esa-sceva/satcom-chunk-collection huggingface dataset:
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 3 PQC-Based PKE Protocols for Using the PQC Algorithm
### PQC Standalone PKE Protocol
If valid, it processes the data; otherwise, it rejects the data to prevent replay attacks (Algorithm 1, lines 49-55). A similar process is followed for data transfer from the server back to the client. The server encrypts its data (\(data_{s}\)) along with its sequence number (\(seq_{s}\)) using the client's public KEM key (\(pkk_{c}\)) to produce a ciphertext \(ct_{data_{c}}\), which is sent to the client. Upon receiving this ciphertext, the client decrypts it using its private KEM key (\(skk_{c}\)), retrieves both the data and the sequence number (\(seq_{s}^{\prime}\)), and checks whether the sequence number is valid. If the sequence number is valid, the client processes the data; otherwise, it rejects the data to prevent replay attacks (Algorithm 1, lines 56-65). The proposed protocol introduces various enhancements compared to traditional PQC-based PKE protocols. First, we incorporate explicit certificate validation during the initial handshake steps to ensure that the exchanged KEM public keys are authentic and untampered. Second, we strengthen the replay protection by including unique sequence numbers in data encryption. Finally, by integrating robust HMAC-based key confirmation mechanisms, our proposed protocol significantly reduces vulnerabilities to MITM attacks while ensuring that shared secrets are securely established between legitimate parties.
## 2 Materials and Methods  
In this study, using observations from global EC flux towers, we appraised 8 common satellite-data-based GPP models and the quality of major spatial data layers that drive the models. In order to utilize remote sensing data as directly as possible and minimize error propagation, 2 new GPP models were developed and compared with the 8 common satellite-data-based GPP models. Finally, 24 existing global GPP products, including the global GPP product generated from the new model that showed better performance, were compared and analyzed for similarity and differences.
# Hardware Reliability Analysis of a Coal Mine Gas Monitoring System Based on Fuzzy-FTA
## 3 Reliability Analysis Process of Gas Monitoring System Based on Fuzzy-FTA
### Establishing the Fault Tree Model  
The fault tree can be established by analyzing the aforementioned fault factors, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Basic Event** & **Event Name** & **Basic Event** & **Event Name** \\ \hline B1 & Industrial computer failure & X7 & Power sensor failure \\ B2 & Monitoring system sensor failure & X8 & Switch sensor failure \\ B3 & Monitoring system line failure & X9 & Breaker failure \\ B4 & Monitoring system power supply & X10 & PLC controller failure \\ B5 & Monitoring staff mistakes & X11 & Mechanically break the line \\ X1 & Industrial computer host failure & X12 & Self-aging short circuit \\ X2 & Display failure & X13 & Poor line contact \\ X3 & Monitor substation failure & X14 & Abnormal power failure \\ X4 & Gas sensor failure & X15 & High communication error rate \\ X5 & Temperature sensor fault & X16 & Short circuit to ground \\ X6 & Wind speed sensor fault & X17 & Lack of responsibility of the staff \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fault tree event table of a gas monitoring system.  
Figure 3: Fault tree of a gas monitoring system.  
When the gas monitoring system has a hardware failure and the staff fails to identify and deal with it in time, the gas monitoring system will malfunction [25].  
Figure 3 shows that the number of OR gates is large, while that of AND gates is small, suggesting a potentially large failure rate of the system [22]. In fact, the OR gates produce an output for any basic event, whereas the AND gates produce an output only when all of the basic events occur simultaneously.
