esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation Steps
### Security Framework Objective Function (SRF-OF)
```
1:Input:Neighbour nodes nbr1 and nbr2 from nbr table
2:Output:Best neighbour/parent to route packets
3:Function\(best\_parent(nbr1,nbr2)\) :
4:\(int\) nbr1_is_acceptable\(\leftarrow\)\((nbr1!=NULL\) and \(nbr\_is\_acceptable\_parent(nbr1))\);
5:\(int\) nbr2_is_acceptable\(\leftarrow\)\((nbr2!=NULL\) and \(nbr\_is\_acceptable\_parent(nbr2))\);
6:if nbr1!=NULL **and** nbr1_is_acceptable and is_blacklisted(nbr1) then
7:if nbr2_is_acceptable then
8:return\(nbr2\);
9:endif
10:return\(NULL\);
11:elseif nbr2!=NULL and nbr2_is_acceptable and is_blacklisted(nbr2) then
12:if nbr1_is_acceptable then
13:return\(nbr1\);
14:endif
15:return\(NULL\);
16:endif
17:if\((((nbr1\to trust\_value>nbr2\to trust\_value)\) or \((nbr2\to trust\_value<38))\) and \((nbr1\to rank<current\_rank))\) then
18:return\(nbr1\);
19:elseif\((((nbr2\to trust\_value>nbr1\to trust\_value)\) or \((nbr1\to trust\_value<38)))\) and \((nbr2\to rank<current\_rank))\) then
20:return\(nbr2\);
21:endif
22:if\(nbr1\equiv current\_preferred\_parent\) and \(within\_hysteresis(nbr1)\)then
23:return\(nbr1\);
24:elseif\(nbr2\equiv current\_preferred\_parent\) and \(within\_hysteresis(nbr2)\) then
25:return\(nbr2\);
26:endif
27:if\(nbr\_link\_metric(nbr1)<nbr\_link\_metric(nbr2)\) then
28:return\(nbr1\);
29:else
30:return\(nbr2\);
31:endif
```
**Algorithm 4** SRF-OF algorithm
```
1:Input: Read buffer received from SRF-IDS
2:for each neighbour do
3:\(nbr\leftarrow\) Get neighbour node details from SRF-IDS buffer
4:\(node\_verified_{T}\gets nbr\rightarrow\)\(verified_{T}\)
5:\(node\_pkts\_forwarded_{t}\gets nbr\rightarrow\)\(pf\_from\_ids\)
6:if\(node\_verified_{T}==0\)then
7:if\(node\_pkts\_forwarded_{t}==0\)then
8:\(direct\_trust\leftarrow(nbr\_total\_packets\_fw/(nbr\_total\_packets\_fw+0.6*(nbr\_ total\_packets\_tx-nbr\_total\_packets\_fw)))\)
9:elseif\(node\_pkts\_forwarded_{t}>0\)then
10:\(direct\_trust\leftarrow(nbr\_total\_packets\_fw/(nbr\_total\_packets\_fw+0.8*(nbr\_ total\_packets\_tx-nbr\_total\_packets\_fw)))\)
11:else
12:\(direct\_trust=0\)
13:endif
14:elseif\(node\_verified==1\)then
15:if\(node\_pkts\_forwarded_{t}==0\)then
16:\(direct\_trust\leftarrow(nbr\_total\_packets\_fw/(nbr\_total\_packets\_fw+0.85*(nbr\_ total\_packets\_tx-nbr\_total\_packets\_fw)))\)
17:else
18:\(direct\_trust\leftarrow(nbr\_total\_packets\_fw/(nbr\_total\_packets\_fw+0.5*(nbr\_ total\_packets\_tx-nbr\_total\_packets\_fw)))\)
19:endif
20:endif
21:endfor
```  
**Algorithm 5** Trust calculation at time T
## 3 Results and Discussion
### Performance Characterization of Dust Suppressant
#### 3.2.3 Test for Wind Erosion Resistance  
According to Table 9, the wind erosion rate of the sample sprayed with water is 9.39% under a wind speed of 5 m/s, and the wind erosion rate is 10.97% when the wind speed is 10 m/s. Therefore, when the wind speed increases, the wind erosion rate increases gradually as well. For the sample sprayed with dust suppressant, the wind erosion rate is basically dependent on whether the wind speed is 5 m/s or 10 m/s. The sample mass increases after it is subject to wind. This may be because the dust suppressant is hygroscopic and absorbs moisture from the air. According to the experiment results, the dust suppressant not only has good bonding performance, which can give the dust a \"crust\" so that it bonds together and is not easy to break, but it also holds the dust together against wind and prevents the moisture infiltrating inside due to wind.
