esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## Preliminaries
### Artificial Neural Networks  
Artificial neural network (ANN) [PERSON] (1998); [PERSON] (1999) is a statistical learning algorithm inspired by the neural network of biology. ANN generally refers to a model with neurons (nodes) forming a network through the binding of synapses that have a problem-solving ability by changing the binding force of synapses through training. This study used multilayer perceptron (MLP). The basic structure of ANN is composed of the input, hidden, and output layers, and each layer is made up of multiple neurons. Training is divided into two steps: forward and backward calculations. In the forward calculation step, the linear function composed of the weights and thresholds of each layer is used for calculation, and the result is produced through the nonlinear output function. Thus, ANN can perform nonlinear classification because it is a combination of linear and nonlinear functions. In the backward calculation step, it seeks the optimal weight to minimize the error between the predicted and target value (the answer).
## 2 Materials and Methods  
### Tested Materials  
Portland cement (CP) type II 32.5 N according to European standards [31] was used. The cement particle size was less than 90 \(\upmu\)m Three different wastes have been analyzed: (1) Chilean-Peruvian scallop shells (Argopecten purpuritas) (SC) from the Chilean aquaculture industry were used. The material was previously crushed to obtain a particle size lower than 350 \(\upmu\)m. (2) Eggshell (E) was generated as solid waste generated from bakeries and was previously crushed to a particle size of less than 350 \(\upmu\)m. (3) Fly ash (OR) from olive pomace combustion in a power plant in southern Spain was used. The ash was sieved to obtain a maximum particle size of 350 \(\upmu\)m. The particle size distribution is shown in Figure 1. It was measured by using a Saturn DigiSizer II model. Egg and scallop shells present the highest differential percentages between 50 and 250 \(\upmu\)m and are similar for all the sizes of this interval, The average particle size of scallop shells is 98 \(\upmu\)m, as shown in Figure 1. Eggshells present a medium value (85 \(\upmu\)m) slightly lower than scallop shells. Fly ash from olive pomace combustion presents the highest differential percentages in the range between 20 and 150 \(\upmu\)m with a medium particle size of 44 \(\upmu\)m, but all of them are higher than Portland cement.  
The chemical composition of the materials was determined after chemical attack and dissolution at 750 \({}^{\circ}\)C (ASTM D 3682 [32]) using atomic absorption spectroscopy. Table 1 shows the main chemical components; the composition is expressed as oxides (but this does not imply that they are present in the material as oxides, either before or after calcination). The specific density was obtained according to ASTM D 854 [33].  
As shown in Table 1, the main component in the shells of eggs (E) and scallops (SC) was calcium carbonate [11; 12; 13]. The calcium carbonate decomposes at a temperature between 650 and 700 \({}^{\circ}\)C, so when analyzed after its calcination, CaO appears and the LOI collects, which corresponds to CO\({}_{2}\) decomposed during the previous calcination. Additionally, the eggshells presented a little more SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), and SO\({}_{3}\). However, the scallop shells presented a greater amount of Na because, unlike eggshells, they are in contact with seawater. Olive oil pomace ash is mainly composed of silica and potassium, and the LOI value is due to the unburned matter present in the ash. According to EN 13282-2 [5], all wastes complied with the permitted concentration of SO3: less than 4%.  
The specific gravities of these wastes are lower than those of Portland cement (CP). Both types of shells present a similar specific density because they have a similar chemical composition.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 3 Reliability of Tunnel Lighting System  
### Definition and Objectives of Reliability  
A system is a collection of components, the system reliability depends on the type or number of components, the reliability of components, and also how they are connected [31].  
The reliability research into traditional tunnel lighting systems includes the hardware, software and management factors of the lighting system, covering both the reliability guarantee and standard establishment in the initial construction stage of the system, and the reliability maintenance and improvement in the operation and maintenance stage, which lasts the whole life cycle of the lighting system. The methods to improve the reliability of general systems, such as the backup mechanism, redundancy mechanism and changing the series-parallel connection relationship of the system components, are also of significance to the reliability research into tunnel lighting systems. However, due to the significant differences between tunnel lighting systems and most control systems, and given their great impact on traffic safety together with the high maintenance costs, the reliability research into tunnel lighting systems needs to comprehensively consider multiple factors including technological advancement and scalability, ease of maintenance, sustainable development and long-term economy. Table 10 describes the key features and objectives of a highly reliable tunnel lighting system.  
