esa-sceva/satcom-chunk-collection huggingface dataset:
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 6 Simulation Results and Analysis
### Performance Analysis
However, DQIR requires all ENs that receive broadcast information to transmit a message to a designated agent for routing decisions. Although this method offloads the reception energy consumption from DEN to an additional agent without energy constraints, leading to more balanced energy consumption, the excessive overhead from replies significantly increases energy consumption and delay. In contrast, through the combination of PRM and pre-selection rules, FDRR effectively reduces redundant transmissions by dynamically adjusting activation ranges and requiring only ENs that meet the pre-selection rules to respond. As a result, compared to DQIR, FRDR achieves lower MTD and MECP, as well as a higher network lifetime. Moreover, by considering RFRV, FRDR effectively avoids selecting candidate routers that will introduce high routing failure risk, which further enhances the performance of PDR. Specifically, when the PDR of DQIR drops to 0.80, FDRR maintains a higher PDR, achieving improvements of 18.90%, 20.68%, and 21.96% at EN densities of 300, 350, and 400, respectively. To summarize, the performance superiority of FRDR mainly comes from the PRM and DQN-based routing decision mechanism. PRM dynamically adjusts activation ranges, which works with pre-selection rules, further reducing unnecessary reception overhead. Meanwhile, the RFRV, in conjunction with other factors such as residual energy, distance to the GW, and transmission hops, is integrated into the DQN-based routing decision mechanism, effectively reducing transmission disruption and enabling faster delivery to the GW while balancing energy consumption. Consequently, FRDR significantly enhances PDR and network lifetime while maintaining a comparable transmission delay.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 3 Results
### Surge Response  
The FEM model's predictions of the surge response of the spar hull for an 11.39 s wave period and a 12.65 m wave height were contemplated to validate the ANN model in the current study. Figure 6 shows the surge responses of the spar hull from the training data of the ANN model results alongside the FEM results. The expected responses of the network outcome align with the FEM results.  
The statistics of the surge response of the spar platform predicted by ANN and FEM for a wave height of 12.65 m and wave period of 11.39 s for the training results are shown in Table 3. The ANN model was used to predict the surge response based on newly chosen environmental forces and a mechanical parameter (Figure 7). A 3000 s time history of surge response was predicted by the ANN model for a wave height of 9.65 m and a wave period of 10.10 s.  
Figure 6: The surge response time history predicted by the training data of the ANN and FEM results for 11.39 s wave period and 12.65 m wave height.  
Figure 7: Surge response time history predicted for wave height of 9.65 m and wave period of 10.10 s.
## 2 Background
### Attacks in V2X Scenarios
#### 2.4.2 Sybil Attack  
A spoofed identity attack (often referred to as a Sybil attack, illustrated in Figure 3) occurs when a malicious actor on a network creates multiple false identities or nodes in order to manipulate, disrupt, or gain disproportionate control over a network. In the context of vehicular networks, such as V2X (vehicle-to-everything) communication systems, a fake identity attack can have serious consequences for traffic safety, communication integrity, and trust within the network.
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 4 Discussion
### Influence of the Optimal Focusing Position  
In the actual experiment, there may be some deviations in the focusing evaluation of the image series, which is caused by experimental noise in the collected image and phenomenon of double peaks in the curve of focusing evaluation function (Section 2.2.1). In this section, experiments will prove that such deviations in the focusing evaluation cause very small errors in the measurement results. That means when there is a small offset in the position of the input focused image during the training and testing of the model, the two-input deep-learning TSOM method can still obtain accurate measurement results.  
In the experiment, defocused images with defocus distances of \(\pm\)200 nm, \(\pm\)400 nm and \(\pm\)600 nm are selected to replace the focused images as an input of Focus & TSOM-CNN in the test set. The testing results are shown in Figure 15.  
As can be seen from Figure 15, when defocusing images of \(\pm\)200 nm, \(\pm\)400 nm and \(\pm\)600 nm are selected to replace the focused images for testing, the MSE of the measurement model can remain below 10 nm\({}^{2}\), the MAE remains below 3 nm, which shows that the accuracy of the method is relatively stable. The result shows that the two-input deep-learning TSOM method proposed in this paper allows certain errors in focusing evaluation and has good applicability in actual experiments.  
Figure 14: Different shift of gold lines in the images. (**a**) No shift; (**b**) Shift to the left; (**c**) Shift to the right.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Test Set** & **Shifted Set 1** & **Shifted Set 2** & **No Shift Set** \\ \hline MSE/nm\({}^{2}\) & 7.24 & 6.97 & 5.18 \\ MAE/nm & 2.13 & 2.07 & 1.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The MSE and MAE of Focus & TSOM-CNN on two shifted gold line test sets and a no shift test set.