## 3 Materials and Research Design
### Experimental Procedure  
The entire experimental session lasted for approximately 35-40 min per participant, depending on task completion speed (see Figure 1 -- workflow flowchart).  
Step 1: Before commencing with the main experiment, participants completed a preliminary training session designed to acquaint them with the experimental setup and procedures. During this session, they were given instructions on how to use the tools and were provided with examples of the tasks to ensure understanding. Participants were asked to seat at a distance of 60-65 cm from the screen throughout the session and the Tobii TX300 (Tobii Technology AB, Stockholm, Sweden) remote eye tracker was set up, allowing calibration for each participant to ensure the accurate recording of eye movements.  
Step 2: Participants completed two sets of 50-word translation tasks, one translating from English to Chinese and the other from Chinese to English, as a warm-up exercise. This was performed to ensure participants' comfort and consistency in their use of the equipment.  
Figure 1: Step-by-step workflow of the experimental procedure.  
Step 3: Participants began their translation tasks, during which their eye movements and keystroke logging data were recorded.  
Step 4: Follow-up participants' retrospective self-reflections.  
Step 5: Data collection and statistical analysis. To analyze the collected data, a Generalized Linear Model was employed, considering relevant co-variables. For the English-to-Chinese (E-C) dataset, variables included the size of the Area of Interest (AOI) for eye fixations, word frequency, average syllables per word, and the average number of letters per word. In the Chinese-to-English (C-E) models, AOI size and word frequency were used as key predictors. These linguistic variables were selected based on widely recognized readability metrics, such as the Flesch Reading Ease, Gunning Fog Index, Flesch-Kincaid Grade Level, Coleman-Liau Index, SMOG Index, Automated Readability Index, and Linear Write Formula. Core factors evaluated included sentence count, average sentence length (in characters, aligned with AOI size), syllabic complexity, the proportion of complex words, and character length per word.  
This carefully structured analytical approach enhances the reliability and validity of the findings, supporting the study's objective to investigate how cognitive resources are allocated during translation tasks.
# Improving Symbolic System-Level Synthesis by Solver Coordination and Domain-Specific Heuristics
## 5 Answer Set Programming (ASP)--Binding & Routing  
In the following section we briefly summarize how our coordinated synthesis approach leverages answer set programming (ASP) as logic solver (cf. Figure 2) to solve two subproblems in system synthesis: the binding **B** of computational tasks to PEs and routing \(\textbf{R}_{\text{route}}^{\textit{bits}}\) of packets for inter-task communication on resources of the NoC. We refrain from introducing the ASP syntax as this would go beyond the scope of this paper. Instead, we point the interested reader to the relevant literature.  
_Answer set programming_ is a declarative problem solving approach with its origins in solving problems in the domain of knowledge representation and reasoning [11; 12; 13]. Among others, one of ASP's distinguishing features is a rich and expressive, but yet simple modeling language, merged with state-of-the-art high performant solving techniques [16]. For system synthesis, ASP quite recently became an interesting alternative choice instead of Pseudo-Boolean satisfiability solving (PB-SAT) [14]. The authors showed that ASP scales better for densely connected architectures, such as NoC, compared to equivalent PB-SAT approaches. Here, the increased scalability is enabled by the possibility to express reachability within the modeling language of ASP directly, which is beneficial for multi-hop routing. These promising results encouraged us to favor ASP instead of PB-SAT solving to decide the routing \(\textbf{R}_{\text{route}}^{\textit{bits}}\) and the binding **B** in system synthesis.  
