esa-sceva/satcom-chunk-collection huggingface dataset:
# Fire-Induced Changes in Soil and Implications on Soil Sorption Capacity and Remediation Methods
## 4 Fire-Induced Changes in Soil and Choice of Soil Remediation Technologies
### Chemical Remediation Methods  
#### Chemical Leaching  
Chemical leaching uses fresh water, reagents and other fluids and or gases to wash contaminated soil in order to leach out contaminants [140; 141]. Fire-affected soils are usually hydrophobic because of the transformation of functional groups in soil organic matter. This may render leaching of contaminants in fire-affected soils with water ineffective. Chemical leachates used in soil washing could be inorganic eluent, chelation agents, and surfactant, which may include hydrogen fluoride, phosphoric acid, sulfuric acid, hydrogen chloride, nitric acid, ethylenediaminetetraacetic acid (EDTA) acetone, hexane, chlorinated solvents, triethylamine, liquefied propane, and supercritical carbon dioxide. The specific chemical used for leaching varies with contaminant and hydrophobicity of the soil. The contaminants are transferred from soil solid to the liquid phase (leachate), using either adsorption, chelation, ion exchange or precipitation from where they could be recovered, or the leachate treated as hazardous wastewater. These chemical processes require specific conditions for them to be successful. The efficiency of soil remediation using chemical leaching varies. Removal efficiencies of 70%, 30%, 25% and 10% have been reported for Cd, Zn, Cr and Pb in contaminated unburnt soils [142]. In fire-affected soils, contaminants bound to the oxidizable phase (organic matter) of the soil are liberated as a result of the transformation of organic matter by heat from the fire. The amount of bioavailable contaminants in fire-affected soil could be high, making chemical leaching a possible remediation technology for such soils. However, the choice of leaching solution plays a major role in the use of this method in fire-affected soils. In studies by [PERSON] et al. [143], chemical leaching has been reported to reduce Cd concentration in soils by up to 85% using FeCl\({}_{3}\). Phosphoric acid has been identified as the most promising extractant of heavy metals in contaminated soil with leaching efficiencies of up to 99.9% in arsenic contaminated soils [143].
## 3 Results
### Summary Statistics of Excess Fluid in MR Lung Density Image: CF vs. Control
#### 3.1.1 Mean Pixel Area of Excess Fluid Collections  
The mean connected component area in each of the 18 ROIs (9 in the right lung, 9 in the right) has been evaluated to investigate if CF subjects have larger collections of excess fluid, compared to healthy control subjects. The median was not chosen for investigation because we aim to extract the extreme behaviors occurring within the CF population. Figure 5 shows the mean connected component pixel area across all 18 lung ROIs in the right and left lungs for individuals with different CF severity levels and healthy controls. We found a statistically significant increase in the mean connected component area for CF subjects from that of healthy controls in 10 out of the 18 regions (\(p<0.05\), red asterisks in Figure 5).
## 3 Experiments  
### Specimen Preparation and Experimental Procedures  
To analyze the influence of the surface roughness on the measurement of the ultrasonic nonlinearity parameter, Al 6061-T6 and SUS 304 specimens with dimensions of \(100\times 40\times 200\) were prepared. The reference specimen with a 0.5-\(\upmu\)m surface roughness was prepared by a milling process, and both the surfaces of the Al 6061-T6 and SUS 304 specimens were polished using an orbital sander (Keyang, FS-3C, Seoul, Korea) to achieve different surface roughness of 0.5-2.9 \(\upmu\)m. Then, the roughness, Ra, was measured using a surface roughness tester ([PERSON], SJ-210, Kanagawa, Japan). It was not easy to match the roughness of Al 6061-T6 and SUS 304 because these two materials have different hardness. Nevertheless, the specimens were polished to have similar roughness ranges. Then, Al 6061-T6 specimens with one rough surface and one smooth surface were prepared via a milling process.  
After preparing the specimens, the nonlinear measurements were conducted. Figure 1a shows the case where the transmission and reception surfaces have the same roughness. Figure 1b shows the case where either the transmission or reception surface is rough (Ra = 0.5 \(\upmu\)m); first, the transmitting transducer was placed on a rough surface and the receiving transducer was placed on a smooth surface to analyze the influence of the surface roughness on the transmission surface. Next, the positions of the transmitting and receiving transducers were interchanged to analyze the influence of the reception surface on the measurements. There is a difference between the amplitude of the ultrasonic wave incident on the material from the transmitting transducer and the amplitude of the ultrasonic wave incident on the receiving transducer from the material. This difference in amplitude is expected to cause a difference in the interaction with the rough surface, so the experiment was divided into two cases. The absolute and relative nonlinearity were measured three in both cases.
