enable brute-force attacks. Second, scalability constraints, these limit the feasibility of multi-stakeholder allocation protocols. Lastly, ambiguity, small-sized watermarks allow for collision, the same bitstring may be the authenticator of ownership of two different (or more) neural networks.
# A Hybrid RF/FSO Transmission System Based on a Shared Transmitter
## 4 Results and Discussion
In conclusion, the proposed hybrid RF/FSO system establishes an optimal balance between transmission performance and practical deployment efficiency through a unified architecture that integrates optical and radio-frequency signal generation. This innovative co-design eliminates hardware redundancies inherent in conventional implementations, such as separate modulators and antennas typically required in dual-transmission systems, while preserving the distinct advantages of both technologies. The FSO subsystem maintains its high-bandwidth capability, whereas the RF subsystem provides weather-resilient operation essential for uninterrupted service. The architecture enables autonomous adaptation to dynamic environmental conditions: under clear atmospheric conditions, the FSO link delivers peak bandwidth performance, whereas the RF link ensures reliable baseline connectivity during adverse weather conditions such as fog or turbulence, thereby eliminating the alignment and synchronization complexities inherent in dual-transmitter architectures. Through component minimization and utilization of shared propagation pathways, the system provides a compact and energy-efficient solution suitable for applications requiring simultaneous ultra-high data rates and operational robustness, including satellite communication links and mobile network infrastructure deployments. This synergistic integration advances hybrid wireless transmission systems by addressing critical challenges in hardware complexity and environmental adaptability.
###### Abstract  
Remote-controlled, non-destructive testing is necessary to detect corrosion of the reinforced concrete structures at the Fukushima Daiichi Nuclear Power Plant (NPP) de-commissioning site. This work aims to demonstrate that laser-induced ultrasonic guided wave technology can be applied to achieve this task. Hence, accelerated electrolytic corrosion is performed on a reinforced concrete specimen fabricated by embedding a steel rod into mortar. Waveforms of the laser-induced ultrasonic guided wave on the rod are measured with a previously employed piezoelectric transducer (PZT) probe, for each fixed corrosion time. Based on the results of Fourier and wavelet transforms of the waveforms, issues concerning the detection and extent of rebar corrosion are discussed. It is exhibited that the changes in bonding strength due to corrosion are distinguishable in the frequency domain of the ultrasonic signal.  
**Keywords:** elastic wave; corrosion; structural health monitoring; decommissioning; dispersion curve; pulsed-laser; wavelet analysis +
Footnote †: journal: Applied
# Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
## 1 Introduction
The constructed dissimilarity space can represent the relationship between the multi-sources domain data and the target data. * In order to construct the defect prediction model, we propose the DM-CPDP method. After constructing the dissimilarity space, this method uses the EMD method to select the samples in the dissimilarity space as the training dataset. For the unlabeled samples converted from the target set, the KNN algorithm is used to label those samples. TrAdaBoost method is utilized to establish the prediction model based on the selected training dataset. The DM-CPDP method avoids using the original feature space to construct the defect prediction model. In the traditional defect method, the defect prediction model makes multi-source domain data be fully utilized. * To evaluate the performance of the DM-CPDP method, we compare it with other classic single-source and multi-source CPDP methods, such as TNB [8], TrAdaBoost [9], MsTrA [10], and HYDRA [11]. In addition, we compare the effects of several different dissimilarity measures and prototype selection methods. The necessity of multi-source data sets is also verified. In terms of performance metrics, we use F-measure, AUC (Area Under ROC Curve), and cost-effectiveness to measure the models performance. The experimental results show that the DM-CPDP method is much better than the other methods. The remaining parts of this paper are as follows: This paper shows the related work in Section 2. The details of the DM-CPDP algorithm is described in Section 3. We verify the performance of the DM-CPDP method and analyze the experimental results in Section 4. The final part is the summary of this paper.
# Drilling Condition Identification Method for Imbalanced Datasets
## 4 BiLSTM-GRU-Based Classification Model for Drilling Conditions
### Model Principle
#### 4.1.2 GRU Model  
GRU is a variant of the LSTM structure with a simplified design. It combines the input gate and forget gate of LSTM into a single update gate and merges the cell state with the hidden state. With the same number of hidden units, the GRU model has lower computational cost and faster training speed compared to the LSTM model, especially when processing large volumes of drilling data. This makes GRU more efficient for training on extensive datasets [32].  
The GRU network structure consists of two gates, as shown in Figure 6:  
Update gate (\(z_{t}\)): This gate determines how much of the previous memory should be retained and how much new information should be added at the current time step. Its input is the previous hidden state (\(h_{t-1}\)) and the current input (\(X_{t}\)), which, after passing through the sigmoid function, produces an output value (\(z_{t}\)) between 0 and 1. When \(z_{t}\) is closer to 1, it indicates that more of the previous state information is retained; when it is closer to 0, it suggests that more reliance is placed on the current input to update the state.  