## 2 Methodology
### Training Module
#### 2.2.2 LSTM Training Process  
During training of the LSTM model, the train set and test set are normalized to rescale the original value of the feature between 0 and 1, using the MinMax Scaler function and then fed into the model. The method for normalizing the data is indicated in Equation (7), where \(x_{i}\), \(min(x)\), \(max(x)\), and new \(x_{i}\) represent the original value of the input feature, the minimum value in input feature, the maximum value in input feature, and the new rescaled value of \(x_{i}\).  
\[new\ x_{i}=\frac{x_{i}-min(x)}{max(x)-min(x)} \tag{7}\]  
The following parameters are selected for fitting the LSTM training process: 100 epochs used for the number of iterations, and mean squared error used as a loss function during the training process. The Adam optimizer is employed over the classical stochastic gradient descent procedure to update network weights iteratively based on training data. In addition, 256 batch sizes are used to work through before updating the internal model parameters. The rectified linear unit (ReLU) is also chosen for the activation function to solve vanishing problems. This activation function can learn much faster than the sigmoid function in networks with many layers, by allowing the training of deeply supervised networks without unsupervised pretraining.  
The sequence length of the LSTM network is one of the parameters which have to be considered to train sequential data. EGAT data are recorded by time series every 30 min in one day, so that the data have 48 periods in total. As a consequence, 48 lag features of the load data are used to forecast time steps for the next day. Figure 7 portrays the input feature vectors \(x\), the number of hidden units \(h\), and the output of the final time step \(o\).
# A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
## 1 Introduction
Results are presented by coherence and interferogram images and displacement map revealing a 40 cm displacement in the line of sight (LOS) direction. The study's conclusion, the potential for future research and application of the demonstrated technique are outlined in Section 5.
## 3 Heterogeneous Parallel Implementation
### Numerical Verification  
The validation of the effectiveness of the fluid simulation algorithm is performed using lid-driven cavity flow, which serves as a benchmark case. In this study, the lid-driven cavity flow is simulated for Reynolds numbers Re of 1000, 2000, 5000, and 7500. The grid size used is 512 \(\times\) 512, with a density \(\rho\) of 2.7 and a lid velocity U of 0.1. The standard bounce-back boundary condition is applied to the walls to achieve the no-slip condition. The simulations are run until the error converges to 1 \(\times\) 10\({}^{-10}\) at time steps 1,370,000, 2,900,000, 7,540,000, and 11,540,000, respectively. The flow field data are outputted and visualized. The stream function for different Reynolds numbers is shown in Figure 7.  
Figure 7 shows the changes in square cavity flow under different Reynolds numbers (Re). When the Reynolds number is low (Re \(\leq\) 1000), three vortices form within the cavity: a primary vortex at the center and two secondary vortices near the lower left and lower right corners. As the Reynolds number increases to 2000, an additional secondary vortex appears in the upper left corner. Further increasing the Reynolds number to 5000 results in the emergence of a tertiary vortex in the lower right corner. When the Reynolds number reaches 7500, the tertiary vortex in the lower right corner becomes more pronounced. Moreover, as the Reynolds number increases, the center of the primary vortex gradually shifts towards the center of the cavity.  
To further validate the accuracy of the simulations, vertical cross-sections of the X-direction velocity profiles are extracted at 1000 points along the Y-axis, with X fixed at 0.5. These profiles are compared with the results from the literature [34], as shown in Figure 8. Similarly, the horizontal cross-sections of the Y-direction velocity profiles at the center are compared, as shown in Figure 9. Here, U and V represent the velocity components in the X and Y directions, respectively. It should be noted that the flow field data outputted in this  
Figure 5: Heterogeneous parallel framework diagram.  
Figure 6: Grid partitioning method. (**a**) CPU + DCU partitioning: (**b**) 28-threaded partitioning.  
From Figures 7-9 it can be seen that as the grid scale expands, the simulation results obtained by this algorithm under different Reynolds numbers are in good agreement with the literature results [34]. This indicates that the current LBM heterogeneous parallel algorithm can effectively simulate the flow field, thereby verifying the effectiveness and correctness of the algorithm.
## 4 Protocol Design  
Specifically, in the RECN scheme, when the value of \(R_{FTE}\) is smaller than a given threshold, wireless routers will explicitly mark packets with a CE (Congestion Explicitness) bit in the IP (Internet Protocol) header. By this cross-layer ECN mechanism, the sender will adjust its sending rate on receiving congestion notification. RECN works at the MAC layer and uses the standard ECN mechanism. Thus, in the RECN scheme, there is only very small modification at the MAC layer. We note that RECN is a supporting design that is compatible with a wide range of TCP protocols.  