Based on our formal system model (see Section 4), a problem instance for an answer set solver is formulated containing the information of the platform model and the send and receive dependencies of messages between computational tasks from the application model. A solution is then described in a problem encoding derived from [14], which captures relevant constraints of a feasible binding and routing: each computational task has to be bound to exactly one PE, the utilization of a PE should not exceed 100% and a route from sender to receiver should be acyclic. Furthermore, we include a necessary and sufficient condition to schedule two computational tasks on one resource non-preemptively [28]: Two computational tasks \(t_{1}\) and \(t_{2}\) with periods \(P_{t_{1}}\) and \(P_{t_{2}}\) and WCET \(C_{t_{1}}\) and \(C_{t_{2}}\) are schedulable if and only if  
\[\texttt{gcd}(P_{t_{1}},P_{t_{2}})\geq C_{t_{1}}+C_{t_{2}}. \tag{2}\]  
The benefit of adding this condition to the problem encoding of the answer set solver is that every pair of computational tasks can be scheduled on the same PE which therefore has not to be checked within the background theory. To the best to our knowledge, our work is the first one to include this constraint within the logic solver of an SMT-based synthesis approach.
## 4 Analysis of Results
### Implementation Recommendations  
In addition to the protocol optimization described above, this research effort brought to our attention several design and implementation recommendations for commercially viable QKD systems. While these recommendations are not entirely new or novel, they are important to highlight for QKD performance, implementation security, and potentially formal certification efforts.  
1. Upon system startup, the decoy state protocol should be configured to quickly perform initial security checks to ensure the quantum channel is free from PNS attacks. For example, 1000 rounds of quantum exchange can be executed in a relatively short amount of time during initial calibration activities.
2. Configure the decoy state protocol to continuously monitor for PNS attacks in real-time and over several rounds of quantum exchange to increase confidence in the system's security.
3. The noise level (i.e., the dark count rate) should be measured during dedicated calibration activities with very large numbers of vacuum signals (e.g., \(\geq\)10\({}^{9}\)) intermixed with signal and decoy states to well-characterize the operational environment and system architecture.
4. During operation, the dark count rate should be compared to the calibration results in order to detect changes in the operational environment such as temperature changes or additional physical disturbances.
5. Minimize the vacuum state occurrence percentage but do not eliminate it. The state can be used as an indicator to monitor for attacks such as the blinding attack [66].  
Additionally, while [PERSON] and coworkers' work optimized the signal state MPN at \(\sim\)0.5, users may want to consider higher signal state MPNs such as those successfully demonstrated in the world's largest QKD network (i.e., \(\mu=0.65\)) [30]. Moreover, past work on the subject recommends MPNs on the order of 1.0-1.2 based on pragmatic technical assumptions [67].
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 2 Materials and Methods
### FAO-56 Penman-Monteith Model  
The FAO-56 model, proposed by [PERSON] et al. (1998), is a formula-based model for calculating evapotranspiration [5] and is known for its high computational accuracy. Since no experimental ET\({}_{0}\) data are available in the study area, FAO-56 was used to calculate ET\({}_{0}\) as the reference target output for machine learning models. This approach is acceptable and commonly employed under such circumstances [37]. The specific formula is as follows:  
\[\text{ET}_{0}=\frac{0.408\Delta(\text{R}_{\text{n}}-\text{G})+\gamma\frac{900} {\text{T}_{\text{means}}+273}\text{U}_{2}(\text{e}_{\text{s}}-\text{e}_{\text{a }})}{\Delta+\gamma(1+0.34\text{U}_{2})} \tag{1}\]  
where ET\({}_{0}\) (mm/d) is the reference evapotranspiration, R\({}_{\text{n}}\) (MJ/m\({}^{2}\)/d) is the net radiation, G (MJ/m\({}^{2}\)/d) is the soil heat flux density, T\({}_{\text{means}}\) (\({}^{\circ}\)C) is the mean air temperature, e\({}_{\text{a}}\) (kPa) is the actual vapor pressure, e\({}_{\text{s}}\)(kPa) is the saturated vapor pressure, \(\Delta\) (kPa/\({}^{\circ}\)C) is the slope of the vapor pressure curve, \(\gamma\) (kPa/\({}^{\circ}\)C) is the psychrometric constant, and U\({}_{2}\) (m/s) is the wind speed at 2 m above the ground. [PERSON] et al. (1998) provides the detailed calculation process of this model [5]. Additionally, radiation is calculated from sunshine duration using the Angstrom equation [38].
# A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection
## 2 Related Work
### Fabric Defect Detection
#### 2.2.2 Deep Learning-Based Methods  
These approaches are based on machine learning algorithms, as well as neural networks. Recently, due to the immense growth achieved by AI across all areas of research, these have become the most common method across the literature in the area, and this growth is likely to continue [71].  
