esa-sceva/satcom-chunk-collection huggingface dataset:
# CAL: Core-Aware Lock for the big.LITTLE Multicore Architecture
## 4 Experiment
### Application Benchmarks  
We evaluate LevelDB to demonstrate the practical effectiveness of CAL. LevelDB is a widely adopted high-performance key-value storage database [24]. We use readrandom in LevelDB's built-in db_bench for testing, in which the get operation applies for a global lock to obtain a snapshot of the internal database structure. To conduct throughput and latency evaluation, we made appropriate modifications to the db_bench. When conducting the throughput and latency test, we inserted FLAG_duration to provide a fixed program running time and modified the corresponding readrandom function to adapt to the inserted  
Figure 8: Variation in a variable contention environment. (**a**) Fairness. (**b**) Throughput. (**c**) Tail latency.  
Figure 7: Comparison in a high contention environment. (**a**) Lock fairness. (**b**) Throughput and tail latency.  
FLAGS_duration parameter. We set FLAG_duration to 10 s, and each test was repeated five times to get the average results. In the fairness experiment, we change the number of threads from 1 to 10. We measure the slowdown ratio of each thread performing a million readrandom operations to calculate fairness value.  
The experimental results are shown in Figure 9. In Figure 9a, we observe that all the lock algorithms maintain high fairness when running on the first six big cores. The FIFO-based ticket lock, due to its inherent strict lock order, demonstrates higher fairness compared to ttas, Pthread lock, and other competition-based locks. With the introduction of little cores, the fairness of locks except CAL has significantly declined, with the lowest drop to 0.56 (ttas), while CAL has always maintained high fairness (above 0.9). In Figure 9b, the throughput of CAL is only lower than Pthread lock's, but the tail latency is less than half of Pthread lock's. The ticket lock and cptltkt lock show slightly higher latency than CAL but significantly lower throughput levels. Spinlock has the lowest throughput, which is 53% lower than CAL's throughput. Furthermore, the tail latency of spinlock and ttas are appreciably higher than that of CAL.
## 4 Experimental Results and Discussion
### Computational Efficiency  
The system has a different processing time when processing different sizes of images with different architectures of the feature extractor. Generally, more time is required for image processing when large images or a complex feature extractor, such as ResNet101 [2], is used. Therefore, we must scale the input size of images.  
Table 3 presents the running time for different image sizes and feature extractors. The system ran on a computer with an Intel Core i7-7700K 4.20-GHz CPU, an NVIDIA GeForce GTX 1080 Ti GPU, and 24 GB of DDR4 RAM. The operating system was Ubuntu 16.04.4 ITS 64 bit. The model was built using TensorFlow 1.6 with CUDA 9.0, cuDNN 7.0, and Python 2.7. The drawing of bounding boxes and image processing parts was achieved using OpenCV 3.4.1. For an image size of \(500\times 375\) pixels,  
Figure 21: Outputs from two variants of the Faster R-CNN [5] with ResNet101 [2]. **Upper row:** Ordinary model trained with PASCAL VOC 2007 [6]. **Lower row:** Proposed system trained with our nighttime data.  
Figure 20: Outputs from two variants of the Faster R-CNN [5] with ResNet101 [2]. **Upper row:** Ordinary model trained with PASCAL VOC 2007 [6]. **Lower row:** Proposed system trained with our nighttime data.  
which was our predefined input size, the system with VGG16 [1] as the feature extractor could run approximately 31 FPS and the system with ResNet101 could run approximately 16 FPS [2].
# Mobile Internet Technology Adoption for Sustainable Agriculture: Evidence from Wheat Farmers
## 3 Study Hypothesis for the MIT Adoption
### Farm Characteristics  
Because of several factors, such as the high demand for information on the farm and the complexity of the organization, large farm size is positively associated with MIT adoption [67]. Generally, the size of the server farm will affect the information technology application [68,69], and it is also believed that large-scale farms are more likely to adopt MIT. Consequently, the following research hypothesis was made:  
**Hypothesis 3a.**_(Farm size): Large farm farmers are more likely to adopt the MIT._  
Internet-based regional obstacles are usually the outcome of the geographic environment of digital communication infrastructure and the absence of digital connection [26]. Regarding agricultural and IT adoption [60,63,67], evidence showed that the regional farm location is strongly related to higher IT adoption rates in the United States and Greece. About MIT, [PERSON] et al. [70] indicated that due to the decentralized digital infrastructure, the adoption of MIT could be influenced by the location of farm and agricultural activities. According to the information specified via the government of Pakistan, the MIT coverage in northwestern Pakistan seems to be relatively underdeveloped. Considering all those facts, the following hypothesis was assumed:  
**Hypothesis 3b.**_(Region): The farm's location in the northwestern area is adversely associated with MIT adoption._  
The impact of farm diversification on agricultural technologies, including computers and the internet, has shown various interesting findings. For example, [PERSON] et al. [71] and [PERSON] [72] found no connection between farm diversification and equipment adoption, while [PERSON] and [PERSON] [64] found a negative correlation between farm diversification and the adoption of IT. According to [PERSON] [72], the diversification of farms is positively related to IT and MIT adoption. The owners of relatively diversified farms must gain more knowledge to make agricultural decisions, hence more likely to MIT adoption. According to the findings of [PERSON] and [PERSON] [73], diversified companies/arms have higher requirements for information technology. Although the outcomes obtained are different in the past study, we assumed the following hypothesis:  
