esa-sceva/satcom-chunk-collection huggingface dataset:
# Preliminary Study on the Damping Effect of a Lateral Damping Buffer under a Debris Flow Load
## 6 Conclusions  
In this paper, we propose and study a new type of device, named a lateral damping buffer, to reduce the vulnerability of building structures to debris flows. The lateral damping buffer can be regarded as a combination of a buffer and a shock absorber, which can reduce the maximum acceleration response and the subsequent vibration respectively. To examine the effectiveness of the lateral damping buffer, an impact test of the lateral damping buffer attached to a two-degree-of-freedom structure under the simulated debris flows load is completed. After the test, a corresponding numerical simulation is performed to validate its rationality. Then, a following parametric study is performed to optimize the lateral damping buffer. Finally, an engineering design procedure is put forward.  
The impact test shows that the lateral damping buffer has good effectiveness in reducing both the maximum acceleration response and the subsequent vibration. Particularly for the tenth loading grade, the maximum acceleration response can be reduced by nearly half and the root mean square acceleration can be reduced by over 30%. In addition, the simulated values fit well with the experimental results. Moreover, the effectiveness of the lateral damping buffer increases with the increase in the loading grade. This characteristic is beneficial to the application in practical engineering.  
The lateral damping buffer can also reduce the subsequent vibration of the primary structure by energy dissipation. However, considering the characteristics of the debris flows, the attachment of the cushion devices at the impact point is more effective.
## 3 System Model
### Energy Model
Thus, \(E_{active\_N_{i}}\) can be calculated as follows:  
\[E_{active\_N_{i}}(t)=E_{Wu}+E_{proc}+E_{Wut}+\sum_{j=1}^{n}E_{s}(i) \tag{8}\]  
where \(E_{Wu}\) is the energy consumption when a device wakes up, \(E_{proc}\) is the energy consumption of data processing, and \(E_{Wut}\) is the amount of energy to move the transceiver from sleep mode to active mode, respectively. In particular, \(E_{s}(i)\) is the energy consumption of a LoRa device in terms of data rate to transmit \(L_{packet}\) with \(n\)-hop communication evaluated as (9) [43] and is calculated as follows:  
\[E_{s}(i)=\frac{L_{packet}}{R_{b}}\times(P_{tx}+P_{rx}) \tag{9}\]  
where \(L_{packet}\) is the size of the transmitted packet in bits, \(P_{tx}\) and \(P_{rx}\) are the consumed power according to the supply current and supply voltage for transmitter and receiver, respectively, and \(R_{b}\) is the bit rate defined in Equation (10). \[R_{b}=\frac{CR\times SF}{\frac{2 SF}{BW}} \tag{10}\]  
Here, \(SF\) is the spreading factor, \(BW\) is the bandwidth, and \(CR\) is the code rate defined as \(\frac{4}{(i+4)}\) with \(i\in\{1,2,3,4\}\), respectively. Ultimately, to understand the LoRa device's energy consumption, it is essential to efficiently utilize the LoRa devices' residual energy \(E_{re\_N_{i}}(t)\) at a given time (t) for the effective selection of CHs and also prevent the untimely demise of devices to ensure that the energy of each LoRa device in the network is consumed in a relatively balanced manner. Thus, \(E_{re\_N_{i}}(t)\) can be computed as (11),  
\[E_{re\_N_{i}}(t)=E_{0}-E_{total\_N_{i}}(t) \tag{11}\]  
where \(E_{0}\) is the initial energy of LoRa devices.
## 4 Experiments and Results  
This section presents experiments on the data described in Section 3.6. Since we are interested in early student performance prediction, we use four distinct Cumulative Weight Assessment upper thresholds (\(\text{CWA}_{U}\)): 20%, 40%, 60%, and 80% in all of our experiments, for both classification and clustering. These thresholds divide the course into progressive stages, helping us better interpret how student performance and learning patterns evolve. The 20% and 40% thresholds represent early engagement, critical for identifying students needing timely support. The 60% and 80% thresholds reflect later stages of the course, allowing us to evaluate how predictive performance improves as more data becomes available. All code was implemented in Python 3.10.9 using the scikit-learn library [35].
