esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Application and Results
### Factors of Change Maps for Thermal Actions  
Thermal actions on buildings, bridges, and other structures in Europe are given in EN1991-1-5 [11]. Temperature variation within an individual structural element is split into four elementary components (uniform temperature, linear horizontal differential, linear vertical differential, and non-linear differential), as illustrated in Figure 1 of Eurocode EN1991-1-5 [10]. These components are functions of the already defined characteristic values of maximum and minimum shade air temperatures, \(T_{\max,k}\) and \(T_{\min,k}\), at the construction site, given in the National Annex.  
Using the proposed approach, the factors of change for \(T_{\max,k}\) and \(T_{\min,k}\) have been derived for the RCP4.5 and the RCP8.5 emission scenarios in terms of difference, according to Equation (4).  
Considering Italy and the time windows 2011-2050, 2031-2070, and 2051-2090, the factor of change maps for \(T_{\max,k}\) and \(T_{\min,k}\) obtained are presented in Figures 7 and 8, respectively.  
The maps show a general increasing trend in line with the expectations about global warming, but variations of characteristic values are much more significant than variations of mean temperature values.  
Actually, in Italy, in the long-term future (2051-2090), increases up to 5.5 \({}^{\circ}\)C may be expected for maximum shade air temperature and up to 6 \({}^{\circ}\)C for minimum shade air temperature.  
In Figures 9 and 10, they are illustrated as the maps elaborated for Germany, which lead to similar conclusions to those already reported for Italy.  
In this case, the percentage of the region affected by the highest variations (\(\Delta T>5.5\)\({}^{\circ}\)C) is much more evident for minimum temperature (54.9% and 96.5% according to the RCP4.5 and RCP8.5, respectively).  
Figure 6: Characteristic wind speed \(v_{\max,k}\) changes in Germany referring to 1951–1990. RCP4.5 (1 st row). RCP8.5 (2 nd row).  
Figure 8: Characteristic minimum temperature \(T_{\rm min,\it k}\) changes in Italy with respect to 1951–1990. RCP4.5 (1 st row). RCP8.5 (2 nd row).  
Figure 7: Characteristic maximum temperature \(T_{\rm max,\it k}\) changes in Italy referring to 1951–1990. RCP4.5 (1 st row). RCP8.5 (2 nd row).  
Figure 10: Characteristic minimum temperature \(T_{\rm min,\lambda}\) changes in Germany referring to 1951–1990. RCP4.5 (1 st row). RCP8.5 (2 nd row).  
Figure 9: Characteristic maximum temperature \(T_{\rm max,\lambda}\) changes in Germany referring to 1951–1990. RCP4.5 (1 st row). RCP8.5 (2 nd row).
## 4 Discussion
### Performance of Partial Correlation Coefficient on Different Vegetation Types  
Researchers have shown that the connection between vegetation growth and climate change in the TP region is complex and highly variable. The TP is primarily a high-altitude cold ecosystem where vegetation growth is mostly constrained by the temperature and precipitation [59; 60; 61; 62; 63]. Many scholars have conducted research on the ecosystem of the Tibetan Plateau. Studies have shown that drought has a lagging and cumulative effect on the alpine grasslands of the plateau. Specifically, the response of the alpine meadows to drought is primarily influenced by precipitation, while the response of the alpine grasslands is mainly influenced by the temperature [64]. Additional studies have shown that drought intensity on the Tibetan Plateau impacts the vegetation distribution, with sparse vegetation being the most affected, followed by meadows and grasslands [65]. Other studies have indicated that global warming has intensified the drought risk on the Tibetan Plateau, highlighting that surface temperature is the dominant factor influencing the degree of the vegetation response to drought [28].  
With global changes, a good deal of attention has been given to high-altitude meadows and alpine grasslands, while high-altitude sparse vegetation and shrubs have received comparatively less attention. However, it is essential to emphasize that the study of the phenological events in the various vegetation categories on the TP has equal importance [38]. Therefore, we conducted a statistical analysis of these four vegetation types. As depicted in Figure 5, it is evident that the correlation between the vegetation peak values (NDVI\({}_{\text{max}}\), GPP\({}_{\text{max}}\)) and SM and VPD varies among the vegetation types. This illustrates the differences in the ecosystem variations and their responses to driving factors across distinct vegetation categories [38]. The most pronounced response, however, occurs in the alpine meadows, where the vegetation peak values are primarily positively correlated with SM and negatively associated with VPD. This could be a result of the effects of precipitation and temperature on them. The principal grassland variety found on the TP is the alpine meadow [66; 67], which has a predominantly cold and humid climate [68]. Many scholars contend that temperature is the influencing factor [66; 69; 70; 71]. However, some researchers argue that precipitation is its main influencing factor [66; 72], and others suggest that it is mostly influenced by spring solar radiation and summer temperatures [66; 73]. As shown in Figure 6, the correlations between the vegetation peak values (NDVI\({}_{\text{max}}\) and GPP\({}_{\text{max}}\)) and SM and VPD exhibited more distinct patterns in temperatures ranging from \(-10\)\({}^{\circ}\)C to \(10\)\({}^{\circ}\)C and precipitation levels between 200 and 800 mm. This further underscores the critical importance of precipitation and temperature influences.