\begin{table}
\begin{tabular}{c l} \hline \hline
1 & Tunnel luminaires provide an ideal solution for extremely corrosive environments. \\ \hline \multirow{2}{*}{2} & Increase in visibility for drivers helps to detect potential dangers and enables them to react in advance to guarantee a safe stopping distance. \\ \hline
3 & High performance with high visual comfort at every stage throughout the full life cycle. \\ \hline
4 & Powerful enabler of the complete management of lighting installation. \\ \hline
5 & Precise dimming, switching, data reporting, system monitoring and extremely short commissioning time. \\ \hline
6 & Minimum probability of occurrence of accidents and critical incidents. \\ \hline
7 & Rapid responses to any sudden events inside the tunnel. \\ \hline
8 & Promotion of safety and efficiency while easing operations and reducing costs. \\ \hline
9 & Reduction in tunnel closures and maintenance activities to fully ensure the tunnel experience. \\ \hline
10 & High level of cyber security and communication efficiency to guarantee the safe transmission of critical data. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Features and objectives of reliable tunnel lighting systems.  
Figure 7: Color temperature changes with weather condition.
## 2 Research Area and Data Source  
Subway Lines 1 and 2 are the backbone central lines of the Xi'an rail transit network. Compared with other newly built lines, the characteristics of the land along these two lines have already been well established. Therefore, the stations within Metro Lines 1 and 2 were selected as the research objects of this paper. Station selection needs to consider the degree of station area land use. Among the 44 subway stations on Lines 1 and 2, 17 stations with a large-scale centralized commercial complex in the surrounding area as well as integrated development characteristics and station-city integrated development potential were selected. The stations are similar in scale and average daily traffic levels, are connected by a relatively continuous underground space system, and have the same status in the entire subway line. The spatial heat values of these sites are relatively high. At the same time, station selection needs to consider the diversity and comprehensiveness of station types to ensure the universality of the results [30]. The station types in this study (based on land use functions) include residential stations (Longshouyuan, Dminggongxi, Fengchengnwulu, Bampo), public stations (Xingzhengzhongxin, Xiaozhai, Kangfulu, Tonghuamen, Weiyijie), commercial service stations (Zhonglou and Xiaozhai, Nanshaomen, Longshouyuan), and mixed stations (Yongningmen, Wulukou, and Fangzhicheng, Kaiyuanmen, Tiyuchang, Chaoyangmen) (Figure 1). There are 17 stations in total.  
Figure 1: Research object and scope.  
Based on most documents, the rail transit station areas mainly range between 400 m and 800 m [31]. In this paper, the station area is defined as the built-up area surrounding the station, with concentric circles centered on the station and an 800 m radius [32; 33; 34]. Given that the road network structure in Xi'an is mainly a grid or ring structure, the research scope is defined by the station area with clear plot ownership, continuous roads, and complete building outlines [35].  
The data used in this study include rail transit station area land use data, rail transit passenger flow data, social development data, and economic level data. Among them, the land use data are based on high-definition satellite images (Baidu map), on-site surveys and measurements, and GIS database statistics of built-up areas inside the Third Ring Road of Xi'an City. The land use data are used to calculate built environment indicators such as the land use type and building area within the station area. The source of the rail transit passenger flow data is the operating branch of Xi'an Rail Transit Transportation Group Co., Ltd., located in Xi'an, Shaanxi Province, China. Regarding these data sources, the acquisition time needs to avoid special periods such as holidays and ensure that the complete subway operation time is covered [36]. The subway passenger flow data of the sample stations were collected for one week (20-26 April 2021). The sources of social development data and economic level data include online public data and field research.