## 3 Materials and Methods
### Evaluation
#### 3.2.2 Model Development and Discriminability Evaluation  
A 5-fold cross-validation was used to evaluate the performance of each architecture, calculating the following metrics on the validation set: recall, precision, Cohen's Kappa, area under the ROC curve (ROC-AUC), and the precision-recall curve (PR-AUC). Mean and standard error were calculated among the 5 repetitions. As there is currently no accepted quantitative metric for measuring the clinical plausibility of attention maps, a family doctor (CV) on the team assessed it, following [PERSON]'s definition of a good explanation [38].  
The model was developed with Tensorflow in Python 3.9. To prevent overfitting, training stopped if the ROC-AUC did not improve more than 0.001 after 10 epochs, and the best weights were restored. Batch normalisation and a rate of 0.1 recurrent dropout were also included. The learning rate was reduced by 0.1 if the ROC-AUC did not change after 5 epochs. Batch size was set to 32, and the maximum number of epochs was 25. The optimizer was Adam [44], with an initial learning rate of 0.001. The number of neurons \(U\) was consistent in all the architectures and set to 128. The recurrent layer was a GRU in all cases. The kernel size was 5 for all the convolutional layers. While in Approach 1, four different kinds of attention layers were compared, only the best attention layer in that approach was used for Approaches 2 and 3.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Clinic Domain** & **Number of Features** \\ \hline Hospital admissions & 9 \\ \hline Clinical measurements & 50 \\ (e.g., questionnaires, BMI) & 64 \\ \hline Drugs & 3 \\ \hline Sociodemographics & 4 \\ \hline Smoking habits & 4 \\ \hline Visits to primary care & 8 \\ \hline Laboratory results & 67 \\ \hline Frailty deficits, as in [43] & 37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the clinical variables that were extracted from the EHR per data domain. The complete list can be found on our Github [41].
# The Microplastics Cycle: An In-Depth Look at a Complex Topic
## 2 The Microplastics Cycle
### Microplastics in Oceans
Generally, microplastics sink when their specific gravity exceeds that of seawater, which is 1.03 g/cm\({}^{3}\)[103]. Despite the fact that microplastics have a low specific gravity in general, some additives added to plastics to make them acceptable for certain applications have rendered their buoyancy unstable and caused them to sink as a result of an increase in specific gravity [104]. Additionally, microplastics that enter the sea or ocean with a lower specific gravity than water begin to sink with age, owing to many causes such as fouling by biofilm, microorganisms, organic pollutants, etc. [105]. Although specific gravity is a crucial factor in the fluctuating buoyancy of microplastics, other conditions may cause them to sink. As microplastics descend, they can adhere to aquatic vegetation like eelgrass, a process facilitated by the formation of biofilm. Two predominant epiphytic bacteria were identified as the key microorganisms within the biofilm, facilitating the adherence of microplastics to the grass (Vibrio and Exiguobacterium) [106]. The disintegration rate, nutritional alterations, and potential interactions of microplastics with specific species as they enter the food chain vary significantly, depending on factors such as environmental conditions, particle size, and chemical composition. As a consequence of photodegradation by sunlight, microplastics that are still in suspension break down at a considerably quicker pace than those that have settled to the bottom [107].
###### Abstract  
To detect areas with the potential for landslides, slopes are routinely subjected to stability analyses. To this end, there is a need to adopt appropriate mitigation techniques. In general, the stability of slopes with circular failure mode is defined as the factor of safety (FOS). The literature includes a variety of numerical/analytical models proposed in different studies to compute the FOS values of slopes. However, the main challenge is to propose a model for solving a non-linear relationship between independent parameters (which have a great impact on slope stability) and FOS values of slopes. This creates a problem with a high level of complexity and with multiple variables. To resolve the problem, this study proposes a new hybrid intelligent model for FOS evaluation and analysis of slopes in two different phases: simulation and optimization. In the simulation phase, different support vector regression (SVR) kernels were built to predict FOS values. The results showed that the radius basis function (RBF) kernel produces more accurate performance prediction compared with the other applied kernels. The prediction accuracy of this kernel was obtained as coefficient of determination = 0.94, which indicates a high prediction capacity during the simulation phase. Then, in the optimization phase, the proposed SVR model was optimized through the use of two well-known techniques, namely, the whale optimization algorithm (WOA) and Harris hawks optimization (HHO), and the optimum input parameters were obtained. The optimal results confirmed that both optimization techniques are able to achieve a high value for FOS of slopes; however, the HHO shows a more powerful process in FOS maximization compared with the WOA technique. In addition, the developed model was also successfully validated using new data with nine data samples.  