## 4 Experimental Evaluation
### Measurement Procedure  
The neSC applications used in the both experiments were deployed in an IRIS (the datasheet of the IRIS note is available at [[http://www.memisic.com/products.html](http://www.memisic.com/products.html)]([http://www.memisic.com/products.html](http://www.memisic.com/products.html))) mote, connected to a MTS400 basic environment sensor board. These applications were compiled by neSC version 1.3.1, built on top of the TinyOS 2.1.1. An oscilloscope (Agilent DSO03202A) was used to measure the power consumption of the mote executing the applications. A PC was connected to the oscilloscope that captures the code snippet execution start and end times by monitoring a LED of the mote, which is turned on/off to signal the execution start/end. The PC runs a tool named AMALGHMA[28] that was responsible for calculating the power consumption.  
In order to measure the power consumption of each neSC operator (see Section 2.2.1) and built-in neSC commands available in TinyOS (e.g., send, receive, read), it was created a generic application as shown in the following:  
```
1 implemention{
2
3 eventvoidBoot.booted({
4 callTimer.startPeriodic(100);
5}
6}
7
8 eventvoidTimer.fired(){ //startpowermeasurement
9 operator; //firsttime
10 operator; //secondtime
11...
12 operator; //N - 1 time
13 operator; //N time
14} //stoppowermeasurement
15}
```  
To reduce the interference in the measurement process, this application does not enable any mote resource (e.g., radio and LED) and repeats the measuring until the average value achieves a specified error. The power consumption of a particular operator (operator) is obtained by dividing the obtained measurement by the number of repetitions.  
A similar approach was used to assess the power consumption of the mote's resources (e.g., radio and LEDs). However, in this case a simple application was executed with and without the particular resource active (on). The difference between both power consumption measurements is the resource's power consumption.
# Asymmetric Propagation Delay-Aware TDMA MAC Protocol for Mobile Underwater Acoustic Sensor Networks
## The APD-TDMA
### Transmission Phase
Considering that the flight time of the \((j+1)\)th beacon packet to UN\({}_{i}\) is \(\delta_{i}(T_{CYC}^{j})+\tau_{B}\), the position of UN\({}_{i}\) at the time of \(T_{Brx,i}^{j+1}\) can be computed by  
\[X_{i}(T_{Brx,i}^{j+1})=X_{i}(T_{CYC}^{j})+V_{i}^{j}\cdot(\delta_{i}(T_{CYC}^{ j})+\tau_{B}) \tag{9}\]  
in which \(T_{Brx,i}^{j+1}=T_{CYC}^{j}+\delta_{i}(T_{CYC}^{j})+\tau_{B}\), which means the arrival time of the \((j+1)\)th beacon packet at UN\({}_{i}\). The downlink propagation delay of UN\({}_{i}\), \(\delta_{i}(T_{Brx,i}^{j+1})\), can be obtained from Equations (7) and (9). Note that \(X_{i}^{j+1}=X_{i}(T_{Brx,i}^{j+1})\) and \(X_{i}^{1}\) are obtained from the information of the initialization phase. If the SN completes the calculation of \(\delta_{i}(T_{Brx,i}^{j+1})\) for all UNs, the transmission order of UNs is defined in ascending order of \(\delta_{i}(T_{Brx,i}^{j+1})\). We call this transmission order a static order (SO). The SO is optimum in a static topology in terms of the channel utilization, as shown in [40]. However, it should be noted that the optimality of the SO may no longer be valid in a mobile scenario, because the propagation delay may be changed within a cycle. In other words, the ascending order of propagation delays at a certain time during the cycle may be different with the SO due to mobility. In this dynamic topology, the SO is not optimum, but still the SO can guarantee near-optimal performance. We will justify the use of the SO through a simulation result that shows that the performance degradation due to the SO is negligible.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 9 Conclusions  
In this paper, we have proposed an efficient routing protocol, _i.e._, 3D-SM for UWSNs. It is implemented on a linear network. To minimize the energy consumption of nodes, the whole field is logically divided into RCs, and also, the MS and CNs are deployed. The mobility of the MS and CNs increases the NLT and also minimizes delays. In order to validate our scheme, we compared it to selected existing schemes. Simulation results show that 3D-SM performs better than DDRP underwater and AUV-PN in terms of NLT, throughput, delay, path loss and scalability. Furthermore, we presented a scalability analysis that shows that 3D-SM is scalable. In the future, we will vary the network dimension and also vary the number of MSs and CNs for presenting a realistic approach towards scalability.  