# LogInjector: Detecting Web Application Log Injection Vulnerabilities
## 5 Design
### Dynamic Testing of Feedback-Guided Mutations
#### 5.3.1 Workflow  
We propose Algorithm 1 for dynamic testing of log injection vulnerabilities. The input of the algorithm includes the initial input _init_input_ and the heap tree _heaptree_. LogInjector maintains a fixed-size heap tree to store test cases that mutate well. The output of the algorithm is \(TC_{success}\), which represents the set of successful test cases. First, LogInjector adds the initial input to the heap tree and then takes the first element of the heap tree as \(TC_{current}\), where \(TC_{current}\) represents the current test case (lines 2-3). Second, LogInjector sends \(TC_{current}\) and then calculates the score of \(TC_{current}\) based on the feedback information  
Figure 2: HTML code of viewing error log web interface page of PHPFusion.  
and verifies whether the vulnerability is triggered (lines 7-9). If the vulnerability is triggered, the successful test case will be returned directly (line 26). If the vulnerability is not triggered, LogInjector compares the scores of \(TC_{current}\) with the parent test case (line 13). If \(TC_{current}\) is better than the parent test case, LogInjector will add \(TC_{current}\) to the heap tree (line 14). Otherwise, LogInjector will reduce the score of the parent test case by 0.1 (line 16). In order to avoid repeated mutation of poor test cases, we reduce the score of test cases that did not achieve better results after mutation by 0.1. Then, LogInjector will adjust the heap tree (line 18). In line 20, if the number of elements in the heap tree is larger than the fixed size we defined, LogInjector removes \(TC_{least}\), where \(TC_{least}\) represents the test case with the lowest score, from the heap tree.  
Then, LogInjector takes the test case with the highest score from the heap tree and mutates it randomly as \(TC_{current}\). Finally, LogInjector repeats the above process until a vulnerability is successfully detected or the number of tests reaches a threshold to stop.  
```
1:\(init\_input\),\(heaptree\)
2:\(TC_{success}=\{\}\)
3:add init_input into heaptree
4:\(TC_{current}\) = heaptree[0]
5:heatree[0].score = 0
6:success = False
7:whilenot success or max tries not reacheddo
8:feedback, response = SendRequest(\(TC_{current}\))
9:\(TC_{current}\)-score = compute(feedback)
10:if Vulexist(responce) then
11:\(TC_{success}=TC_{success}\cup TC_{current}\)
12:success = True
13:else
14:if\(TC_{current}\)-score > heaptree[0].score then
15: add \(TC_{current}\) into heaptree
16:else
17: heaptree[0].score = 0.1
18:endif
19: heapify(heatree)
20:if len(heatree) > threshold then
21:\(TC_{least}\) = Least(heatree)
22: remove \(TC_{least}\) from heaptree
23:endif
24:\(TC_{current}\) = RandomMutate(heatree[0])
25:endif
26:endwhile
27:return\(TC_{success}\)
```  
**Algorithm 1** Dynamic Testing Algorithm Based on Feedback-Guided Mutation.
# 2-Methoxy-4-Vinylphenol as a Biobased Monomer Precursor for Thermoplastics and Thermoset Polymers
## 3 Results
### Thermoplastic Applications
The molecular weight (M\({}_{\text{n}}\)) of the homopolymers was characterized by size-exclusion chromatography (SEC) with dimethyl formamide (DMF) as the eluent (see Table 1). The thermal properties of the precipitated homopolymers were investigated by DSC and TGA (Table 1, Figure 1a). All homopolymers exhibited glass transition temperatures (T\({}_{\text{g}}\) s) lower than polystyrene (PS). This may have been due to the increased mobility of the substituting group, which would have increased the free volume [44]. Additionally, the thermal degradation of the homopolymers under nitrogen atmosphere, expressed by \(T_{\text{deg}}\), showed a fast degradation in the case of MVP-AcOH (around 100 \({}^{\circ}\)C), which could be attributed to the cleavage of the -COOH group (corresponding to approximately 20 wt.%), as shown in the literature for the case of methacrylic acid (MAA) (Figure 1a and Figure S19a) [45]. To demonstrate the radical polymerization of the MVP-platform monomers in water and reduce the fossil-based content in commercial plastics, without compromising their properties, the MVP-derived monomers were copolymerized with styrene via emulsion polymerization. It was hypothesized that MVP, due to its unprotected phenol, would act as a radical scavenger during radical polymerization; thus, only the protected MVP monomers were considered [36]. It was found that, when the biobased content exceeded 10 wt.