The pseudo-code of RECN implementation is described as:  
\(Mac802\_11::check\_pktTx()\)  
\(\{\)  
// calculating total transmission time (including data packet, ACK delay and propagation delay)  
\(T_{Trans}=txtime(pktTx)+txtime(ACK)+T_{prop}\);  
// recording the current time  
\(bt\) = now;  
\(T_{SL}=T_{Trans}+T_{RTS}+T_{CTS}+3T_{SIFS}\);  
\(T_{FSD}=bt-st+T_{Trans}\); //\(st\) is the beginning time for transmitting.  
\(R_{FTE}=\dfrac{T_{SL}}{T_{FSD}}\)  
if \(R_{FTE}<threshold\) // threshold is a given value.  
set CE bit to 1; // mark ECN bit  
\(\}\)  
Since the RTS/CTS mechanism is widely used in multi-hop wireless networks, we only take the RTS/CTS mechanism into consideration. However, our method is also suitable for the basic access mechanism.  
Figure 3: The relation of \(R_{FTE}\), \(\tau\) and \(n\).
# Maintenance Management of a Transmission Substation with Optimization
## 5 Results of Optimal RCM Maintenance
### Optimal Maintenance for the Observed TS
In the optimization procedure, we also took into consideration the fact that the boundaries between maintenance areas of the bays on the \(c-i_{\text{avg}}\) diagram could also be changed by varying the distances \(d_{1}\) and \(d_{2}\) or the angle \(\beta\) (Figure 8b). The determination of boundaries of individual areas in the \(c-i_{\text{avg}}\) diagram depends on risks (outages of TS elements, financial risks) that the transmission system operator is willing to accept. Changing the boundaries and angle \(\beta\) in Figure 8b influences the maintenance concept. Any change of boundaries in the \(c-i_{\text{avg}}\) diagram also influences boundary conditions of the optimization and requires a new run of the optimization tool. The optimization process is performed with a feedback loop, which enables correction of new parameters of technical condition (index \(c\)) with regard to the period of the maintenance works, characteristic maintenance states, and performance of tasks. This ensures feedback to the input parameters through the index of technical condition \(c\) and a feedback loop to the optimization model. The time window in the optimization model is three years, although the optimization is performed every year. This means that the time window is, every year, shifted by one year to the future. In the case of major investments to the TS the model is updated, and a new optimization run is performed. If there are no extraordinary events, the state of elements in principle does not change; otherwise, a correction is made that is considered in the optimization model for the time window of the next three years. The purpose of introducing the optimization procedure in maintenance is not only to optimize costs by providing the highest reliability possible, but also in planning of maintenance tasks for the whole year.
## 5 Conclusions  
We proposed an approach to automatically predict landmark location and used a deep learning method with very small training data, with only 150 X-ray training images. The results outperformed other benchmarks' results in all SDR and nearly all SCR, which proves that the proposed method are effective for cephalometric landmark detection. The proposed method could be used for landmark detection in clinical practice under the supervision of doctors.  
For future work, we are developing a GUI for doctors to use. The system offers either automatic or semi-automatic landmark detection. In the automatic mode, the system will automatically extract an ROI (based on registration) and select a proper model for each landmark. In the semi-automatic mode, the doctor needs to give a bounding box extract ROI manually and select a corresponding model for each landmark detection, which can reduce computational time. Since we used simple registration in this study and it took a long time to register one test image with all the training images, around twenty minutes as we mentioned before. Under such case, we aim to design a better method that reduces the registration time in testing phase, to make the automatic detection more efficient. For example, maybe we will also utilize deep learning method, either to do registration or just simply to regress a coarse landmark region, to replace the rigid registration we used in this paper. Moreover, because we detected all the landmarks in patch images, we did not take global-context information (i.e., the relationship among all the landmarks) into consideration. In future work, we want to utilize this global-context information to check whether we can achieve better performance. For example, maybe we will try to change the network architecture to allow the network to utilize whole image's features to better and faster locate the coarse locations of landmarks.  