slope stability; factor of safety; support vector regression; whale optimization algorithm; Harris hawks optimization +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Transmission Availability Data System (TADS)  
#### 2.2.1 Overview  
NERC has been collecting North American automatic outage data for transmission elements of 200 kilovolts (kV) and above since 1 January 2008. An automatic outage is defined as an outage that results from the automatic operation of a switching device, causing an element to change from an in-service state to a not in-service state. Single-pole tripping followed by successful AC single-pole (phase) reclosing is not an automatic outage [51]. Transmission elements of BPS reportable in TADS are (1) alternating current (AC) circuits (overhead and underground), (2) transformers (no generator step-up units), (3) direct current (DC) circuits (a DC circuit element is a complete line, not just a single pole), and (4) AC/DC back-to-back converters [51]. In 2015, TADS' reporting changed to align with the implementation of the Federal Energy Regulation Commission (FERC)-approved bulk electric system (BES) definition [87]. Two additional voltage classes were added--namely, less than 100 kV and 100-199 kV. Sustained automatic outages are the only outages collected at voltage classes below 200 kV.
# An Access Control Model for Preventing Virtual Machine Hopping Attack
## 3 Preliminaries
### Access Control
#### 3.2.1 BLP Model  
The Bell-LaPadula security model [24] (BLP model), proposed by [PERSON] and [PERSON] in 1973, is a multi-level security model simulating military security strategy and is the most famous multi-level security model. The BLP model is used to control access to classified information. As the first mathematical model to formalize the security policy, it is a state machine indeed, which uses state variables to represent the security state of the system, and state transition rules to describe the change process of the system.  
The BLP model has many advantages: (1) The BLP model is one of the earliest models to describe multi-level security policy. (2) The BLP model is a strictly formalized model, and has the formalized proof. (3) The BLP model is a safe model with both discretionary access control and mandatory access control. (4) Control information can only flow from low security to high security, which meets the military department and other institution with high data confidentiality demand.  
However, it also has some disadvantages: (1) Low-security information flows to high-security objects, which may damage the data integrity of high-security objects and be used by viruses or hackers. (2) As long as it is legal for the information to flow from low security to high security, it does not conform to the minimum privilege principle, no matter whether there is demand for work. (3) The BLP model focuses on confidentiality control, but lacks integrity control, so it cannot solve the problem of hidden channels, which means high-level processes can convey information to low-level processes by sharing resources.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 6 Performance Results
### Node Lifetime  
The network lifetime is an important parameter; it has, however, several definitions [25]. Here we adopt the definition that the network lifetime equals the time until the energy of the first node depletes. In our simulations, we have utilized a battery size of 2821.5 mAh.  
Figure 12 presents the average node lifetimes in years for all simulations. Due to the extra TX/RX cell when Overprovisioning (OP), Path A has a significantly lower battery lifetime compared to Path B. Comparing Path A of OP with alternative RPE shows that OP has the lowest lifetime. It shows an imbalance as Path A is over-utilized and Path B not utilized. Dual Path (DP) also has worse performance at each node, compared to all simulations using RPE.  
All simulations show a correlation between lifetime and where the upstream copy was eliminated. \(\tau=816\), \(\tau=1624\) and OP has the highest number of packets eliminated at the source. Hence, the source has the highest lifetime, as it does not send the replicated packet as often as for the other scenarios. It is very interesting to note the decreased load on node A1 when \(\tau=1\). This is due to the frequency of alternating drops at sink, A1 and B1. On the other hand, it causes the source to have the lowest battery life of all RPE scenarios. The reason for the lowest battery life at the source is as mentioned in the selection of delays, that the source always sends both copies.  
We found that for all simulations the first node to run out of power is A1, except in \(\tau=1\) and \(\tau=8\) were in approximately \(\frac{1}{30}\) simulations B1 had the shortest lifetime. Figure 13 presents the lowest node lifetimes. As seen previously in Figure 11, no elimination was registered when using \(\tau=816\) or \(\tau=1624\), causing traffic to behave as DP and SP. This is reflected in the lowest lifetime as for both scenarios it is within 7 days of SP and DP. The effects of OP are clear with A1 running out of battery 338 days before any other version due to the over-utilization at Path A. As mentioned in Section 4.1, packet size matters, and the effects of utilizing RPE is clear on the node lifetimes.  
Figure 12: Scenarios and their average node lifetimes in years (Note: y-axis starts at 4 years).  
Figure 13: Lowest node lifetime for the different scenarios.