%, the resulting emulsions were unstable. Furthermore, MVP-AcOMe and MVP-Bz were miscible with styrene, whereas MVP-AcOH could dissolve in neither styrene nor water, despite the presence of a carboxylic acid, which could be attributed to the pH being slightly acidic (pH 4). Thus, a third comonomer had to be used, i.e., MVP-AcOMe. Each copolymer was named according to the targeted styrene and MVP-AcOMe, MVP-AcOH, or MVP-Bz content in subscript. After 3 h of polymerization, emulsions with high monomer conversions were obtained (Table 1 and Figure S17). The hydrodynamic diameters (D\({}_{\mathrm{H}}\)) recorded by dynamic light scattering (DLS) were similar for all samples except for P(S\({}_{90}\)-MVP-AcOMe\({}_{5}\)-MVP-AcOH\({}_{5}\)), which was higher (Table S2). This can be explained when considering the polydispersity index (PdI) and the lower monomer conversion achieved compared to the remaining emulsions. It was observed that both P(S\({}_{90}\)-MVP-AcOMe\({}_{10}\)) and P(S\({}_{90}\)-MVP-Bz\({}_{10}\)) were monodispersed, whereas P(S\({}_{90}\)-MVP-AcOMe\({}_{5}\)-MVP-AcOH\({}_{5}\)) showed a range of different sizes (Table S3). This may have been due to the poor stability and complexity of the system, being the result of three different comonomers with competing reactivities. The thermal properties of the copolymers are summarized in Table 1. It can be seen that the emulsion copolymers exhibited \(T_{\mathrm{g}}\) values very close to PS, as also verified by
## 2 FPHs for Human Functional Benefits
### FPH with Anti-Diabetes Type 2 (T2D) and Satiety Activities  
FPH may contain peptides that can affect the progression or prevent T2D. Peptides from FPH can stimulate glucagon-like peptide-1 (GLP-1) secretions and enhance insulin release from pancreatic cells and may also inhibit enzymes like dipeptidyl peptidase-IV activity (DPP-IV), resulting in an increase in glucose uptake and tolerance, a reduction in blood glucose concentration, and the up-regulation of GLUT4 [34]. [PERSON] and colleagues [35] recently collated a review on fish and milk derived peptides from hydrolyses and their impact on T2D development [35]. A review of the literature concerning FPH and in vivo studies came to the conclusion that even though there is methodological variation between several studies performed to date, there is significant potential for the application of FPH to control hyperglycaemia and weight [35]. A study by [PERSON] and colleagues demonstrated both in vitro and in vivo evidence for a satiating effect of FPH obtained from blue whiting (_Micromessitis puttassou_) muscle [36]. Several in vivo studies with FPH looking at the anti-hyperglycaemic effect of FPH have shown a reduction in blood glucose with doses of FPH as low as 50 mg kg-1 bodyweight [37].  
Furthermore, all in vivo studies investigating the satiating effect of FPH have shown a decrease in bodyweight or a reduction in weight gain [38]. Antidiabetic peptides mainly inhibit dipeptidyl peptidase-IV (DPP-IV) activity. Inhibitors of DPP-IV can cleave nectins like glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GDP). [PERSON] and colleagues generated the peptide GPAE from salmon gelatine previously and this peptide was shown to have considerable inhibitory effects on DPP-IV [39]. DPP-IV inhibitory peptides were also identified from discarded _Sardine pilchardus_ protein previously [39; 40].
###### Abstract  
A Substitution box (S-box) is an important component used in symmetric key cryptosystems to satisfy Shannon's property on confusion. As the only nonlinear operation, the S-box must be cryptographically strong to thwart any cryptanalysis tools on cryptosystems. Generally, the S-boxes can be constructed using any of the following approaches: the random search approach, heuristic/evolutionary approach or mathematical approach. However, the current S-box construction has some drawbacks, such as low cryptographic properties for the random search approach and the fact that it is hard to develop mathematical functions that can be used to construct a cryptographically strong S-box. In this paper, we explore the non-permutation function that was generated from the binomial operation of the power function to construct a cryptographically strong S-box. By adopting the method called the _Redundancy Removal Algorithm_, we propose some enhancement in the algorithm such that the desired result can be obtained. The analytical results of our experiment indicate that all criteria such as bijective, nonlinearity, differential uniformity, algebraic degree and linear approximation are found to hold in the obtained S-boxes. Our proposed S-box also surpassed several bijective S-boxes available in the literature in terms of cryptographic properties.  