The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for funding this research through Research Group Project No. RG#1435-051.  
[PERSON] and [PERSON] proposed main idea. [PERSON] and [PERSON] performed simulations. [PERSON] and [PERSON] performed mathematical modeling. [PERSON] and [PERSON] provided overall supervision and paper's write-up and refinement.  
The authors declare no conflict of interest.
# DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things
## 2 Related Works
### TMS Based on Dirichlet Distribution  
[PERSON] et al. proposed Dirichlet-based trust management to measure the level of trust in Intrusion DetectionSystems (IDS) according to their mutual experience [13]. Meanwhile, an acquaintance management algorithm was proposed to allow each IDS to manage its acquaintances according to their trustworthiness. Their approach achieved scalability properties and was robust against common internal threats, resulting in an effective the CollaborativeIntrusion Detection Network. [PERSON] and [PERSON] put forward a Dirichlet Distribution based Model (DDTM) to detect malicious attacks in WSN [14]. This model that included the monitoring module and trust value module used trinomial Dirichlet distribution for trust evaluation, and used Dirichlet fusion rule to combine the opinions gathered from neighbor nodes to defend against the bad mouthing attacks and ballot attacks. They also designed a penalty scheme and a dynamic sliding window scheme to find attacks quikly and provided malicious behavior feed back to the routing model for secure data transmission. [PERSON] et al. proposed a new Dirichlet based trust management system for the IoT (DTMS-IoT) [15]. This system mitigated both on-off attacks and dishonest recommendations by detecting nodes malicious behavior. They used service levels and things capacities to reinforce security, and the computation of trust valuewas based on direct observations and recommendations.  
In addition, [PERSON] et al. proposed a Dirichletbaseddetection scheme to address such opportunistic attacks(DDOA) [16]. In this scheme, a Dirichlet-based probabilisticmodel was built to assess the reputation levels of local agents. Initial reputationlevels of the local agents were first trained using the proposed model, based on their historical operating observations. An adaptivelyetection algorithm with reputation incentive mechanism was thenemployed to detect opportunistic attackers. [PERSON] presented a reputation computing framework based on Dirichlet distribution in WSN [17]. The proposed framework involved the detection module and reputation module. The detection module determined each node's outlier degree using the Local Outlier Factor (LOF) algorithms. The reputation module included obtaining the reputation information of its neighbor nodes, establishing the reputation function based on Dirichlet distribution.
## 3 Results
### Compressive Strength  
Figure 7a,b show the results of compressive strength tests depending on the content of waste glass and PET.  
Regardless of the type of waste material incorporated, the compressive strength of mortars diminished after 28 days of curing, with the most significant decreases in strength exhibited by mortars where 20% glass powder content partially replaced the cement. This is likely attributable to the limited capacity of the powder to react with Ca(OH)\({}_{2}\) within the given timeframe. The decline in strength of mortars, where 10% of the binder is substituted by powder, is 11%, while the introduction of 20% powder results in a 25% decrease in strength. This augmented reduction in the strength parameter could be associated with the agglomeration of powder particles within the slurry matrix. The incorporation of PET ameliorates the adverse effects of glass powder on strength. After 56 days of maturation, the strength of mortars with 20% glass powder content and those with a combination of 20% glass powder and 10% PET content, exhibited augmentation in comparison to the values noted after 28 days. The strength values of the PET mortars after 28 and 56 days exhibited similar characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mixture ID**} & \multicolumn{3}{c}{**Results**} \\ \cline{2-4}  & **Density [kg/m\({}^{3}\)]** & **Air Content [\%]** & **Flow [mm]** \\ \hline
0S-0 PET & 2210 & 4.00 & 153 \\
10S-0 PET & 2198 & 4.05 & 155 \\
20S-0 PET & 2201 & 3.50 & 159 \\
0S-5 PET & 2169 & 4.30 & 162 \\
10S-5 PET & 2173 & 3.60 & 164 \\
20S-5 PET & 2155 & 4.00 & 170 \\
0S-10 PET & 2129 & 4.50 & 174 \\
10S-10 PET & 2129 & 4.00 & 171 \\
20S-10 PET & 2133 & 3.40 & 174 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of fresh mortar properties.  