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 2 Materials and Methods
### Materials and Architecture of the Devices
#### 2.1.1 High-Speed Single-Mode VCSELs' Structure and Fabrication Process  
The single-polarization, single-mode VCSELs described in this manuscript operate at the 1.5 \(\upmu\)m wavelength window [9; 30]. The structures were based on a double-mesa short-cavity design with a length threefold the laser's emission wavelength, permitting the minimization of the photon lifetime. To reduce parasitic limitations, the double-mesa VCSEL module was fully encapsulated in benzocyclobutene (BCB). A highly strained active region embedded between an n-doped InP layer and a highly doped AlInAs cladding (Figure 1a) enhanced the intrinsic dynamic performance of the structure. The optical gain was provided by six AlGaInAs quantum wells with a 5.0 nm thickness, grown with a 1.7% compressive strain. The dielectric distributed Bragg reflectors (DBRs) at the top (outcoupling) and the bottom (hybrid) of the design had reflectivity percentages of 99.3% and 99.9% respectively. A circular p\({}^{+}\)-AlGaInAs/n\({}^{+}\)-GaInAs buried tunnel junction (BTJ) with a diameter of d\({}_{\text{bij}}\) = 5 \(\upmu\)m confined the current within the structure. A p\({}^{+}\)n junction with a reversed bias expanded over the bottom mesa diameter d\({}_{\text{bm}}\) to achieve current blocking outside the aforementioned area, whereas the BTJ was overgrown by an n-type doped indium phosphide layer to counterpart the length of the cavity. The capacitance of the structure C\({}_{\text{bm}}\) was proportional to (d\({}^{2}\)\({}_{\text{bm}}-\)d\({}^{2}\)\({}_{\text{bij}}\)) and was associated with the depleted region due to the steep change in the concentration of the reverse-biased junction impurities. The double-mesa architecture proposed in this manuscript allowed the construction of 16 \(\upmu\)m bottom mesas, which limited the capacitance of the VCSEL and enhanced its overall response.  
Figure 1b shows the power, current, and voltage characteristics of the double-mesa VCSEL; the inset shows its optical spectrum at 9 mA. At standard temperature conditions, the lasing threshold was achieved at around 1.0 mA whilst the peak output power was met at approximately 4 mW. To measure the small-signal modulation response of the VCSEL (Figure 1c), an HP 8519C network analyzer was used. The maximum modulation bandwidth was achieved at 10.4 mA and measured to be 20.6 GHz.
# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
The possible interference scenario for LDACS is portrayed in Figure 2. In words, it can be affirmed as follows: (a) LDACS FL is impaired by DME GS (FL), not by DME airborne station (AS), as (RL) is not active in this part of spectrum, (b) LDACS RL is impaired by both the DME GS (FL) and DME AS (RL), (c) DME FL is impaired by interference from both the LDACS GS (FL) and LDACS AS (RL) and (d) DME RL is impaired with interference from LDACS AS (RL), not from LDACS GS (FL), as it is not active in this part of the spectrum. Figure 1: L-band Spectrum Occupancy [9]. Identifying the possible interference scenario, the role of interference mitigation is recognized as critically important as any malfunctioning of the licensed system can affect flight safety. The rest of the paper is oriented as follows: Section 2 lists the literature survey of existing DME interference mitigation techniques in LDACS with their advantages and drawbacks. Section 3 expounds on the system and noise model used for this work. Section 4 elaborates the theory and functioning of the proposed method in spotting and reducing DME interference. The section also includes the detailing of nonlinear methods used for the comparison of the proposed method in DME mitigation. Section 5 elucidates the results obtained for the proposed method in terms of reducing the bit error rate in received data with other DME interference reduction techniques. The following are the list of abbreviations used in this paper.
## 2 LIG-Based Wearable Systems for Multifunctional and Intelligent Health Care  
When applied to sensors, LIG is effective for monitoring a range of physiological signals such as electrophysiological signals, temperature, electrochemical signals, and mechanical signals. Recently, there has been a growing interest in researching multifunctionality in LIG-based devices due to the increasing demand for systematization. This is primarily due to the following reasons. First, many diseases and health conditions are evaluated based on a combination of physiological signals, so multi-mode physiological signal monitoring is crucial for LIG-based wearable systems. Second, LIG-based wearable systems are becoming \"portable doctors\" to meet the needs of long-term health monitoring in daily life. This means that LIG-based wearable systems can not only collect physiological information, but also process or transmit physiological information in situ, and even realize the diagnosis and treatment of diseases. This kind of application demand puts forward the requirements of integrated, all-in-one, self-powered, and intelligent LIG-based wearable systems [22; 23; 24; 25].  
This section presents eight examples of LIG-based wearable systems in health care to illustrate the use of LIG in monitoring multi-physiological signals, providing multi-functional health care, and enabling intelligent diagnosis and treatment. The eight systems described in this section represent several distinctive efforts for daily long-term health care applications including the gas permeable system, green system, self-alarm system, self-powered system, machine learning-based system, the system with actuators, anti-interference system, and multi-model system. We will start with the application requirements they solve, introduce their manufacturing process and performance, and discuss their application prospects in detail.