esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Rice Phytoalexin Biosynthesis
### Biosynthesis of Rice Diterpenol Phytoaleoxins
#### 3.1.2 Biosynthesis of Phytocassanes  
As mentioned above, at least two biosynthetic gene clusters (BGCs) are involved in rice diterpenoid phytoplankton biosynthesis, c2 BGC and c4 BGC, which are nominally associated with phytocassane and momilactone production, respectively [119; 120] (Figure 7). It has also been reported that the production of momilactone A requires the intervention of enzymes encoded by genes not belonging to c4 BGC, namely CYP701A8, responsible for hydroxylation at C38 [135] and CYP76M8 for hydroxylation at C68 [124] (Figure 8). It should be noted that _CYP76M8_ belongs to c2 BGC, which also contains the gene _OsKLS7_ involved in the first step of phytocassane biosynthesis [136], as well as the upstream acting genes _OsCPS1_ and _OsCPS2_[131; 137] (Figures 6-8).  
Six cytochrome P450 enzymes (CYP71Z6 and Z7, CYP76M5 to M8) encoded by genes on rice chromosome 2 contribute to phytocassane biosynthesis [138]. In vitro enzyme assay systems have been extensively used to explore the biological role of these P450s in phytocassane biosynthesis. However, little information is available regarding the in planta contribution of relevant P450 genes. Recently, [PERSON] et al. [138] investigated their involvement in phytocassane production in planta by exploiting loss-of-function rice plants defective in each P450 gene. By characterizing CYP76M7/M8- and CYP71Z7-suppressed plants, they proposed a phytocassane biosynthetic pathway and identified novel candidate intermediates (Figure 9). They speculated that, in planta, CYP76M7/M8 are responsible for C11\(\alpha\)-hydroxylation of 3-hydroxy-_ent_-cassadiene and CYP71Z7 is involved in  
Figure 8: Proposed pathway for the biosynthesis of momilactones A and B. Chemical functions in blue indicate characterized reactions, while predicted functionalities are indicated in red. Dashed arrows indicate missing steps. Enzymes encoded by genes on chromosome 2, 4, and 6 (c2, c4, c6) are highlighted in green, yellow, and blue, respectively. GGPP: geranyl geranyl diphosphate; OsMAS: rice momilactone A synthase; OsKSL4: _syn_-pimara-7,15-diene synthase (EC 4.2.3.35); OsCPS4 (OsCycl): _syn_-copalyl diphosphate synthase (EC 5.5.1.14); CYP76M8: oryzaein D synthase (EC 1.14.14.112; 1.14.123); CYP99A3: 9-beta-pimara-7,15-diene oxidase (EC 1.14.14.111); CYP99A2: cytochrome P450 99A2 (EC 1.14.-.); CYP701A8: _ent_-sandaracopimaradiene 3-hydroxy (EC 1.14.14.70) (adapted from [128]).  
C2-hydroxylation of phytocassanes. Further studies are needed to confirm this hypothesis and fill gaps in the proposed pathway through the discovery of currently unknown enzymes that are likely encoded by genes outside of the cluster.  
Interestingly, phytocassane BGC has also been found in non-_Oryza_ species, such as Chinese rice (_Zizania latifolia_), with complementary subclusters separated on chromosomes 8 and 10 [139].