Conceptualization, Y.S.; Data curation, Y.S. and X.Q.; Formal analysis, Y.S.; Funding acquisition, X.Q. and Y.-w.C.; Investigation, Y.S.; Methodology, Y.S., X.Q., Y.I. and Y.-w.C.; Project administration, X.Q.; Supervision, Y.I. and Y.-w.C.; Validation, Y.S.; Writing--original draft, Y.S.; Writing--review and editing, Y.S., X.Q., Y.I. and Y.-w.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under the Grant No. 61603218, and in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 18H03267 and No. 17K00420.  
The authors declare no conflict of interest.  
Figure 11: Green points: predicted landmarks’ locations. Red points: ground-truth locations of landmarks. We can see the result is not satisfying at all.
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 1 Introduction
First, it requires a large amount of labeled data, while the number of training data is usually not enough to train the CNN model in the real world. Second, the training process is often time consuming if there are not extensive computational and memory resources. Pretrained CNNs have been proposed to function as generic feature extractors. In the training phase, there is a process, named as fine-tuning to take a network model that has already been trained for a given task, and make it perform on a new task. Fine-tuning a pretrained CNN is a promising alternative to training a CNN from scratch, especially when there is a limited dataset, and fine-tuned pretrained CNNs has an advantage of the speed of convergence. Pretrained CNNs have been successfully applied in imagery classification as a feature extractor or as a baseline for transfer learning. The pretrained CaffeNet [15] was fine-tuned to classify the remote sensing scenes solving the overfitting problem due to the lack of labeled images [16]. A pretrained model was applied to classify SAR targets with limited labeled data and achieved a superior performance than the methods based on CNNs [17]. Moreover, pretrained CNNs have been applied in disaster identification and achieved satisfactory performance. In order to achieve a good recognition performance toward small dataset tasks, instead of training deep CNN from scratch, deep transfer learning (TL) with a Visual Geometry Group (VGG) [18] pretrained model is implemented, and feature extractor and fine-tuning as two TL strategies. The results showed the potential use of deep TL in image-based structural damage recognition [13]. CNNs have been widely adopted in image classification, while there are still limited studies related to the detection of damaged buildings after an earthquake. VGGNet is a popular CNN structure. It achieved excellent performance on ImageNet Large Scale Visual Recognition Challenge (ILSVRC) classification and localization tasks, and it can generalize well to a wide range of tasks and datasets [18]. In remote sensing, it is difficult to obtain a large number of labeled samples. VGGNet could be adopted as the base model which was pretrained using a large amount of labeled data, and then a small amount of target data was applied to fine-tune the pretrained network for tasks including remotely sensed imagery classification and target detection [19; 20]. In this study, a pretrained VGGNet model was proposed to recognize collapsed buildings caused by the 2010 Haiti earthquake using pre- and post-event remotely sensed overhead imagery, and the performance of fine-tuned pretrained VGGNet model and VGGNet model trained from scratch were compared. Dataset augmentation was also considered to enlarge the training dataset to improve identification accuracy. The study area and data sources were described in Section 2. The detailed methodology is illustrated in Section 3. The results achieved by the applied methods and discussion were shown in Section 4. Section 5 demonstrated the conclusion.
## 3 Principals of Routing Protocols and Their Breakdowns
### Acar [57]
#### 3.3.1 Ma-rfr Arp [58]  
Propagation delay and limited bandwidth are a few significant challenges for multi-functional UWSN sensors. To overcome this problem, authors in [58] proposed multi-modal acoustic-RF adaptive routing protocols (MA-RF-ARP). The authors proposed using two multi-nodal acoustic-RF adaptive routing and simulated it on grid-based and randomly deployed UWSNs. Then, they tested the strength of RF and underwater acoustic signals. They also categorized the packets in terms of latency and reliability. Their simulation shows that the protocols achieve satisfactory results.  