# Vulnerability of Buildings to Meteorological Hazards:
## 4 Limitations and Further Developments of the Study  
The study presented herein describes a well-established vulnerability assessment method modified for three meteorological hazards. The approach uses a new set of indicators for each hazard based on available literature review. These indicators are weighted by using expert judgment and the AHP method and then are aggregated in an index. It is important to note that there were some limitations in the study. The most important limitation is the lack of indicators related to the phenomenon itself and its intensity. We assume that the building will be damaged by a potential hazard, e.g., a hail storm, but we do not define the intensity of this storm or its characteristics (e.g., hail grain size). This limitation should inspire a future development of the study where the intensity can also be considered in the index. This has been achieved in the case of tsunamis [10] and dynamic flooding [14] by including the intensity of the process, e.g., the water vulnerability (part of the building submerged in the water). Moreover, the spatial pattern of the phenomenon under study could also be included and overlayed with the vulnerability of buildings belonging to a settlement. This could guide emergency management decisions and local adaptation measures. Another limitation is the scoring of the indicators. The scoring as presented herein is based on a review of the existing scientific literature, which is limited. The scoring should be revised in the future and should be seen in combination with the characteristics of the potential meteorological hazard. Finally, future projections of the meteorological hazards discussed herein could also be included and combined with the vulnerability index to identify future catastrophic scenarios and potential negative consequences. Another limitation which is common in studies including expert judgment is the choice, background, number, response rate, and subjectivity of the experts involved. A higher number of experts, the involvement of international experts, or the involvement of experts belonging to the same group (e.g., engineers) could also be a future development of the study.
## 4 Overview of the Proactive Transmission of Replicated Frames (PTRF) Mechanism
### PTRF Operation
#### 4.2.1 Approach A: End-to-End Estimation and Replication  
The main characteristic of this approach is that only end-systems replicate frames and eliminate surplus replicas upon reception. Bridges are Commercial Off-The-Shelf (COTS) standard TSN bridges which forward all the frames that they receive, whether they are replicated or not. Furthermore, the number of replicas that talkers must create is calculated to achieve a specific end-to-end reliability, i.e., a given reliability for the largest path from the talker to the listeners considering the unreliability of each one of the devices and links of that path.  
As we can see in Figure 3a, the talker \(T_{1}\) transmits three replicas of frame \(f_{1}\), labelled as \(r_{1,1}\), \(r_{1,2}\) and \(r_{1,3}\). \(B_{1}\) correctly receives all replicas and forwards them, but replica \(r_{1,1}\) is affected by a temporary fault in link \(l_{2}\) and dropped upon reception by \(B_{2}\). \(B_{2}\) forwards replicas \(r_{1,2}\) and \(r_{1,3}\), but \(r_{1,3}\) is affected by a fault in link \(l_{3}\). Thus, listener \(L_{1}\) delivers \(r_{1,2}\) to the application and drops \(r_{1,3}\).
# Study on the Wellbore Flow for Carbon Dioxide Fracturing
## 3 Solution Procedure
### Coupling Computation Methodology  
During the injection process of CO\({}_{2}\) fracturing, the temperature and pressure in the wellbore and CO\({}_{2}\) properties are coupled with each other. Both temperature and pressure vary with well depth along with CO\({}_{2}\) flow. Therefore, the calculation of temperature, pressure, and CO\({}_{2}\) properties must be coupled in the process of iteration.  
The wellbore is divided into \(N\) units along the direction of well depth. To ensure the calculation unit is much shorter than the well depth, the CO\({}_{2}\) properties of each and every calculation unit can be considered as constant. The injection temperature (\(T_{0}\)) and pressure (\(p_{0}\)) are regarded as initial conditions.  
For the \(i\)th calculation unit, the iterative method is as follows:  
(1) The temperature and pressure of (\(i\)-1)th calculation unit are used as the starting of the iteration, marked as \({T_{i}}^{0}\), \({p_{i}}^{0}\), so the temperature (\(dT\)) and pressure (\(dp\)) increment can be estimated according to Equation (5) and Equation (7). Then, temperature and pressure of \(i\)th calculation unit, marked as \({T_{i}}^{1}\), \({p_{i}}^{1}\), can be obtained.  