# FTIR Characterization of Asphalt SARA Fractions in Response to Rubber Modification
## 3 Results and Discussion
### Compositional Changes of Rubber  
Thermal analyses of original and extracted rubbers are depicted in Figure 10, illustrating the percentage of dissolved rubber and compositional components. The COV value for OL was 16.67%, for NR ranged from 9.52 to 10.33%, for SR ranged from 3.33 to 5.56%, and for FR ranged from 1.02 to 3.33%. Analyzing these data confirms the migration of rubber components into the asphalt fractions. The dissolved percentage of rubber increased from 34.3% in A-RB 190-600 to 82.0% in A-RB 190-3000, while increasing the temperature to 220 \({}^{\circ}\)C caused excessive dissolution that reached 92.0%. This increase in rubber dissolution corresponds to the decrease in the asphalt light fractions, saturates, and aromatics, as discussed in Figure 3, reaching the lowest values in A-RB 220-3000. Rubber particles absorbed low-molecular-weight components from asphalt, swelled, partially dissolved, devulcanized, and released their components back into asphalt. The rubber-released components were detected by FTIR and TGA. OL components in rubber decreased from 7.0% in the original rubber, 1.4% in A-RB 190-600, 0.5% in A-RB 190-3000, and 0.1% in A-RB 220-3000, supporting the C=C aromatic peaks that emerged in A-RB 190-3000 at 1571 cm\({}^{-1}\) and in A-RB 190-600 at 1560 cm\({}^{-1}\). NR and SR decomposed and reached the lowest values in A-RB 220-3000. Migration of these polymeric components was confirmed in A-RB 190-3000 at 971 cm\({}^{-1}\) for PB and at 735 cm\({}^{-1}\) for PS, forming network structures [32] that enhanced  
Figure 9: Temperature sweep results of neat binder and A-RBs: (**a**) \(G^{*}\) and (**b**) \(\delta\).  
the rheological performance. FR decomposed from 44.0% in the original rubber to values between 36.1 and 4.3% in the extracted rubbers, showing more disintegration of rubber components in asphalt binders.
# Automatically Extracted Antarctic Coastline Using Remotely-Sensed Data: An Update
## 5 Discussion
The results from a previous study show that the spatial scaling of remote sensing data reduces the average error from 69.4 m to 58.3 m on the coastline with the length of 3871.3 m [42], which is minor. In our study, the impact of the difference in resolution (i.e., 30 m and 75 m) is slight for the extraction of Antarctic coastlines longer than 35,000 km. Therefore, we did not resample the images to the same pixel size. In the future, studies about seeking the accurate length of coastlines should be conducted using remote sensing imagery at a common spatial resolution by spatial scaling-up or scaling-down.
## 2 Background
### Defenses against Adversarial Attacks
#### 2.2.1 Model Architecture Modifications  
These defenses alter the model architecture to introduce intrinsic robustness against adversarial perturbations. Methods such as defensive distillation [13] and feature squeezing [14] fall into this category. However, these approaches often do not generalize well against diverse attack types and can introduce additional computational overhead [10].
# Pavement Defect Segmentation in Orthoframes with a Pipeline of Three Convolutional Neural Networks
## 10 Experiments and Results
### Training  
#### 10.2.1 RSN Training  
The RSN was trained and tested in multiple iterations as more annotated orthoframes became available. Each training iteration was performed from scratch to avoid using images that were in the training dataset in the previous iteration in the validation dataset in the current iteration. During the training, additional data augmentation is applied to these images: (1) horizontal and (2) vertical flipping, (3) shifting and (4) rotation. The augmented images are processed in batches of eight and model parameters are updated after each batch in respect to the gradients backpropagated from IoU loss (18).  
\[L_{IoU}=1-\frac{\sum_{k\in K}y_{k}p_{k}}{\sum_{k\in K}\left(y_{k}+p_{k}-y_{k}p_ {k}\right)}. \tag{18}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset (Number of Orthoframes)** & **Total Samples** & **Defective Patches** & **Defective Pixels** \\ \hline (1) Full sampling (5842) & 814 426 (224 \(\times\) 224) & 14.7\% & 4.3\% \\ (1’) Full sampling (200) & 16 169 (336 \(\times\) 336) & 16.8\% & 5.24\% \\ (2) Dilated defect contour sampling (5842) & 140 187 (336 \(\times\) 336) & 65.1\% & 14.6\% \\ (2’) Dilated defect contour sampling (200) & 5675 (336 \(\times\) 336) & 67.0\% & 15.2\% \\ (3) Defect contour sampling with false positives (5842) & 105 293 (336 \(\times\) 336) & 85.2\% & 18.6\% \\ (3’) Defect contour sampling with false positives (200) & 5 880 (336 \(\times\) 336) & 76.3\% & 16.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The datasets used in this work, generated from the orthoframes reserved for training and validation.  
