esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Evaluation
### Experimental Environment
#### 3.1.2 Fuzzy Logic Toolbox of MATLAB Environment  
Fuzzy Logic Toolbox (FLT) is a MATLAB toolkit using the fuzzy logic, which provides analyzing, designing, and simulating systems of functions, applications, and a Simulink block. The fuzzy logic model contains the stages of designing fuzzy inference systems. Functions of the Fuzzy Logic Toolbox are provided for many common methods, including fuzzy clustering and adaptive neuro-fuzzy learning. The toolbox allows one to simulate complex system behavior using simple logical rules, and then implement these rules in a fuzzy inference system.  
Fuzzy inference is a method that interprets values in the input vector and, based on user rules, assigns values to the output vector. Using editors and viewers in the Fuzzy Logic Toolbox, we can create a set of rules, determine membership function and analyze the behavior of the fuzzy inference system (FIS). We used all of the following editors and viewers to design our FIS models:  
* FIS Editor--we used to create and select the type of fuzzy output system.
* Membership Editor--we edited the membership functions associated with FIS input and output variables.
* Rule Editor--Using this editor, we developed and edited the Mamdani FIS fuzzy rules.
* Rule Viewer--Shows detailed FIS behavior to help diagnose the behavior of specific rules or examine the impact of changing input variables.  
Figure 4: The main window of the performance monitor.  
* Surface Viewer-This generates a three-dimensional surface from two input variables and the FIS output.
# A Swarm Intelligence Solution for the Multi-Vehicle Profitable Pickup and Delivery Problem
## 1 Introduction
They are based on iteratively improving solutions in a population and evolving the whole population until a set of good solutions is reached. Exploration-oriented features of P-metaheuristics have made them better than S-metaheuristics at diversifying the search space. The most famous examples of P-metaheuristics are algorithms that are inspired by natural behaviour such as genetic algorithms and swarm intelligence (SI) [5]. Generally, there are two phases that should be applied to obtain a good problem solution: solution construction and solution improvement. Solution construction involves creating one or more initial possible solutions that can be considered as a starting point for exploring the search space. Solution improvement is based on modifying the starting solutions to improve them, according to predefined strategies, until a high-quality solution is reached or stopping criteria have been met (e.g., a maximum number of iterations, or a specific amount of time) [5]. Due to the fact that the MVPPDP is an NP-hard problem (as it is a subtype of the SPDP, which is a special case of PDP) [4], metaheuristic algorithms can be employed to efficiently locate optimal solutions in a reasonable amount of time, particularly for problems of moderate to large sizes. In this paper, we introduce novel algorithms for tackling this problem, with the objective of enhancing the initial solution proposed in [6; 7]. Our approach is based on the utilisation of the Artificial Bee Colony (ABC) algorithm, which is one of the widely recognised artificial intelligence algorithms known as SI. Diverse strategies were implemented with ABC, each demonstrating efficacy in accelerating the search procedure and reducing processing time. The primary goals of this paper are (1) assisting food delivery applications and delivery companies in optimising their services through accelerated customer selection and route planning in order to maximise profits as well as customer satisfaction; (2) efficient transport planning that can help conserve energy resources by reducing the environmentally damaging effects of gas emissions; and that (3) for some tested instances, the proposed approaches achieve results that surpass the state-of-the-art methods documented in the literature for the MVPPDP. The rest of this paper is organised as follows: a review of some related work is presented in Section 2. Section 3 introduces the formal problem definition. The proposed method is described in detail in Section 4. Section 5 illustrates the experimental results. Finally, conclusions and future work are presented in Section 6.