Figure 5: BiLSTM network architecture.  
Figure 6: GRU network architecture.  
Reset gate (\(r_{t}\)): This gate controls how much of the previous memory should be retained. Its input is the previous hidden state (\(h_{t-1}\)) and the current input (\(X_{t}\)). The output (\(r_{t}\)) is also a value between 0 and 1, and it is used to control the impact of the previous hidden state (\(h_{t-1}\)) on the current computation. When the reset gate output is 0, the previous hidden state is ignored; when it is 1, the previous hidden state is fully considered.  
The candidate hidden state \(\widetilde{h}_{t}\) is obtained by concatenating and linearly transforming the scaled previous hidden state \(h_{t-1}\) (adjusted by the reset gate) and the current input \(X_{t}\). The current hidden state \(h_{t}\) is then computed by using the update gate's output to control the relative contributions of the previous hidden state \(h_{t-1}\) and the candidate hidden state \(\widetilde{h}_{t}\). The update gate dynamically balances historical information and new data, allowing the hidden state to effectively capture changes and patterns in sequence data. This mechanism helps avoid the issues of vanishing and exploding gradients when processing long sequences, thus enabling better learning of long-term dependencies.  
The computational expressions for the GRU are as follows:  
\[z_{t}=\sigma(W_{z}X_{t}+U_{z}h_{t-1}+b_{z}) \tag{14}\]  
\[r_{t}=\sigma(W_{r}X_{t}+U_{r}h_{t-1}+b_{r}) \tag{15}\]  
\[\widetilde{h}_{t}=tan\ h(W_{h}X_{t}+U_{h}(r_{t}\times h_{t-1})+b_{h}) \tag{16}\]  
\[h_{t}=(1-z_{t})\times h_{t-1}+z_{t}\times\widetilde{h}_{t} \tag{17}\]  
where \(\sigma\) is sigmoid activation function, \(W_{z}\), \(W_{r}\), \(W_{h}\), \(U_{z}\), \(U_{r}\), \(U_{h}\) are the weight matrices, and \(b_{z},b_{r}\), \(b_{h}\) are the bias terms.
# Secure Service Proxy: A CoAP(s) Intermediary for a Securer and Smarter Web of Things
## 4 The Secure Service Proxy
### Secure Service Proxy: Implementation
#### 4.3.1 Virtual Devices and Endpoints  
Virtual device endpoints are created and deleted via the management interface. This is a straightforward REST interface that is hosted on the secure service proxy over CoAPs. As this interface handles sensitive information such as keying material, access is restricted to authorized users, which are allowed to manage endpoints and adapter chains.  
POST requests with an endpoint description are used to create a new endpoint for a virtual device. The endpoint description contains both the virtual device to which the endpoint belongs, as well as any configuration details describing the endpoint itself. This description is serialized as a JSON object in the payload of the POST request. For a plain-text CoAP endpoint, the configuration details are limited to the UDP transport port of the endpoint. For a DTLS CoAPs endpoint, the configuration also includes information about the supported cipher suites and any parameters for the cipher suites. In the current implementation, the \"TLS_PSK_WITH_AES_128_CCM_8\" and \"TLS_ECDHE_ECDSA_WITH_AES_128_CCM_8\" cipher suites are supported for CoAPs endpoints. When creating an endpoint that supports the PSK cipher suite, the pre-shared-key and an (optional) client identity hint have to be specified as parameters. For the elliptic curve DSA suite, the secp256r1 private key and signed certificate have to be provided as parameters. These are both encoded in base64 in the endpoint description. The following listing contains an example POST request that creates a CoAP endpoint for a virtual device hosted under 2001:6a8:1d80:23::1 on port 5684 with an ECC cipher suite.  
POST /virtualDevices Content-Format: application/json {  \"address\": \"2001:6a8:1d80:23::1\",  \"prefixLen\": 128,  \"port\": 5684,  \"dulia\": {  \"supportedCipherSuites\": [  {  \"cipherSuite\": \"TLS_ECDHE_ECDSA_WITH_AES_128_CCM_8\",  \"parameters\": {  \"b64 PrivateKey\": \"QVHO_...=\",  \"b64 Certificate\": \"LS0t...==\"  }  }  }  }  }  }  
2.01 Created /virtualDevices/2001:6a8:1d80:23::1\"128\"5684  
The response of the secure service proxy links to a newly-created resource that can be used to delete the endpoint at a later time. This resource is also used for managing the adapter chains that belong to an endpoint, as explained in Section 4.3.3.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 3 Energy Consumption Analysis
### Energy Dissipation by CH Nodes  
We now analyze the expected amount of energy expended by CH nodes in a round. First, consider sending ADV and TDMA messages with transmission radius of \(R\). Average amount of energy consumption by a CH node while transmitting ADV and TDMA messages are  
\[E_{T-ADV}=l_{A}(E_{e}+\epsilon_{m}R^{4}) \tag{17}\]  
and  
\[E_{T-TDMA}=l_{T}^{(k,R)}(E_{e}+\epsilon_{m}R^{4}), \tag{18}\]  
respectively.  