Figure 14: Three of the sampling strategies visualized on an orthoframe. Each green square is of size 336 \(\times\) 336 and added to the given dataset. For illustrative purposes, annotated defects are marked in blue and predicted false positives (only shown in c) are marked in red. For the sake of visual clarity, partial overlapping is not displayed and the focused part of the road in conjunction with the road area mask is highlighted. (**a**) Full sampling.; (**b**) Dilated defect contour sampling.; (**c**) Defect contour sampling with false positives.  
where \(L_{IoU}\) is the IoU loss, \(K\) is the set of all pixels in the images in the batch, \(y_{k}\in\{0,1\}\) is the label for the given pixel and \(p_{k}\in[0,1]\) is the predicted probability for the correct label of that pixel. Note that while cross entropy is the _de facto_ default loss function for classification, object detection and semantic segmentation tasks, it is mathematically much more closer to accuracy--that is biased toward reporting the accuracy of identifying negative cases--than IoU, even though the final performance of semantic segmentation models is measured using IoU [39; 40].  
The RSN model uses Adam optimizer with initial learning rate (LR) of \(1\times 10^{-4}\). The LR was halved if validation loss did not improve in 18 epochs. The final version of RSN was trained in 79 epochs.
## 3 Proposed EP-MAC Protocol Design
### Transmission Power Adjustment  
The power adjustment features of EP-MAC allow the sensor nodes to suitably vary the transmission power to reduce energy consumption. This concept is based on the power control protocol for wireless ad-hoc network proposed in [17]. During transmission of the RTS and CTS packets EP-MAC protocol use the maximum power \(P_{max}\). If receiver node receives an RTS packet, it sends a CTS packet to the sender node by using maximum power level \(P_{max}\). Now when the source node receives this CTS packet, the source node can calculate the actual required power \(P_{desired}\) by using the received power level \(P_{r}\) and transmitted power level \(P_{max}\) by using the formula  
\[P_{desired}\;=\;\frac{P_{max}}{P_{r}}\times Rx_{thres}\times c\]  
where \(Rx_{thres}\) is the minimum required signal strength and c is a constant. During the transmission of data packet the source node uses power level \(P_{desired}\). In the same way, receiver can determine the actual required power level \(P_{desired}\) by using the signal power of received RTS packet. And receiver node use power level \(P_{desired}\) for transmitting the ACK packet. This method assumes that the attenuation of the signal is the same in both directions _i.e._, the direction of both sender and receiver nodes. The noise level at nodes is also assumed to be less than a certain predefined threshold value.
## 4 Performance Evaluation
### Detailed Results
#### 4.3.2 Scenario #2
Inferring from Figure 7d, at 86 s, the client decided to switch from 1401 Kbps to 838 Kbps and skipped the value at 1118 Kbps. As a result, the \(\Delta t\) was reduced comparing to the Reactive method and the buffer was able to recover before fully depleted, thus avoiding rebuffering events. Additionally, Table 9 shows the number of HTTP responses and the number of PUSH_PROMISE frames client \(a_{1}\) received throughout the whole session, while Figure 8 illustrates the sum of those metrics for each video segment specifically. According to Table 9, the FAURAS and Reactive method show consistently identical statistics with the baseline as described in Section 4.2. The behavior of those methods is represented in Figure 8a. It is obvious that the client alternately received either a single HTTP response or a single PUSH_PROMISE frame for every segment throughout the streaming session. The Proactive method, however, failed to achieve such a performance, the _Res_ and _PP_ values exceeded the baseline in both cases. Referring to Figure 8b (subscenario 2-A) and Figure 8c (subscenario 2-B), after the 100 th segment, which was the moment that the new clients joined, the client \(a_{1}\) consecutively received both HTTP responses and a PUSH_PROMISE frame for 6 and 10 segments, respectively. This indicates that, although those segments had been pushed by the server, the client still requested and used those in the HTTP responses instead of fetching from its push cache. As result, those pushed segments were wasted and the client switched back to the pull-based mechanism of HTTP/1.1 during this period.
purpose or due to a malicious attack) and not conform to the rules of the protocol, e.g., either by sending malicious messages and/or not responding to other messages. These malicious attacks and software/hardware faults are common in distributed settings, especially in open participation systems that consist of independent actors in a highly scalable and dynamic environment. In the literature, such faults are referred to as _Byzantine_[3, 4] with a significant body of work on algorithms that enable Byzantine fault-tolerance on such systems (e.g., [5, 6, 7]), despite the failure of a limited number of their components [8].  