There are many different approaches in this area, given the wide selection of neural network architectures available. However, we can clearly identify three main approaches which have been prominent for the last decade.  
The first is the use of Convolutional Neural Networks (CNNs), which are composed of multiple convolutional layers, mixed in with subsampling or pooling, performing increasingly more complex feature extraction between the input and output layers until reaching a final classification layer [72]. This appears to be the most commonly used approach in the reviewed articles.  
The second is based on object detection approaches across other domains. These revolve around the use of one-stage or two-stage detectors. One-stage detectors such as Single-Shot MultiBox Detector (SSD) [73] or You Only Look Once (YOLO) [74] treat object detection as a regression problem and learn class probabilities and bounding-box coordinates directly. Two-stage detectors such as R-CNN, Fast R-CNN [75], Faster R-CNN [76], or Mask R-CNN [77] approach the problem in two stages, using a Region Proposal Network (RPN) in the first stage to generate regions of interest, which are sent to the next stage for classification and bounding-box regression. One-stage detectors are much faster than two-stage detectors but have lower accuracy [78].  
The third is the use of generative models, which are neural networks trained to approximate high-dimensional probability distributions using a large number of samples. Their architectures involve numerous hidden layers. These models are usually used for generative tasks, such as finishing a word at the end of a sentence or generating images based on several instances. There are several variants of this approach, such as Generative Adversarial Models (GANs), or autoencoders [79; 80].  
Each of these topics is approached in their own subsection ahead.
## 2 Convective Heat Transfer Coefficients  
In this section, the correlations used to estimate the convective heat transfer coefficients are presented. The coefficients are determined largely by mathematical relations, but also by numerical approximations using reasonable concepts within maximum and minimum limits based on common sense according to the current application and similar case studies. The heat flux from hot gases to metallic surfaces can be estimated using [PERSON]'s convective heat transfer law  
\[q=h\big{(}T_{g}-T_{w}\big{)}, \tag{1}\]where \(q\) is the heat flux, \(h\) is the convective heat transfer coefficient, \(T_{g}\) is the reference gas temperature and \(T_{w}\) is the wall temperature. For compressible flow the reference temperature is the adiabatic wall temperature \(T_{aw}\). The adiabatic wall temperature, defined as  
\[T_{aw}=T_{s}+r(T_{T}-T_{s}), \tag{2}\]  
takes into account energy dissipation due to viscous heating. \(T_{s}\) is the static temperature, \(T_{T}\) is the total stagnation temperature and \(r\) is the recovery factor. The recovery factor takes into account the diffusivity of heat and momentum. If heat diffuses faster than momentum, the thermal boundary layer is thinner than the momentum boundary layer. In this case, a higher heat transfer rate is expected. The Prandtl number is used to compare the diffusivity of heat and momentum, such that the recovery factor is defined as  
\[r=Pr^{1/2}\ (\text{for laminar flow})\ \text{and}\ r=Pr^{1/3}\ (\text{for turbulent flow}). \tag{3}\]  
For \(Pr=0.7\), \(r\) is equal to 0.83 and 0.88 respectively. In this work an average value \(Pr=0.85\) will be assumed, regardless of the flow regime. The distribution of the heat transfer coefficient over the surface depends on the flow conditions, gas velocity and temperature distributions. Given the gas temperature distribution, the heat flux q can be computed by Fourier's Law as a function of the thermal conductivity of the gas \(k_{g}\) and the temperature gradient \((dT/dy)_{p}\),  
\[q=-k_{g}\bigg{(}\frac{dT}{dy}\bigg{)}_{p}. \tag{4}\]  
The heat transfer coefficient can be expressed in nondimensional form, which results in the Nusselt number \(Nu\) which is defined by  
\[Nu=\frac{lx}{k_{g}}. \tag{5}\]  
The solution of the fluid mechanics governing equations, even when the boundary layer approximation is used, is very complex for compressible flows with temperature dependent fluid properties. In a preliminary study, the heat transfer coefficient may be estimated by simplified analytical and experimental correlations. The heat transfer models presented in this paper are based on these correlations for a first order approximation. In the following sections, correlations for heat transfer coefficients are presented for the flow over stator vanes and disc blades, compressor and turbine disc faces, and compressor and turbine platforms (inner wall) and casing (outer wall).