## 3 Results and Discussion
### Process Water (PW)--Ecotoxicity and Characterization
The study showed that the temperature increase affected the composition of PW; the toxicity of PW produced at 250 \({}^{\circ}\)C was approximately four times higher than that obtained at 200 \({}^{\circ}\)C for the same reaction time [21]. These findings reinforce that temperature significantly impacts both the composition and toxicity of PW. When the daphnids were exposed to the previously obtained EC\({}_{50}\) values with the pH of the exposure medium adjusted to neutral, there was no change in toxicity levels (Figure 7). This indicates that the toxicity effects observed for PWs at different Co-HTC temperatures were not influenced by the acidic pH of the exposure medium (pH\({}_{\text{PW-180}}\) = 5.8; pH\({}_{\text{PW-215}}\) = 5.3; pH\({}_{\text{PW-250}}\) = 5.1), but probably by the specific composition of each PW and the interactions among their chemical constituents (Tables 7 and 8). However, while this conclusion appears evident, it should be interpreted with caution, as the toxicity of individual compounds and mixtures within each PW has not been tested. These results underscore the necessity of identifying alternative methods for treating and, most crucially, valorization of PW to reduce its potential environmental impacts. Despite its toxicity, the PW from the Co-CHT of sawdust and sewage sludge contains molecules with commercial potential (e.g., furfural). Instead of viewing this by-product as  
Figure 7: Average number of mobile _Daphnia magna_ microcrustaceans (bars \(\pm\) standard deviation, n = 6) after 48 h of exposure to effect concentration values for 50% of exposed organisms (EC\({}_{50}\)) previously obtained for process water (PW) from the co-hydrothermal carbonization (Co-HTC) of sawdust and non-dewatered sewage sludge at 180, 215, and 250 \({}^{\circ}\)C with pH adjustment of the exposure medium with NaOH (pH = 7). The asterisks (*) represent a significant increase in the number of immobile microcrustaceans compared with the control test (Dunnett’s test, \(p\leq 0.05\)). an effluent to be treated and disposed of correctly in the environment, it is imperative to assess the potential of PW to obtain marketable products. For example, furfural, levulinic acid, vanillin, and catechol are considered platform chemicals, which are molecules that can serve as a substrate to produce a range of other higher value-added products [72; 73; 74; 75].
## 4 CS Detector Architecture
#### State-of-the-Art Space Flight Instrumentation  
In this section, we briefly discuss the current state-of-the-art and the advantages of using a CS detector as compared to traditional detectors.  
Figure 5: A diagram of satellite constellations observing the same spatial region in order to capture a microlensing parallax of any microlensing event occurring the given field–of-view. X represents a satellite with a CS detector system.  
Figure 6: A potential CS implementation of the detector system using a telescope to acquire the light from the spatial region, a set of micro-mirror arrays to reflect light using CS projection methods, and a photodiode to capture a single measurement of the total reflected light.  
The front-end-electronics for acquiring photometric measurements will be completely transformed to a novel data acquisition approach using CS techniques, resulting in fewer data samples, eventually putting the image reconstruction load onto computational imaging. Key differences in the optical setup and read-out of traditional detectors and CS based detectors are shown in Table 2.  
The current state-of-the-art for microlensing parallax consists of large space observatories, like Nancy Grace Roman Space Telescope detecting an event, then alerting a microlensing event, which is then followed up by a ground observatory. Use of large observatories is very costly. A CS based instrument can detect a microlensing event and capture the complete set. If a parallax measurement is needed, a ground based observatory may be alerted. However, replacing a large observatory with a smaller instrument constellation for parallax detection, which acquires the same resolution science will be a game-changer, causing significant reduction in costs and resources.
## 4 Protocol Design  
Based on the previous analysis, we propose novel Temperature exchange-based Energy Adaptive Routing (TEAR) protocol. In this section, we explain protocol design and practical issues related to the realization of the temperature transfer based communication. We split the protocol realization into two phases: Discovery phase and Forwarding phase.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 3 Method
### Decoupled High-Frequency Learning  
While DFACC can effectively suppress the loss of high-frequency information in channel fusion, it still cannot solve the problem of high-frequency information loss caused by interval sampling in the space-to-depth operation, which disrupts the spatial structure of the original feature maps. Therefore, DHFL decouples the high-frequency components from the input features as supplementary features. To achieve this goal, we extend the concept of wavelet transform and employ multiple high-pass filters, low-pass filters, and linear convolutional layers to capture high-resolution information.  