To enable collaboration between a set of federated nodes, a fundamental challenge is for the nodes to agree with _finality_ on some data upon which some other computational process will be executed. Similar challenge exists in unreliable distributed databases where the system needs to decide on whether to commit a data transaction to the state of the database or to abort it [2, 8]. This problem is realized as the _agreement problem_ where the challenge is not _what_ the processes will be agreeing on but how all processes reach the _same_ conclusion (i.e., _consensus_ (the state in which nodes in a distributed system reach final agreement)), in the presence of faults where distinct nodes are likely to receive conflicting conclusions [8].
# A Review of Deep Learning Applications for Railway Safety
## 5 Train Safety
### Wheel  
Wheel defects in trains are also one of the main causes of damage to railway systems and railway-related facilities. Neglecting train wheel defects will shorten the service life of a railway infrastructure, which may result in unnecessary maintenance costs. Furthermore, ground vibration and noise are generated when train wheel defects are present, causing significant damage to the surrounding environment. To determine train wheel faults, [PERSON] et al. [68] focused on the vertical force of the train. They continuously measured a load of trains running at top speed from wheel load checkpoints (WLCs) placed on rails at regular intervals and studied two methods to detect train defects. The first method determined train wheel defects using an SVM model based on the train load data processed by the discrete wavelet transform (DWT), a time series data processing method. Second, a CNN-based model was developed to detect train wheel defects. They found that these proposed methods show better performance than conventional defect detection methods. In particular, the CNN-based model had strengths identifying flat spots (wheel defects that stop wheel rotation and drag along the rails) and non-roundness (wheel defects that cause vibration and noise generation).  
In addition, acceleration sensors for inspecting the position of railway wheels have been widely studied to increase information utilization and efficiently perform maintenance decisions. However, the acceleration sensor has a limitation of relatively accurate detection of the longitudinal movement of train wheels but poor lateral movement accuracy. [PERSON] et al. [69] attempted to solve this problem by utilizing an image-based point tracking method with acceleration sensor data. Their proposed model was designed based on YOLO and generated a wheel reference point indicating a wheel position from the input image and comparing it with a normal position. Furthermore, they adopted various filters and data acquisition methods to improve performance, even in weather environments such as snow and fog.
## 2 Threat Model  
We consider the attacker as a passive eavesdropper between the user and the Tor network entry node, as shown in Figure 1. A potential attacker could be the user's internet service provider, routers, autonomous services, etc. The attacker collects traffic traces and does not delay, modify, or drop any packets. After that, the attacker trains machine learning classifiers to identify whether users are visiting a particular website, thereby destroying anonymity.  
**Evaluation Scenario** Two basic WF attack evaluation scenarios are used in this study: closed-world and open-world.
* Closed-world scenario. The closed-world scenario assumes that the user only visits a fixed collection of \(K\) websites, which is monitored by the attacker. We assume a weak attacker could only gather \(N\) instances for each monitored website. The attacker trains his classifier with the \(K\times N\) training instances and the test instances belong to one of the \(K\) monitored websites. Despite many criticisms for being unrealistic, this scenario is widely used to evaluate the basic classification performance of WF attacks in previous studies.