## 4 Simulation Results  
In this section, we evaluate the performance of the proposed protocol using MATLAB in terms of lifetime, throughput and stability of the network. The simultated WSN is composed of heterogeneous sensor nodes with different energy levels and transmission ranges.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 12 Joint Routing and Scheduling in WSNs with Multiple Sinks having Different Location Possibilities
### Important Results  
For a network of four sensor non-sink nodes, one sink node and two possible locations for the sink, the problem instance was solved to optimality in less than one second taking less than six column generation iterations. For larger problems the computational time is unknown according to the authors in [7]. The experiments also showed that the mobility of sinks across possible locations can dramatically extend the network lifetime.
## 2 Materials and Methods
### Obstacle Cost Map Construction
#### 2.2.2 Binary Map Construction  
Binary map construction is the preprocessing work for Obstacle Cost Map construction. Firstly, Compare whether the slope angle \(\beta\) of adjacent points \(p_{k}\big{(}x_{p_{k}},y_{p_{k}}\big{)}\) and \(p_{k+1}\big{(}x_{p_{k+1}},y_{p_{k+1}}\big{)}\) in \(F_{p}\) greater  
Figure 3: Schematic diagram of feature point extraction. In (**a**), the red dots are the extreme points on the section scanning line. In (**b**), the red marks are the feature points filtered according to the threshold \(T\), and only the points with large elevation change are retained. The x-axis, which depends on the size of the point set represents the length of the scanline; the y-axis represents the elevation value of the point on the scanline.  
than \(\theta\) sequentially. If so, all the corresponding coordinate points between \(p_{k}\) and \(p_{k+1}\) are assigned as 1, otherwise, it is 0, i.e., the value of one is represented obstacle. As shown in Equations (3) and (4). Figure 4 is the schematic diagram of the four-direction scanning binary map.  
\[\beta=tan^{-1}\bigg{(}\frac{y_{p_{k+1}}-y_{p_{k}}}{x_{p_{k}}-x_{p_{k}}}\bigg{)}, \tag{3}\]  
\[p_{j}=\left\{\begin{array}{ll}1,\beta\geq\theta\\ 0,\beta<\theta\end{array},j=1,2\ldots m,\right. \tag{4}\]  
where \(p_{j}\in E_{i}\).  
Secondly, fuse four scanning binary maps to form the final binary map in a simple mixture method: Traverse all points of the four maps. If the accumulated value of the same coordinates in four maps is greater than or equal to 2, then the value of the coordinate point is set to 1. Otherwise, it is 0. The final obstacle binary map is shown in Figure 5.  
Figure 4: Schematic diagram of four direction scanning binary map. (**a**) is the vertical scanning map; (**b**) is the horizontal scanning map; (**c**) is the 45\({}^{\circ}\) left scanning map; (**d**) is the 45\({}^{\circ}\) right scanning map.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Scalability  
Scalability is the ability of a CR-WSN to adapt to its own expansion while improving efficiency. The CR-WSN is said to be scalable if it accommodates more CR-WS nodes at a later stage, after the design. Clustering in CR-WSNs efficiently deals with problems within a local cluster, and resolves issues over resource limitations. It is easy to keep localized (distributed) spectrum sensing records and update spectrum information, which is the key success factor for communications in CR-WSNs. Clustering also helps to localize the route set up within the particular geolocation, which reduces the size of the routing table stored in the individual sensor nodes. Compared with a flat topology, this kind of network topology is easier to manage, and is more scalable when responding to events in the environment. An important condition for scalability is to keep the average distance between source and destination nodes small enough as the network size grows.  
Figure 4: Hierarchical network topology for a CR-WSN where only the CHs are CR-WS nodes.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 3 The Proposed POI Recommendation Scheme
### System Configuration  
Figure 3 shows the overall system configuration for the proposed scheme. The proposed scheme involves the collection and preprocessing phase, analysis, and recommendation phases. The collection phase involves acquiring information regarding users and visited locations from location-based websites. The preprocessing phase involves generating the necessary data from the collected information by extracting each attribute. The analysis phase involves utilizing the extracted attributes to compute the frequencies of user activities and visits. The activity index derived in this manner is used to divide the user  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Factors** & **Existing Scheme** & **Proposed Scheme** \\ \hline
1. User activity & Considered & Considered \\
2. Time & Considered & Considered \\
3. Space & Considered & Considered \\
4. Activity pattern & Not considered & Considered \\
5. Category similarity & Not considered & Considered \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between existing and proposed schemes.  
group by applying the fuzzy c-means (FCM) clustering algorithm [9]. The database stores the obtained time-based activity patterns. The preprocessed database table is utilized in the recommendation phase. When the user's time, latitude, and longitude data are entered, the proposed system analyzes the user's activity level, generates a POI candidate group that is appropriate for each user, and recommends a POI that is suitable for the user from among the candidate groups.