More specifically, we first define three filters-- \(k_{\text{HH}}\), \(k_{\text{HL}}\), and \(k_{\text{LH}}\)--by alternately applying high-pass and low-pass filters:  
\[\begin{split} k_{\text{LH}}&=\frac{1}{\sqrt{2}} \Bigg{[}\begin{array}{cc}-1&-1\\ 1&1\end{array}\Bigg{]},\quad k_{\text{HL}}=\frac{1}{\sqrt{2}}\Bigg{[} \begin{array}{cc}-1&1\\ -1&1\end{array}\Bigg{]},\\ k_{\text{HH}}&=\frac{1}{\sqrt{2}}\Bigg{[}\begin{array}{cc}1&-1\\ -1&1\end{array}\Bigg{]},\end{split} \tag{6}\]  
where HH represents the high-frequency information at the point with the highest amount of high-frequency information, HL stands for the main component of the high-frequency information, and LH captures the high-frequency information that cannot be obtained by a single high-pass filter.  
Next, we apply these three filters to the input features, \(x\), alternately to obtain the corresponding downsampled high-frequency components, denoted by \(z_{\text{HH}}\), \(z_{\text{HL}}\), and \(z_{\text{LH}}\). The \((i,j)\)-th element in these high-frequency components can be expressed as follows:  
\[\begin{split} z_{\text{LH}}(i,j)=&-x(2i-1,2j-1)-x(2i-1,2j) \\ &+x(2i,2j-1)+x(2i,2j)\\ z_{\text{HL}}(i,j)=&-x(2i-1,2j-1)+x(2i-1,2j)\\ &-x(2i,2j-1)+x(2i,2j)\\ z_{\text{HH}}(i,j)=& x(2i-1,2j-1)-x(2i-1,2j)\\ &-x(2i,2j-1)+x(2i,2j)\end{split} \tag{7}\]  
To obtain features with comprehensive high-frequency information, we convolve the high-frequency components \(z_{\text{LH}}\), \(z_{\text{HL}}\), and \(z_{\text{HH}}\) using a single linear convolutional layer, thereby producing high-frequency complimentary features, \(y_{\text{DHFL}}\), for DFACC.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 2 Background and Problem
### Execution Trace Components  
A workflow orchestrates a set of computing tasks to form a complex workflow logic. Typically, besides a \"start\" state and an \"end\" state, a workflow comprises two kinds of steps, namely tasks (where the main computing functions are performed) and control stages (such as \"choice\", \"fork\", \"join\" and \"loop\"). For illustration purposes, Figure 1 presents the diagram of a simple workflow, called SimpleFlow, which includes two tasks (Task 1 and Task 2). The workflow logic of SimpleFlow is as follows: After the workflow starts, two parallel threads are spawned at the \"fork\" stage. In the first thread, Task 1 is executed once. In the second thread, Task 2 is executed iteratively, depending on the output of the \"ok2\" stage. After both threads enter the \"join\" stage, the workflow terminates.  
In real-world workflow engines (such as AWS Step Functions), workflows are usually defined in a domain-specific specification language (such as a JSON-style language). The execution traces of a workflow can be identified at different levels of abstraction. For example, we can consider each task as a single execution stage of SimpleFlow or a combination of the lower-level execution stages (e.g., scheduling, queuing and processing). For simplicity, we adopt the former (i.e., more abstract) level of execution traces. A trace component can be viewed as a part of the workflow, which can be described by some temporal pattern. Informally speaking, a trace component represents a subset of possible execution traces in a workflow. Consider the following two examples of trace components for SimpleFlow:  
**TC-A**: Task 2 is executed only once.
**TC-B**: Task 2 is executed at least twice.  
The formalisation of trace components and their relationship with workflows is provided in Section 4.  
Figure 1: Specification of SimpleFlow
## 5 Identification Experiments with Different Sensors in the Same Area
### Hj-2a Ccd3  
As for Figure 8, the remote sensing image originates from the CCD3 sensor on the HJ-2A satellite, with a spatial resolution of 16.5 m and dimensions of 325 \(\times\) 367 pixels. Compared to the TMS sensor, the CCD3 sensor has a lower spatial resolution, but the images produced are smoother with less noise and local variation, facilitating the identification of the overall Arctic sea ice imagery. During this period, the sea ice is in its melting phase, leading to the presence of numerous thin and fragmented ice pieces in the image, which increases the complexity of identification. Overall, EAEND model (10) and the MD method outperform in terms of identification results, while the Otsu thresholding method lags slightly behind. As indicated in Table 6, EAEND model (10) achieves an identification accuracy of 0.948641, excelling in other metrics as well compared to the competing methods. The SVM method, on the other hand, exhibits the weakest performance, particularly notable for its substantial under-detection in the central area of Figure 8d, where the Kappa coefficient is only 0.529627, and the OA, AA, PP have the lowest values. This underperformance implies that the SVM method is susceptible to disruption during the identification process, leading to compromised stability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MetricsMethod** & **EAEND** & **SVM** & **Otsu Thresholding** & **MD** \\ \hline OA & **0.982533** & 0.974369 & 0.965996 & 0.928233 \\ AA & **0.988634** & 0.932212 & 0.979035 & 0.979034 \\ PP & 0.999629 & 0.969362 & **1.000000** & 0.922038 \\ Kappa & **0.944920** & 0.911825 & 0.896257 & 0.730540 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Identification metrics and results of four methods for the Arctic sea ice from ZY3-02 TMS.  