# Interdisciplinary Urban Tunnel Control within Smart Cities
## 2 Systemic Approach to Urban Road Tunnel
### Urban Tunnel Management as a Smart City Component  
This section presents an illustrative example of how the intelligent tunnel can be integrated into smart urban area. Let us assume that the tunnel communicates with whole district and negotiate its control strategy not only locally but also with respect to criterial functions of whole district.  
For simplicity, let us build as a criterion function \(Q_{j}\) of \(j\)-sectors their financial impacts on the whole area. For example, transportation area around the tunnel can be represented by \(Q_{1}\) that includes all the financial implications of the loss of drivers' time. On other side \(Q_{2}\) captures the financial impacts of transport on the environment, e.g., pollution CO\({}_{2}\), NOX, PMx. \(Q_{3}\) describes energy losses in the whole area expressed in financial parameters, including fuel consumed by all vehicles in congestion or energy consumption of own intelligent tunnel operation (ventilation, lighting).  
For simplicity, consider only two possible tunnel control scenarios--either the tunnel is open \(S_{1}\) or closed \(S_{2}\). The main advantage of the intelligent tunnel as a smart city component is that the decision on the control strategy (\(S_{1}\) or \(S_{2}\)) is not made locally, but with regard to the financial impact of the whole district area. Described mathematically, control is carried out using two criterion functions:  
\[Q(S_{1})=Q_{1}(S_{1})+Q_{2}(S_{1})+Q_{3}(S_{1}) \tag{3}\]  
\[Q(S_{2})=Q_{1}(S_{2})+Q_{2}(S_{2})+Q_{3}(S_{2}) \tag{4}\]  
Determining the financial impacts on the whole district area is a difficult task. It is important to use all available information and prepare a traffic simulation model to allow the expression of these criterial parameters \(Q(S_{1})\), \(Q(S_{2})\) depending on the control strategies \(S_{1}\), \(S_{2}\).  
It is obvious that the described approach can be further extended to several different control strategies, including e.g., speed limitation in the tunnel (50 or 70 km/h), ramp metering with different options of vehicles entry into the tunnel, different tunnel ventilation scenarios, etc. All these variants represent an extended set of possible control strategies \(S_{1}\), \(S_{2}\), \(S_{3}\),... For each control strategy it is necessary to estimate financial impacts to the whole area to be able to provide right decision-making.
## 5 Numerical Results
In Figure 4, we compare the minimum energy gain of users of the proposed robust algorithm with the other three benchmark schemes with different estimation errors of users. From Figure 4, we can find that with the increase of the estimation errors, the minimum energy gain is decreased, while the reduction of the proposed robust algorithm is less than the non-robust scheme and the no-trajectory-planning scheme. This is because with the increase of the estimation errors, the worst-case location error will greatly increase the distance between UAV and user. Furthermore, compared with the no-trajectory-planning scheme flying as a circle, the non-robust scheme has less time to approach the user, which also leads to the fastest decline among all the schemes. Therefore, for the environment with location errors, it is necessary to introduce robust design into trajectory optimization. Figure 2: The optimized UAV trajectories under different schemes and battery capacity. The distance between UAV and user is affected by horizontal distance and flight altitude. Thus, we present the minimum energy gain of users of the proposed robust algorithm with the other three benchmark schemes with different flight altitude of UAV in Figure 5. It can be seen that with the increase of flight altitude, the decrease of user energy gradually speeds up. The reason is that when the flight altitude is greater than the horizontal distance \(d_{n}[t]\), the distance between the UAV and users is mainly affected by the flight altitude, and vice versa. Thus, in order to ensure that users receive more energy, the UAV can appropriately reduce the flight altitude when the estimation error is small. Figure 6 also compares the minimum energy gain of users of the proposed robust algorithm with the other three benchmark schemes with different WPT transmit power of UAV. We can find that the energy gain achieved by the proposed robust algorithm is the highest among the schemes, while the increase of energy is proportional to the transmission power. That is, the user's energy gain is only affected by the transmitting power of the UAV when the computation task is processed. Figure 4: Energy gain of users under different schemes and estimation errors of users. Figure 3: Energy gain of users under different schemes and battery capacity of UAV.