## 4 Simulations and Analysis
### Simulation Results and Analysis
#### 4.2.2 Snow  
Snow is a typical low adhesion track-ground condition with the largest adhesion coefficient around 0.2. Simulation results of these four strategies conducted under snow are shown in Figure 7, and the values of indicators are listed in Table 3. The abbreviations are the same with braking on a muddy road.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicator** & **FB** & **TABS** & **SMSCM** & **SMSCR** \\ \hline \(T\) (s) & 6.21 & 5.24 & 5.19 & 5.07 \\ \(S\) (m) & 67.64 & 58.45 & 59.24 & 57.16 \\ \(D\) (\%) & 1054.50 & 17.00 & 9.50 & 3.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The value of indicators in the simulations on a muddy road.  
Figure 6: Simulation results of four emergency braking control strategies mentioned in Section 4.1 on muddy ground. They are the curves of vehicle and track speed, slip ratio and braking torque of: (**a**) full braking (FB); (**b**) traditional ABS (TABS); (**c**) sliding mode slip control without motor (SMSCM); (**d**) sliding mode slip control with regenerative braking (SMSCR). VV, TV, HRT, MBT, and MRT are vehicle velocity, track velocity, hydraulic retarder torque, mechanical brake torque, and motor regenerative torque, respectively.  
As shown in Figure 7 and Table 3, the last three braking control strategies still performed much better than full braking. Compared with the results of a muddy road, the deviation of the real slip ratio from the demanded value became larger when using full braking as the time of completely locking took a larger part in the whole braking process. The performances of SMSC strategies whether with the participation of regenerative braking performed by PMSMs or not exceeded the one of TABS significantly when braking on snow, while taking the PMSMs in use with the proposed allocation method remained superior to only using the mechanical brakes and hydraulic retarders. The stopping distance of SMSCR even reduced more than 1.5 m compared to SMSCM, not to mention that all indicators can be improved by more than 10% compared to full braking. What is more, we can find that the proposed allocation method is more effective than the proposed allocation method.  
Figure 7: Simulation results of four emergency braking control strategies mentioned in Section 4.1 on snow. They are the curves of vehicle and track speed, slip ratio, and braking torque of: (**a**) FB; (**b**) TABS; (**c**) SMSCM; (**d**) SMSCR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **FB** & **TABS** & **SMSCM** & **SMSCR** \\ \hline \(T\) (s) & 10.75 & 9.57 & 9.23 & 9.09 \\ \(S\) (m) & 115.50 & 106.15 & 103.31 & 101.74 \\ \(D\) (\%) & 1404.25 & 17.50 & 3.25 & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The value of indicators in the simulations on snow.  
that the value of all the indicators by SMSCM was improved greatly over TABS, which means with the decrease of track-ground adhesion, the control effect of the traditional antilock braking method met a change toward the negative direction, and the significance of an advanced braking control method gradually stood out.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 2 Resistance of Concrete to External Chlorides
### 2.
[99] examined changes in phase assemblage and chloride binding in Portland cement systems blended with between 5 and 55% limestone. Paste samples with a water/solid ratio of 0.5 were cured for 28 days before immersion in 0.5 M NaCl for 1 year. Increasing limestone contents led to decreasing total chloride contents, from 13.52 mg/g in OPC to 8.89 mg/g in the sample prepared with 55% limestone. There was, however, an increase in free chloride content with increasing limestone content and this was attributed by the authors to the greater porosity and permeability enabling greater chloride ingress. However, they also reported a reduction in bound chloride contents with increasing limestone content, both in terms of that adsorbed onto C-S-H and that chemically bound as Friedel's salt. Thus, the increased free chloride content is likely a result of reduced chloride binding capacity. limestone mix at 20% total replacement compared to an equivalent 1:1 fly ash-limestone mix [108]. In addition to the reduction in chloride transport, ternary cements also offer potential for chloride binding. Generally, ternary limestone cements show higher chloride binding potential than limestone cements, but slightly lower than binary cements containing SCMs. The chloride binding capacity is related to the degree of SCM hydration and the aluminum content in the SCM [52]. Thus, at early ages, blends containing metakaolin, slag and natural pozzolans greatly outperform those containing fly ash and silica fume [109; 110]. LC3 systems have generally been found to perform well and show good chloride binding potential, primarily due to the high clay reactivity and aluminum content [111]. Exposure to high chloride concentrations may lead to destabilization of monocarbouluminate [57] with the formation of Friedel's salt. However, in such situations, the chlorides are bound. There have been very few studies of chloride-induced reinforcement corrosion in ternary cement systems, but the results are inconclusive. The addition of up to 20% limestone was found to increase the time taken for the corrosion potential to cross the \(-350\) mV threshold (rendering the steel susceptible to corrosion) [112]. However, others have reported increased corrosion susceptibility in LC3 cements [113]. Densification increases the time taken for chlorides to reach the reinforcement, but the addition of limestone can reduce the pH and so increase chloride susceptibility. In summary, with the growing interest in ternary cements, there is a need for a better understanding of their curability performance.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### DTN Module
#### 3.1.1 DTN Controller  
As shown in Figure 1, the DTN Controller serves as the central processing entity for all incoming or outgoing DTN packets, and is therefore responsible for all interactions between the DTN Module and the custom LwIP. Upon receiving a packet (message 1. receive pck), the DTN Controller analyzes its nature and determines the appropriate handling procedure. If the packet is to be forwarded, it interrogates the Routing Function for a contact (message 2. request contact), which evaluates and reports whether the packet should be stored or forwarded based on the current network conditions and routing logic (message 3. return contact). At the same time, if the packet is identified as signaling traffic, it is also processed directly by the DTN Controller. Depending on the content of the signaling message, a variety of actions may be triggered, ranging from updating routing  
Figure 2: Internal elements of the DTN Module and fundamental interactions between them.  