Figure 7: Identification results of four algorithm-assisted CEM method (1) for the Arctic sea ice from ZY3-02 TMS. (**a**) Original image. (**b**) Ground truth. (**c–f**) Identification results of EAEND model (10), SVM method, Otsu thresholding method, MD method.
# On the Security of Offloading Post-Processing
### Contributions  
In this work, we contribute to these efforts via a complementary approach by presenting novel methods for offloading (or outsourcing) the most expensive parts of the QKD post-processing stack. For this, we combine our expertise from QKD and cryptography. We will clearly demonstrate the problem, show the benefits of our approach and present new protocols and barriers.  
More precisely, we present and analyze protocols for outsourcing information reconciliation to external and untrusted environments, therefore facilitating new application scenarios (e.g., usage in low-power access networks). We furthermore discuss possibilities to outsource the privacy amplification step, which could further help to reduce the required processing power in QKD nodes. Additionally, we present possible use cases to undermine the practical relevance of the novel developed methods.
# The Opto-Electronic Functional Devices Based on Three-Dimensional Lead Halide Perovskites
## 3 The LHP Based Functional Devices  
### Lasers  
#### Lasers  
Asers and Factors Influencing Their Performances  
LHPs are ideal candidates for gain materials in next-generation lasers. By now, a diversity in different lasing configurations has been enjoyed, which could be referred to in Figure 4. As an instance, [PERSON] et al., 2016 put forward a general structuring approach for a LHP-based distributed feedback (DFB) laser by evaporating the MAPbI\({}_{3}\) film onto a nanoimprinted polymer gratings with different pitches (\(\Lambda\)) [72]. The laser emission wavelength could be altered in the range of 770-793 nm by tuning the grating periodicity. A threshold in the level of \(\sim\)uJ/cm\({}^{2}\) has been found when pumped by a 532 nm nanosecond pulsed laser. [PERSON] et al., on the other hand, reported a LHP photonic crystal (PhC) laser, fabricated by depositing the MAPbI\({}_{3}\) thin films onto a 2D quartz PhC resonator [7]. In the PhC cavity, light-matter interaction could be enhanced via the in-plane distributed feedback, and single mode lasing could be achieved by appropriately matching the PhC band edge mode with the gain band of LHP. The device exhibited an output beam with a coherent and well-defined spatial profile, showing a small angular divergence (<2\({}^{\circ}\)) in the far-field pattern. The laser demonstrated a threshold of 68.5 uJ/cm\({}^{2}\) when pumped by a 532 nm laser with 270 ps duration and showed a differential quantum efficiency of 35.8 \(\pm\) 5.4%. By now, in addition to DFB lasers [72; 73; 74; 75; 76; 8] and PhC lasers [2; 7], LHP have also been utilized in lasers including whispering gallery mode (WGM) lasers [77], vertical cavity surface-emitting lasers (VCSEL) [78; 5; 14; 79], and so on. The detailed parameters regarding LHP-based lasers' performances are presented in Table 1.
## 4 Case Studies
### Glucose Management Protocol Case Study  
The glucose management protocol is a nurse-driven protocol with the aim of controlling and maintaining stable glucose levels in patients. The process is relatively simple; a nurse measures the glucose value of a patient either using point-of-care measurement or central laboratory determination using arterial blood samples. Based on the result, he/she adjusts the settings of the perfusor (i.e., machine that administers drug to the patient on a continuous basis). In this paper, we study the wards A and B, where the same glucose management protocol (shown in Tables 1 and 2) has been implemented for many years. Therefore, in this paper, we analyze wards \(A\) and \(B\). The protocol under consideration concerns intravenous administration of short-acting insulin and applies to every patient till the moment when she/he can receive oral nutrition. The protocol assumes that the target values of blood glucose are between 4.5 and 7.0 mmol/L. The starting scheme (perfusor settings for a new patient) is shown in Table 1, and the adaptation schemes for the perfusor settings are presented in Table 2.  