The average number of member nodes of a CH is \((N-k)(1-P_{k,R}^{(0)})/k\). The expected data-aggregation energy by a CH node is  
\[E_{Agg}=\left(1+\frac{(N-k)(1-P_{k,R}^{(0)})}{k}\right)l_{D}E_{DA}. \tag{19}\]  
A CH node sends aggregated data to the BS by expending \(E_{T-CHtoBS}\)  
\[E_{T-CHtoBS}=l_{D}\left(E_{e}+\epsilon_{m}\overline{d_{BS}^{4}}\right), \tag{20}\]  
where \(\overline{d_{BS}^{4}}\) is calculated as in Equation (13).  
In LEACH-based clustering protocol, a CH node receives other CHs' ADV messages, although they are just dumped; we ignore the energy related to this step. A CH node will hear Join-REQ messages from all non-CH nodes in its control-message range, to check if they are destined for itself or not. Therefore, while a CH node receives Join-REQ messages, it consumes \(E_{R-Join}\):  
\[E_{R-Join}=l_{J}E_{e}(N-k)\frac{\overline{A}(R)}{\pi L^{2}}. \tag{21}\]  
A CH requires energy \(E_{R-DATA}\) during receiving DATA from its member nodes as follows:  
\[E_{R-DATA}=\frac{(N-k)(1-P_{k,R}^{(0)})}{k}l_{D}E_{e}. \tag{22}\]  
Consequently, the expectation of the total energy expended among all CH nodes in a round is given by  
\[E_{CH}(k,R)=k\left(E_{T-ADV}+E_{T-TDMA}+E_{Agg}+E_{T-CHtoBS}+E_{R-Join}+E_{R-DATA }\right). \tag{23}\]  
Finally, the expected amount of energy consumption in a round is  
\[E(k,R)=E_{non-CH}(k,R)+E_{CH}(k,R). \tag{24}\]
## 2 Dechirp Influence on Squint Sliding-Spot Imaging Mode
### The Geometric Inverse Calculation Process  
As mentioned above, the doppler centers of all targets are pulled to zero frequency after dechirp. That is, for a certain target on the new slant range image (Figure 3), in the entire satellite flight trajectory, the moment when the satellite is closest to any target point is the azimuth moment of the target imaging. Based on the geographical coordinates of the target and the entire SAR trajectory, we can obtain a certain SAR position and corresponding imaging time when the slant range between SAR and the target is closest (removing the slant range offset) through iteration. Then the image pixel coordinates of the target can be calculated according to the imaging time and the closest slant range.  
Knowing the latitude and longitude coordinates \(\left(T_{lat},T_{lon}\right)\) and elevation \(H\) of the target \(T\), the process of obtaining its image coordinates \(\left(cr,ca\right)\) is as follows:  
1. Convert \(\left(T_{lat},T_{lon},H\right)\) to 3D coordinates \(\left(T_{x},T_{y},T_{z}\right)\) under WGS84.
2. Set the initial value \(\alpha_{begin}=0\), \(ca_{end}=m\_nHeight\). m_nHeight is the total number of lines in the azimuth direction of the slant range image.
3. Let \(ca=\left(ca_{begin}+ca_{end}\right)/2\). Suppose the satellite locates at point S\({}_{ca}\) and S\({}_{ca-1}\) at time \(ca\) and \(ca-1\), respectively. Calculate the distances between the two points and \(T\), respectively, and remove the near slant range to find the difference between the two \(N=\left\{|S_{ca}T|-r_{min}(ca)\right\}-\left\{|S_{ca-1}T|-r_{min}(ca-1)\right\}\).
4. If N \(>0\), it indicates that the target azimuth coordinate is between \(ca_{begin}\) to \(ca\), let \(ca_{end}=ca\). Otherwise, it indicates that the target azimuth coordinate is between \(ca\) to \(ca_{end}\), let \(ca_{begin}=ca\).  
Figure 9: Forward geometric positioning flow for squint sliding-spot imaging mode.  
* If \(ca_{end}-ca_{begin}>1\), go to (c) to execute a new loop, otherwise the loop ends, and \(ca\) is the azimuth image coordinate of the target \(T\), and its range image coordinate is calculated as \(cr=(|S_{ca}T|-r_{min}(ca))*2*f_{S}/c\).