information to generating new signaling messages or interacting with other functions. This ability to differentiate and appropriately manage user plane and signaling packets ensures the efficient operation of the DTN Module and the overall DTN communication system.
## 3 System Design  
### HAS Architecture  
Figure 3 depicts the proposed architecture for ZiWi, which is a single-home version of the generic home automation architecture shown in Figure 2. As it can be observed, there is a group of gateways that are installed locally on embedded devices, although the system has been designed so that the functionality provided could also be offered through external cloud services. Specifically, such a group of gateways is responsible for the HAS user interface and the communications with the different home automation devices, also providing persistent storage for the data collected from the sensors.  
ZiWi's node communications are exclusively wireless through WiFi and ZigBee transceivers. Nodes are connected either directly to a gateway or to other nodes that forward their data. Every node consists of several sensors and/or different actuators, whose number and type depends on the room where the node is deployed, existing certain common sensors/actuators to all rooms, like the ones related to temperature, humidity, luminosity or the light dimmers.  
It is important to note the different nature of both types of home automation devices present in the architecture: in most home automation systems, sensors tend to be more numerous than actuators and send frequent data updates, while actuators only operate on specific occasions. For this reason, ZigBee is usually preferred for sensors (especially for the ones that depend on batteries), while actuators,which usually have to be kept listening continuously for remote commands, are equipped with WiFi transceivers that allow for receiving direct IP packets from the home controller.  
The WiFi modules and the home controller communicate through the home network using a WiFi router, adding APs (Access Points) as repeaters when necessary. If the WiFi infrastructure is limited and coverage needs to be increased, the architecture would enable using ZigBee routers, which would only require connecting an XBee module to one of the General-Purpose Input/Output (GPIO) ports of the node's embedded microcontroller.
## 2 Results and Discussion
### The Ability of TPE Groups inside PLA-Based Polyurethane to Form Radicals  
Although, according to the above-cited literature, the -C(Ph)\({}_{2}\)-C(Ph)\({}_{2}\)- bond in a benzopinacole molecule can dissociate and initiate radical polymerization, it was not obvious that it could form radicals when present in a benzopinacole moiety (TPE-diET) placed in polylactide-based polyurethane. To check this, electron spin resonance (ESR) spectroscopy measurements were performed. PLA-PU with TPE inside was analyzed in the presence of TEMPO (2,2,6,6-tetramethylpiperidine-1-oxyl) radical able to capture -C(Ph)\({}_{2}\bullet\) radicals formed during heating. Figure 2 presents the ESR spectra recorded at different temperatures and the progress of the decomposition of TPE groups present in PLA-PU polymer with the temperature increase. The higher the temperature, the faster the decomposition of the TPE units (more formed radicals, and, consequently, more trapped radicals). It can be seen that above 75 \({}^{\circ}\)C, the rate of dissociation increases only slightly, and thus a temperature of 85 \({}^{\circ}\)C was selected in order to generate radicals at a sufficient rate while also limiting possible side reactions.  
Figure 2: (**a**) ESR spectra of PLA-PU recorded at different temperatures in the presence of TEMPO. (**b**) Progress of the decomposition of TPE groups in a PLA-PU polymer with an increase in temperature (the relative concentration of radicals was calculated according to the method described in the SI using normalized intensities from ESR spectra, Figure S2, Equations (S1a,b)).