s-box; cryptographically strong s-box; binomial power function; non-permutation function; redundancy removal algorithm  
11T71; 94A60; 68P25
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 5 Performance and Evaluation
### Potential Application  
The above evaluation demonstrated that CoFi could provide a much higher network throughput than the widely adopted 802.11 MAC protocol, especially when one or more clients in the WLAN are experiencing harsh channel conditions. However, CoFi does not guarantee low per-packet delay for all cases. Therefore, it is best applicable to bulk file trans- missions and streaming protocols, which adopts buffers to tolerate an initial setup delay. In addition, when the custom VLSI of RLNC is implemented and the computing power of CPU improved, it is possible to apply random linear coding on handheld mobile devices and standalone wireless nodes [35]. There can be a wide variety of form for clients, such as the sensor nodes in IoTs, which greatly expands its range of application.
## 4 Modeling Ball/Pad Impact
### Finite Element Model Description  
Explicit 3D finite elements simulations using the commercial software ABAQUS(r)[26] were pursued to model the impact experiments. With this modeling strategy, the geometry/architecture, the materials intrinsic behavior and the contact properties of the mechanical problem were considered.  
The 3D model is presented in Figure 7a. The ball comprises 5058 S4R shell elements with five integration points through the thickness and the polymeric layers is made of 221,598 C3D8R elements: 100,314 elements for the foam layer, 26,028 for the periodic array of perfectly cylindrical pimples and 95,256 elements for the dense top layer of the compact. This distinction has been made since these three layers were modelled as different instances that were bonded together when needed, using the tie function between the shared surfaces as evidenced in the inset of Figure 7b.  
Figure 7: Finite element modeling (FEM). (**a**) 3D model and associated meshes. (**b**) Details on the _foam + compact_ modeling.  
The nodes of the foam bottom free surface were fixed to replicate its gluing onto the wooden rigid frame. At time t = 0 the ball was positioned 0.3 mm away from the top surface of the target. In order to ensure its rigid motion, the initial velocity was assigned to every node M of the ball and was calculated using the following transport equation:  
\[\overset{\rightarrow}{\mathrm{V}}_{\mathrm{M}}=\overset{\rightarrow}{ \mathrm{V}}_{\mathrm{C}}+\overset{\rightarrow}{\mathrm{\omega}}_{\mathrm{C}} \wedge\overset{\rightarrow}{\mathrm{CM}}, \tag{3}\]  
with \(\overset{\rightarrow}{\mathrm{V}}_{\mathrm{C}}\) and \(\overset{\rightarrow}{\mathrm{\omega}}_{\mathrm{C}}\) the linear and angular velocities of the ball center C. The velocity was applied for a short duration (10\({}^{-6}\) s) and the ball was then let free to move (the drag force and gravity were not implemented). Also, constant internal pressure equal to the atmospheric pressure was prescribed as justified elsewhere [8].  
The contact between the two bodies was defined _rough_ if not otherwise specified. More precisely, the rough condition implies an infinite coefficient of friction (total adhesion), suggesting that all relative sliding motion between two contacting surfaces is prevented [27]. It is worth mentioning that this hypothesis was used in [22] to successfully predict the linear coefficient of restitution of the various polymeric targets normally impacted. This hypothesis is strongly challenged and discussed with this new set of data.
# Adaptive Emergency Call Service for Disaster Management
## 2 Related Works
### 3 GPP Works on Public Safety  
Two main approaches in _Mission Critical services_ were introduced by the 3 GPP group concerning proximity-based services in 4G/5G mobile networks; extending the gNBs coverage by using the D2D [11; 12] and supporting the group calling communication, also referred as push to talk (PTT) service [4; 13]. PTT is described in TS 22.179 of the 3 GPP standard [14; 15]. Although the PTT and D2D mechanisms present a way to extend the network coverage and services; these technologies are conceived for well-deployed local mobile networks. An extended networking protocol using such mechanisms is required for implementing an emergency call service under extreme situations, i.e., a high number of emergency calls, the partial failure of the network, and a large affected geographical area, to reliably forward calls to the core network.