(2) The new temperature and pressure increment, \(dT\)' and \(dp\)', also can be calculated with the known of \({T_{i}}^{1}\), \({p_{i}}^{1}\).  
(3) The calculated \(dT\)' and \(dp\)' are compared with the initial \(dT\) and \(dp\). If the differences between the calculated values are within the error tolerance, the calculation ends and the calculation of the next unit begins; if not, replace \({T_{i}}^{0}\)and \({p_{i}}^{0}\) with new values and return to (1).
## 3 Results
### Wavelength Selection  
To understand the kinetics of AuNP aggregation during assays, we quantified the changes in absorbance of the mixture of water sample and detection reagent at selected wavelengths using the portable system. LEDs used for the presented assay emit light at wavelengths of 430 nm, 530 nm, 630 nm, 660 nm, and 690 nm. The aggregation profiles were analyzed in terms of different absorption ratios Abs630 nm/Abs530 nm ratio and the Abs690 nm/Abs430 nm ratio for each DNA-AuNP probe. Owing to the characteristics of noble-metal nanoparticles and their spectra, it is important to choose the appropriate illumination wavelength. The used citrate capped AuNPs had a maximum at 520 nm. Nevertheless, the plasmonic peak varies slightly, depending on the surrounding media, size distribution, and functionalization of the noble-metal nanoparticles. The detection efficiency of our instrument does not differ much at 530 nm and 430 nm (Figure 5). To be useful for photometric sensing, the absorption intensity ratio must be independent of colloid concentrations. Our measurements show that a ratio for Abs690 nm/Abs430 nm is more useful than choosing a wavelength close to the resonance peak.  
Figure 4: Aggregation kinetics of an AuNP solution, run-time = 8.5 min. Addition of an aggregation promoter generates different levels of color change, which leads to changes in the LED signals. (a) 2-LED-\(\upmu\)Spectrophotometer absorbance values at the selected wavelengths after salt addition at t = 40 s; (b) corresponding UV/vis curve using benchtop spectrometer in an overall time of 8 min.
# Kinetics of Hyaluronidazoleae Inhibition by Rice (_Oryza sativa_ L.) Protein Hydrolysate
## 2 Materials and Methods
### Kinetic Properties of Rice Protein Hydrolysate  
The method used here is reported elsewhere [16]. In brief, we first assayed the dose-dependent relationship between our rice protein hydrolysate and hyaluronidase. We placed our hydrolysate solution (40 \(\mu\)L; 0, 0.5, 1, 2, 5, 10, and 20 mg/mL) in a 96-well plate and then added hyaluronidase solution (5 \(\mu\)L; 0.5, 1, 2, 5, and 10 mg/mL); moreover, 5.0 mg/mL HA in acetate buffer (100 \(\mu\)L, pH 5.35) was the substrate. This was followed by kinetic studies, where we added our hydrolysate solution (50 \(\mu\)L; 1 0, 0.5, 1, 2, 5, and 10 mg/mL) to a 96-well plate and then hyaluronidase solution (5 \(\mu\)L; 1 mg/mL) was added. Next, we constructed a Lineweaver-Burk plot using the inverse of the substrate concentration [5] and the reaction rate \(V\):  
\[\frac{1}{V}=\frac{K_{m}}{V_{max}}\times\frac{1}{[\text{S}]}+\frac{1}{V_{max}}.\]  
Here, the maximum velocity and the Michaelis constant are denoted \(V_{max}\) and \(K_{m}\), respectively. In this double reciprocal plot, the linear regression line can be drawn and used for \(K_{m}\) and \(V_{max}\) calculation because its \(x\) intercept, \(y\) intercept, and slope are \(-1/K_{m}\), \(1/V_{max}\), and \(K_{m}/V_{max}\), respectively. In addition,this plot can be applied for understanding if an inhibitor's reaction mechanism is competitive, uncompetitive, or noncompetitive [17].