A visual examination of the mortar samples subjected to mechanical testing revealed a weakened interaction zone between the polymer aggregate grains and the cement slurry (Figure 8). This aspect could be a contributing factor to the decreased flexural and compressive strengths of the tested composites embedded with recycled materials. The weak bond between the PET aggregate and the cement matrix is the main problem of combining the two materials. Unlike the natural aggregate, the smooth and hydrophobic surface of polyethylene terephthalate impedes the optimal binder hydration around the grains and hampers the formation of a sufficiently durable bond between the two materials.  
Figure 6: Flexural strength of mortars after (**a**) 28 days of curing and (**b**) 56 days of curing.
## 2 Related Work  
### Traditional NTC Methods  
In the early days of NTC development, to solve the problem of traffic classification, different TCP or UDP protocol ports were used to determine the type of traffic, such as those specified by the Internet Assigned Numbers Authority (IAIA) [12] or those widely used by the conventions [13]. However, today more and more traffic cannot be classified by this scheme. In practical applications, the network ports of servers do not always use the ports nominally associated with their applications, and such behavior is not always malicious. For example, users running Web or file transfer protocol (FTP) servers on alternate ports lack administrator privileges. Users running servers that provide nonweb applications on port 80/TCP bypass the firewall of the system to avoid security and policy implementation monitoring [14]. [PERSON] et al. [15] compared three methods to classify peer-to-peer (P2P) applications: port-based classification, application layer signature, and transport layer analysis. Their experiments showed that nearly 70% of Internet traffic could not be identified simply by port-based methods. DPI technology mainly analyzes the payload of data packets in the network traffic. If the payload part can match with the known application program or protocol in some characters, then the network flow can be roughly considered as the known application program or protocol. The ML-based method uses an ML algorithm to classify network traffic [16]. Byte sequences and statistical features of packets or flows in traffic data are the most common features used by ML-based network flow classifiers. ML-based methods rely on statistical methods to extract features from datasets, but network traffic data are usually large, and statistical extraction methods are often prone to lose important information.
## 5 Conclusions  
This paper presents an adaptive surrogate-assisted particle swarm optimization (AS-APSO), and it enables the dynamic calibration of switching conditions between the localized and globalized surrogate models with a normal distribution adaptive technique. ASAPSO is guided towards optimal solution spaces by the amalgamated surrogate model and an uncertainty resolution mechanism. ASAPSO outperforms other PSO paradigms and SAEAs in identifying viable solutions within limited computational resources. The superiority is particularly evident in multi-modal functions, where the improvements are clearly noticeable. However, it is important to note that there are inherent limitations to the algorithm. Specifically, its optimization effectiveness is somewhat reduced when applied to unimodal functions, and the adaptive parameter strategy is less effective in scenarios with lower dimensionality. These findings lead to two important considerations. Multi-modal conundrums are characterized by an abundance of local optima. Although our surrogate model can achieve partial optimization by switching modalities as explained here, navigating towards the global optimum remains a complex task. Secondly, the limited number of local optima in unimodal functions, compared to their multi-modal counterparts, suggests that our adaptive parameter strategy may not demonstrate the same level of resilience when faced with the former types of functions.  
Writing and editing, S.Q; software and methodology, F.L.; supervision, Z.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by the National Foreign Expert Program of the Ministry of Science and Technology (Grant No. G2023041037L) and the Shaanxi Natural Science Basic Research Project (Grant No. 2024)C-YBMS-502).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
All authors declare no conflict of interest. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# MADDPG-Based Offloading Strategy for Timing-Dependent Tasks in Edge Computing
## 5 Experimental Results and Discussion
### Parameter Settings
As shown in Figure 8, the MADDPG_DTO algorithm and the UBP_DTO algorithm based on MADDPG show good performances in scenarios in which the computing resources dynamically change. According to the offloading delay results, the UBP_DTO algorithm based on user behaviour prediction outperforms the MADDPG_DTO algorithm in terms of offloading delay by nearly 6.7% and is higher than other benchmark algorithms. Meanwhile, Figure 9 demonstrates the performance comparison between the proposed algorithm and five other benchmark algorithms in terms of resource utilization under different physical locations, even though the proposed algorithm does not perform optimally locally. However, the proposed UBP_DTO algorithm maintains a smooth resource utilization in the vast majority of cases and performs optimally on average, with an improvement of 30.6% compared to the suboptimal algorithm, when observed from the perspective of comprehensive global resource utilization. This is due to the fact that the agent considers the strategies of other agents on the basis of historical offloading decisions and task attribute information, which maximizes the accuracy of the offloading action from the perspective of the global offloading interests of all agents and effectively utilizes the global computing resources. Figure 8: Comparison of agent offloading delay in different physical locations. Figure 7: Convergence results of different reinforcement learning algorithms.