* Open-world scenario. The open-world scenario is a realistic but challenging setting. It considers that users may visit a large number of websites that the attacker may not be interested in, to be called unmonitored websites. The attack gathers instances for the unmonitored websites to join with the monitored instances as the training set. In reality, the number of unmonitored websites is over the capability of an attacker can monitor. Therefore, the unmonitored instances in the test set would be from other never-seen websites. The attacker must identify whether an instance belongs to the unmonitored or monitored set. If it belongs to the latter, the attacker should further figure out which is a monitored website.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
## 2 Gnlcsa
### Azimuth Filter and Coefficients
\Bigg{)}\) \\ \hline \(G(q_{2},q_{3},q_{4},q_{5},X_{3},X_{4},X_{5})\) & \(\frac{-\pi K_{LRW\mathrm{C}}}{2(K_{\mathrm{LRW\mathrm{C}}}+6)^{2}}\Bigg{(} \begin{array}{c}-6K_{\mathrm{LRW\mathrm{C}}}\big{(}3K_{\mathrm{d}}q_{2}q_{3}+ 8q_{3}^{2}\big{(}3K_{\mathrm{d}}q_{2}+8q_{3}^{2}X_{3}\big{)}\\ -K_{LRW\mathrm{C}}^{2}\big{(}23K_{\mathrm{d}}q_{2}X_{3}+9q_{3}X_{3}+q_{2}q_{3}^{ 2}\big{(}3K_{\mathrm{d}}q_{3}+8q_{3}^{2}X_{3}\big{)}\\ -3K_{LRW\mathrm{C}}^{2}\big{(}3K_{\mathrm{d}}q_{2}+8q_{3}^{2}\big{(}3K_{ \mathrm{d}}q_{3}+8q_{3}^{2}X_{3}+q_{2}^{2}\big{(}3K_{\mathrm{d}}q_{3}+8q_{3}^{2 }X_{3})\big{)}\end{array} \Bigg{)}\) \\ \hline \(G(q_{2},q_{3},q_{4},q_{5},X_{3},X_{4},X_{5})\) & \(\frac{-\pi K_{LRW\mathrm{C}}}{4(K_{\mathrm{LRW\mathrm{C}}}+6)^{2}}\Bigg{(} \begin{array}{c}-6K_{\mathrm{LRW\mathrm{C}}}\big{(}3K_{\mathrm{d}}q_{2}^{2}+8q _{3}^{2}\big{(}3K_{\mathrm{d}}q_{3}+8q_{3}^{2}\big{(}3K_{\mathrm{d}}q_{3}+8q_{3}^{ 2}\big{)}\\ -K_{LRW\mathrm{C}}^{2}\big{(}3K_{\mathrm{d}}q_{2}+8q_{3}^{2}\big{(}3K_{ \mathrm{d}}q_{3}+8q_{3}^{2}X_{3}+6q_{3}^{2}\big{(}3K_{\mathrm{d}}q_{3}+8q_{3}^{ 2}X_{3})\big{)}\end{array} \Bigg{)}\) \\ \hline \(H(q_{2},q_{3},q_{4},q_{5},X_{3},X_{4},X_{5})\) & \(\frac{-\pi K_{LRW\mathrm{C}}}{4(K_{\mathrm{LRW\mathrm{C}}}+6)^{2}}\Bigg{(} \begin{array}{c}K_{\mathrm{d}}q_{2}^{2}(45q_{2}^{2}-16q_{2}q_{4})+K_{LRW \mathrm{C}}\big{(}54q_{2}^{2}-8q_{2}^{2}q_{4})+(52q_{3}^{2}-24q_{2}q_{3}q_{4 }+4q_{2}^{2}q_{5})\\ +(52(7q_{3}^{2}-24q_{2}q_{3}q_{4}+4q_{2}^{2}q_{5})+K_{LRW\mathrm{C}}^{2}(6q_{ 2}(4 The azimuth compression filter is given as follow:  
\[H_{5}\big{(}f_{\eta}\big{)}=\exp\left(-\sum_{i=2}^{5}A_{i}(q_{2},q_{3},q_{4},q_{5 },X_{3},X_{4},X_{5})f_{\eta}^{i}\right) \tag{34}\]  
The principle of stationary phase is applied to derive the signal after azimuth compression in the RD domain. The signal in the azimuth time domain is expressed as:  
\[s(\tau,\eta)=\text{sinc}\bigg{(}\tau-\frac{2R_{LRWC}}{c}\bigg{)}\text{sinc} \Big{(}\eta-\frac{\eta_{p}}{2\alpha}\Big{)}\exp\big{(}-j2\pi\eta_{p}\eta\big{)} \tag{35}\]  
From the Equation (35), it obtains that the target position after focusing is shifted to \(\big{(}R_{0}+x_{p}\sin\theta_{s},x_{p}/(2\alpha)\big{)}\). Readers can adopt the method of geometric correction to correct the geometric shift, which has been illustrated in [1]. The generalized chirp scaling algorithm in [4] is the special form of GNLCSA in the case of \(\theta_{s}=0\). The difference between ENLCSA and GNLCSA is that the coefficients and the phase of azimuth compression in GNLCSA framework are more precise than ones of ENLCSA.