In our study, we measured the perceived compliance measure of the wards' coordinators and nurses working on those two wards and calculated compliance levels according to different metrics. First, we interviewed the wards' coordinators and nurses regarding the awareness, use and reasons for (non)adherence to this protocol. In ward \(A\), the interviewees indicated that they did not follow the protocol (anymore), although many of them (57%) were aware of it. Controlling and managing the blood glucose level is achieved based on the experience and insight of nurses. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Starting Schema**} \\ \hline \multirow{2}{*}{**Blood Glucose (mmol/L)**} & **Insulin Perfusor Setting** & \multirow{2}{*}{**Insulin Bolus**} \\  & **(50 units/50 mL NaCl 0.9\%)** & \\ \hline \(<\)7.0 & - & \\ \hline
7.0–8.0 & 1 unit/h & \\ \hline
8.0–10.0 & 2 unit/h & \\ \hline
10.0–15.0 & 4 unit/h & 4 units insulin \\ \hline
15.0–20.0 & 6 unit/h & 6 units insulin \\ \hline \(>\)20.0 & 6 unit/h & 8 units insulin \\ \hline \end{tabular}
\end{table} 
Table 1: Starting scheme of the glucose management protocol used in the study.
# A Metamaterial Solution for Soundproofing on Board Ship
## 3 Metamaterial Solution: Materials, Methods, and Design
### Metasolution Geometry  
The Metasolution is composed of a periodic structure with an in-plane repetition of a primitive cell (Figure 4). The unit cell consists of two mass elements and a series of internal beam connections. The geometry of the primitive cell is composed of three main elements: two panels (mass elements) and a system of beams connecting the two panels (spring equivalent element). The system is composed of 4 S-shaped beams which connect the external part of the first panel to the inner area of the second panel.  
From the dynamic point of view, the mass elements work with a rigid body motion, and the beam-like connections deform in bending, giving an MSM type of behavior to the solution that leads to optimal sound insulation performance over a wide frequency range.  
In the design process, particular attention was paid to the resonance-dominated region of the STL and to the interaction between the mechanical and air stiffness inside the panel (MAM behavior). The internal beams are dimensioned to give the solution a self-standing capacity, at the same time avoiding excessive stiffening of the structure, and therefore not affecting the acoustic performance in the medium-low frequency range.  
The unit cell is made of steel, with properties as listed in Table 1.
# Synchronous Rectification Method for CLLC Resonant Converters Based on State-Trajectory Models
## 3 SR Method for CLLC Resonant Converters Based on State-Trajectory Models  
It is obtained from the analysis of the Section 2 that the key to synchronous rectification is to calculate the timing of synchronous rectifier tubes in over-resonant and under-resonant states, and to propose a synchronous rectification strategy based on the state-trajectory model.
## 2 Geographic Hash Table (GHT), the first DCS Proposal
### GHT Discussion  
One of the main drawbacks of GHT is the complexity added by GPSR when it applies the perimeter routing algorithm, since it requires (among other things) to planarize the connection link graph of the sensornet. Some other geographic routing proposals (e.g., [16; 17]) can be found in the literature and could be also applied to DCS. In addition, there are some proposals that introduce DCS-specific routing protocols (Section 3. covers these proposals).  
Using a single home node could create a hot-spot problem because all traffic targets a single node (the home node) that extends its battery quicker than other nodes. The authors propose to use several local replicas, by means of PRP, in order to reduce the potential high load suffered by a single home node. Then, if several replicas are answering consumer queries the load is balanced among them. However, local replication does not solve the routing hot-spot problem. For a very popular content many queries will reach the home perimeter, thus the home perimeter surrounding area becomes a routing hot spot. Therefore local replication helps to balance the home node extra-energy expenditure, but it does not fully solve the routing hot-spot problem. As it will be shown later in this paper, it is necessary to replicate the data in different places along the sensornet in order to reduce that effect (see Section 5.).