* Comparison  
Here, most of the protocols use anycast routing except ACRA, which uses both broadcast and unicast routing. All of them are capable of avoiding void zones. All of these protocols use a pressure gauge to determine pressure except ACRA. The differences between the protocols are illustrated in Table 13.  
* Sender-based Routing Protocol:  
In the sender-based routing protocols, the sender node chooses its next hop node by itself.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Protocol** & **Routing** & **Void Avoidance** & **Pressure Detection** & **Simulation Tool** & **No. of Sink** & **Overhead** & **Throughput** & **Delay** \\ \hline HYDRO- CAST & Anycast & Yes & Onboard pressure gauge & QualNet & Multiple & High & Medium & Medium \\ VAPR & Anycast & Yes & Onboard pressure gauge & Not given & Single & Low & Medium & Medium \\ ACAR & Broadcast and unicast & Yes & Depth & NS2 & Single & Medium & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of Pressure-Based Routing Protocol.
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 2 Relate Work
### Road Extraction Based on CNNs  
Super performance [22; 23; 24] about CNN has been shown in remote sensing image segmentation, especially U-Net with an encoder-decoder structure, e.g., in order to enhance the effectiveness of road feature representation, a variant of U-Net with residual was presented by [PERSON] et al. [25]. The model absorbed the strong points of U-Net and residual networks so that the convergence property and information propagation were better. Likewise, [PERSON] et al. [26] designed a new model that mainly consists of skip contacts and densely connected blocks. The model was able to utilize road features representation from different layers fully. Moreover, a model based on LinkNet was introduced by [PERSON] et al. [27] in which an elaborate dilation convolution structure was applied to capture multi-scale context information. The methods mentioned above perform well, but they are still CNN based.  
In addition, many researchers also have obtained accurate segmentation performance by designing an effective loss function besides modifying the structure of the model. [PERSON] et al. [28] introduced a novel loss based on the cross-entropy loss function. The loss skillfully combined the geometrical features of road characteristics. In order to avoid blurriness segmentation results, [PERSON] et al. [29] defined a loss function based on structural similarity. [PERSON] et al. [30] believe that the topological structure also affects the final result, so they proposed a loss function, synthesizing the topological awareness. These loss functions fully consider the structural characteristics of roads. However, they neglect the influence of the imbalance between background information and road samples.
# PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm
## 4 Functional Requirements for PDCA 4.0
### Organizationally Transversal System for Consulting Best Practices (R9)  
A transversal system for consulting best practices is defined as a requirement for PDCA 4.0, as it will enable knowledge sharing across the company regardless of the user's location, avoiding information retention in organizational silos, and facilitating the standardization of new countermeasures. This requirement is present in conceptual approaches that mention that although AR/AsR (Sim&Virt) enables users to access real-time information about process data [11], it can also be used to remotely support employees in performing manual operations, such as maintenance or production tasks [8; 12]. This way, work instructions can be shared with the operator [8], guaranteeing that best practices are shared across the organization, facilitating their standardization. In a similar context, VR can also facilitate the employees' training [8]. According to the literature, the use of these technologies is supported by Cloud and IoT [90; 91]. Empirically, AR and VR are considered as interdependent with CI, although to a lesser extent when compared with visual management [59]. This requirement is further demonstrated in practical use cases. Several publications cover the application of these technologies through use cases in the context of production, maintenance, and quality control activities [1; 75; 76; 77]. As an example, [PERSON] et al. [1] mention the use of AR on manual workstations for the identification of both tasks and relevant information.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments  
Accurate relay measurement is key factor in the fault location estimation. Normally, inverters are not installed close to the shore; therefore, it is always required to employ a combination of overhead transmission lines and submarine cables for offshore wind farms integration with onshore grids. Kii Channel HVdc system [71], Anan Kihoku HVdc system [72], Hokkaido Hanshu HVdc link, Japan [73] and Basslink HVdc inter-conneter system, Australia [74] are possible examples of power transmission over hybrid medium. Three-line segment model of dc-link is considered to elaborate the behavior of cables and OH line for dc fault location estimation with respect to TWs in the following section. Here, faults are classified as dc cable and dc overhead line faults.