## 2 Materials and Methods  
### Principle of Atomic Force Microscopy (AFM) Testing  
Atomic Force Microscopy (AFM) is a high-resolution surface characterization technique based on the interaction forces between a probe and a sample. Its core principle involves detecting the intermolecular forces (such as van der Waals forces, electrostatic forces, or chemical bond forces) between a nanoscale probe at the end of a cantilever and the surface of the sample to achieve three-dimensional imaging of the surface morphology [21]. The probe system of AFM is controlled by a piezoelectric ceramic scanner, which can precisely adjust the lateral (\(x\)-\(y\) axes) and vertical (\(z\)-axis) displacements of the probe on the sample surface at the nanoscale [22]. When the probe scans across the sample surface, the interaction force between the probe and the sample causes bending deformation of the cantilever. This deformation is reflected by a laser onto a photodiode detector and then converted into an electrical signal to reconstruct the surface topology [23].
## Abbreviations  
**AACN**: advanced automatic collision notification
**ACL**: access control list
**ACN**: automatic collision notification
**ADAS**: advanced driver assistance system
**AES**: Advanced Encryption Standard
**AI**: artificial intelligence
**BLE**: Bluetooth low energy
**BT**: Bluetooth  
**CAN** controller area network  
**CDN** content delivery network  
**CMI** crash momentum index  
**CSI** crash severity index  
**CVSS** Common Vulnerability Scoring System  
**DDoS** distributed denial of service  
**DoS** denial of service  
**DSRC** dedicated short-range communication  
**DTC** diagnostic trouble code  
**GDPR** General Data Protection Regulation  
**eCall** emergency call  
**ECU** electronic control unit  
**EDR** event data recorder  
**EES** energy equivalent speed  
**EoP** elevation of privilege  
**ERS** emergency response service  
**FHSS** frequency-hopping spread spectrum  
**GNSS** Global Navigation Satellite System  
**GPS** Global Positioning System  
**GSM** Global System for Mobile Communications  
**HSM** hardware security module  
**HTIPS** hypertext transfer protocol secure  
**I-V** in-vehicle  
**IDS** intrusion detection system  
**IEEE** Institute of Electrical and Electronics Engineers  
**IIHS** Insurance Institute for Highway Safety  
**IoT** internet of things  
**IoV** internet of vehicles  
**IPS** intrusion prevention systems  
**IR** impact response  
**ISO** International Organization for Standardization  
**IVI** in-vehicle infotainment  
**LoRa** long range  
**MAC** media access control  
**MEMS** micro-electro-mechanical system  
**MEA** multi-factor authentication  
**MITM** man in the middle  
**ML** machine learning  
**OBD** on-board diagnostics  
**OBU** on-board unit  
**OEM** original equipment manufacturer  
**OpenCV** Open Source Computer Vision Library  
**PCIs** pre-crash indicators  
**PHP** hypertext preprocessor  
**PKI** public key infrastructure  
**pre** previous  
**SAE** Society of Automotive Engineers  
**SecOC** secure on-board communication protocol  
**SMS** short message service  
**SQL** Structured Query Language  
**STRIDE** spoofing; tampering; repudiation; information disclosure; denial of service; elevation of privilege  
**TARA** threat analysis and risk assessment  
**TPM** trusted platform module  
**TCP** transmission control protocol  
**TCU** telematics control unit  
**UART** universal asynchronous receiver/transmitter  
**USB** universal serial bus  
**V2C** vehicle-to-cloud  
**V2I** vehicle-to-infrastructure  
**V2P** vehicle-to-pedestrian  
**V2V** vehicle-to-vehicle  
**V2X** vehicle-to-everything  
**WAF** web application firewall