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## 1 Introduction  
Finding the shortest path in the presence of obstacles, referred to as the Euclidean shortest path problem, is one of the fundamental problems in path planning [1]. This problem arises in many industrial applications. The idea of using a flying robot such as an unmanned aerial vehicle (UAV) to navigate through a tunnel-like environment can be found in the inspection of dam penctsos [2; 3; 4], chimneys [5], ventilation systems [6], onshore oil and gas industry [7], narrow sewers [8], and other hazardous deep tunnels [9; 10]. In addition, many marine applications also require navigating through underwater tunnel-like environments with autonomous underwater vehicles (AUVs). These include, for instance, the inspection of different kinds of underwater structures such as offshore oil platforms [11], flooded spring tunnels [12; 13], water delivery tunnels [14], etc. In these applications, shortest path planning that minimizes the total distance travelled by the vehicles plays an important role in optimizing the energy consumption, thus extending the operation time without recharging their batteries [15; 16]. It may also reduce the travelling time and will be very useful for search and rescue missions during disaster events in underground tunnels [6; 17].  
Another example of the studied problem is to determine the location of a non-elastic chord between two points within a tube. Indeed, this problem can be found in controlling the deformation of slender tube-like robots actuated with at least one internal tendon [18; 19]. Calculating the tendon load effect on the tube wall requires determining the tendon location. As it is only attached at the tip, pre-positioned at the base, and the rest freely locates inside the innermost tube, its location results in the shortest path connecting two points at the base and at the tip.
## 2 Deep Neural Networks for Inverse Kinematics
### Unsupervised Learning
#### 2.2.4 Graph Neural Networks  
Graph Neural Networks (GNN) [73] are a type of Deep Neural Network specialized in processing data that can be represented as graphs. The main element of GNNs is the use of message passing for graph nodes to update their information by communicating with their neighbors. In the case of IK, this method presents the possibility of creating one neural network to solve the problem for every robot provided it is described as a graph. This way, the training does not depend on the manipulator's specific parameters. This can be seen in [74; 75; 76]. It also allows for motion constraints to be introduced as part of the robot's graph description. It is to be noted that this approach to IK, although promising, is still not as developed as others.
## 2 Existing Solutions and the Combined Approach of AFF_CGE
### Existing Solutions
#### 2.1.1 Signature-Based Detection  
Signature-based detection methods were initially developed to identify known attack patterns in plaintext traffic and have been integral to Intrusion Detection System (IDS) development since its early days. These methods work by analyzing known malware or attack techniques to extract behavioral features such as unique code fragments, protocol patterns, and byte sequences. The traffic is then processed using pattern-matching rules. For instance, the signature-based approach introduced by [PERSON] [4] utilizes Snort [5] for detecting malicious traffic. Although Snort supports Deep Packet Inspection (DPI), it is limited to plaintext traffic and cannot address encrypted traffic detection.  
To overcome this limitation, [PERSON] et al. [6] proposed a rapid signature-based Network Intrusion Detection System (NIDS) capable of identifying malicious traffic even when it is encrypted. Similarly, [PERSON] [7] recommended using the Covenant framework to analyze both encrypted and unencrypted traffic data to extract features suitable for signature-based detection methods.  
While signature-based detection is efficient for identifying known threats, it requires frequent updates to incorporate new attack signatures. Signature generation typically involves detailed analyses of known attack samples through techniques such as reverse engineering and dynamic analysis. However, as the signature repository grows, managing and updating these signatures becomes increasingly complex. To address this challenge, automated signature generation methods that leverage machine learning and big data techniques have been developed to enhance update efficiency. For example, [PERSON] et al. [8] proposed D-sign, a deep learning-based hybrid system for intrusion detection and unknown cyber-attack signature generation. This system rapidly detects new attacks, automatically generates valid signatures, and dynamically updates the signature database.  
Despite their effectiveness and speed in identifying known threats, signature-based detection methods face limitations in detecting unknown threats and handling encrypted traffic, as they rely heavily on known attack signatures. Encrypted traffic, in particular, obscures many of these signatures, posing a significant challenge for signature-based detection.
# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 1 Introduction
Our model contains three outputs: lesion detection as the main task, and signed distance map regression and contour delineation as the auxiliary tasks. The auxiliary tasks are operated with self-generated ground truth maps from the provided lesion ground truth, conveying the information of the boundary and the pattern of the lesion to the encoder. The structural information brings spatial awareness to the backbone model, broadening the predicted areas if the anticipated boundary covers bigger areas and vice versa. This also improves the localization of skin lesions since the spatial information is lost after sequences of pooling layers in the downsampling path. Considering the variation in size, we integrate Selective Kernel (SK) modules [18] into the skip connections to locate the receptive fields correlated with the scale of the lesions. The application of this module is based on its original concept, which dynamically adjusts the receptive field in harmony with the size of the target objects. While the atrous convolutional layer [19] enlarges the receptive field and then aggregates multi-scale features from different kernels, the Selective Kernel module evaluates the combination of information from multiple kernels and selects  
Figure 1: Challenging skin lesion samples. First column: Large variation in size. Second and third columns: Lesions with artifacts (mark, hair, bubbles). Fourth column: Low contrast compared to background. Fifth column: loose and detailed annotation. effective spatial scales. Furthermore, we attach the deep supervision module to each layer of the decoder with the purpose of multi-scale feature fusion, improving the decision of the last score map. Our architecture is trained and used for end-to-end prediction. The contributions of our paper are described as follows:  
* We evaluate the effectiveness of the two auxiliary tasks that are integrated into the decoder of U-Net architecture [15] for skin lesion segmentation. By feeding the information of the boundary and shape constraints of the lesions to the backbone model, we improve the pixel-wise classification and localization ability of the network;
* We develop a new skip connection which contains a Selective Kernel module [18] for learning and adopting multi-scale features from the encoder. This module accumulates information from different kernel sizes and yields only beneficial features from the global and comprehensive representation. The remainder of the paper is organized as follows. In Section 2, we provide an overview of the related literature in the field of skin lesion segmentation. The details of our neural network are analyzed and discussed in Section 3. Section 4 displays the achievements of our model through experiments on some public datasets. The conclusion is presented in Section 5.
# Covert Communication for Integrated Satellite-Terrestrial Relay Networks with Cooperative Jamming
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AF & Amplify and forward \\ AWGN & Additive white Gaussian noise \\ CDF & Cumulative distribution function \\ CEE & Channel estimation errors \\ CR & Cognitive radio \\ GEO & Geosynchronous earth orbit \\ HI & Hardware losses \\ ISTRN & Integrated satellite-terrestrial relay networks \\ LOS & Line of sight \\ NOMA & Non-orthogonal multiple access \\ OP & Outage probability \\ PDE & Probability of detection error \\ PDF & Probability distribution function \\ QoS & Quality of service \\ SatCom & Satellite communication \\ SNR & Signal-to-noise ratio \\ SR & Shadowed Rician \\ TDMA & Time division multiple access \\ UAV & Unmanned aerial vehicle \\ \end{tabular}
# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
###### Abstract  
The past decade has shown a growing awareness of the dangers of intentional interference (especially jamming and spoofing) with GNSS signals. The Antiference project uses reconfigurable digital signal processing methods in the detection, classification, and mitigation of interference by employing machine learning techniques. The ML-based jamming classifier uses distinctive features of spectrograms for the differentiation of various jamming attacks. A residual neural net is used to map the spectrograms to the different jamming types. It relies on a fingerprinting architecture. Fingerprints summarize the characteristics of all the incoming signals, which are stored in and matched to a database of previously encountered interference types. To validate the implemented functionalities, a developed test-bed runs test scenarios and benchmarks the results against two state-of-the-art COTS receivers with interference mitigation capabilities.  
machine learning; jamming; spoofing; ResNet50; COTS receivers; interference detection and mitigation +
Footnote †: _Enge. Proc._**2023**, 54, 61. [[https://doi.org/10.3390/ENC2023-15451](https://doi.org/10.3390/ENC2023-15451)]([https://doi.org/10.3390/ENC2023-15451](https://doi.org/10.3390/ENC2023-15451))