**Hypothesis 3c.** (_Farm diversification): Farms diversification is correlated to MIT adoption_.
## 5 Performance Study
### Simulation Results
#### 5.3.2 Simulation Results using a Realistic Scenario
For example, when the load of each node is 900 Kbps, RoCoMAR has the average throughput over 600 Kbps, while other protocols show less than 400 Kbps. As also shown in Figure 9, RoCoMAR's throughput increases until the node load becomes 900 Kbps then decreases. The reason is that a significant amount of MAC layer packet collisions and retransmissions occur as the data rate becomes close to the network capacity, which degrades the valid network throughput. DSR shows the lowest throughput when the network load is high. Figure 8: Effects of node mobility on average end-to-end delay. Figure 10 compares the average end-to-end delay of RoCoMAR with other protocols. As shown in the Figure 10, RoCoMAR and AODV show a consistently low average end-to-end delay (less than 0.5 seconds) over the network loads. This indicates that RoCoMAR can be applied effectively without a large delay in a mobile sensor network where the high data rate applications are used. As also shown in Figure 10, the delay of GFR, DSR, and OLSR sharply increase as the network loads grow. In particular, the delay of DSR is fluctuating due to the unpredictable nature of DSR routing cache.
## 4 Performance Evaluation
### Performance Analysis
#### 4.3.2 Impact of Variation in 3D Spatial Reuse Factor  
Recall that both 28 GHz and 60 GHz mmWave spectra can be reused on each floor. Hence, using Equation (6), the size of a 3D cluster is 1, 4, 9, or 16 for a maximum number of 18 apartments per floor for a 10-story building. However, using Figure 2, it can be found that for a 3D cluster size of 1, \(I_{\text{optimal,intra}}\) needs to be set to 1, which is not feasible, since the maximum value of the normalized desired received signal power of a small-cell UE from its serving small cell is 1. Hence, using Equation (10), a 3D cluster size of 4, 9, or 16 corresponds to \(\varepsilon_{\text{RF}}\) value of 35, 20, or 11.25, respectively.  
Figures 4 and 5 show SE and EE responses for \(\varepsilon_{\text{RF}}\) values of 35, 20, and 11 with the variation in \(L\). From Figure 4a, it can be found that SE improves linearly with an increase in \(L\) for any value of \(\varepsilon_{\text{RF}}\). Similarly, SE improves linearly with an increase in \(\varepsilon_{\text{RF}}\) for any value of \(L\) (Figure 5a). Hence, Figures 4a and 5a imply that SE improves by a factor of \((\varepsilon_{\text{RF}}\times L)\) (i.e., it is a horizontal and vertical spatial reuse product). However, from Figures 4b and 5b, it can be found that EE improves noticeably with an increase \(\varepsilon_{\text{RF}}\) for low values of \(L\) (e.g., \(1\leq L\leq 5\)). As \(L\) gets larger (e.g., \(L>50\)), no significant improvement in EE is achieved, even though \(\varepsilon_{\text{RF}}\) increases from 11 to 35 (Figure 5b). Hence, \(\varepsilon_{\text{RF}}\) mainly impacts SE enhancement, irrespective of the values of \(L\), whereas \(\varepsilon_{\text{RF}}\) contributes to enhancing EE for low values of \(L\).
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 6 Conclusions  
VANETs are the main component of communication framework intelligent transportation systems; therefore, they have been extensively studied from both industry and academia in the last twenty years.  
It is expected that vehicles will be equipped with advanced onboard units, multiple communication technologies, and sensor platforms. VANETs provide important information to drivers by using vehicle-to-vehicle communications. Since VANETs are a distributed, self-organized network, a key component in their operation to guarantee their minimal dependence in fixed infrastructure routing plays a crucial role.  
In this article, we discussed the importance of developing routing protocols in VANETs and summarized some that are especially proposed for this kind of wireless networks. More precisely, we focused on proposals that use several metrics to select the forwarding path, presenting the importance of these metrics in the behavior of vehicular networks, and how their selection can improve in vehicle communication. We presented the characteristics of these routing protocols (i.e., year of release, area of evaluation, metrics used) and how they are evaluated (i.e., simulators and mobility generators).  
In general, after analyzing the contents of Table 5, we concluded that link stability (or link lifetime or stability), position, density, and speed are the more promising metrics in routing protocols for VANETs because of geographical constraints. However, the performance of a routing protocol in vehicles' networks is very related with the mobility model, for that reason, other metrics that provide good performance are direction, trajectory or acceleration that are very related to the mobility of the network.  
In a nutshell, depending on the application of VANETs, it is necessary to design specific routing protocols and consider the mobility model to fulfil its requirements. Even though routing in VANETs has received more attention in the wireless-network community, there are still quite a few challenges that have not yet been carefully investigated. New directions in the development of VANET routing protocols include artificial intelligence [92] or trust in communications [93]. As future work, we are gathering statistics of VANET routing protocols reported in the literature to carry out meta-analysis of VANET performance.  
[PERSON] developed the proposal of the study, carried out the information search and analysis of the investigation, and did most of the writing. [PERSON] wrote several sections, made corrections, and edited the paper. [PERSON] and [PERSON] made corrections throughout the manuscript and guided the entire process. All authors contributed to the selection of papers to be analyzed from the searching results in order to provide a more comprehensible final version. This work has partially been supported by the \"Universidad Autonoma de Sinaloa\" (UAS, Mexico) by PRODEP. The authors declare no conflict of interest.
## 4 Use Case and Simulation of the Proposed System
### PMIPv6/AAND
#### 4.4.5. PMIPv6/AAND--Handover Analysis  
In PMIPv6/AAND, the MN handover is determined by Equation (6).  
\[\text{HOD}_{\text{PMIPv6/AAND}}=\text{Layer 2 connection}+\text{MAX}(\text{ t}_{\text{RS}},\text{ t}_{\text{RA}})+\text{TTD}_{\text{Data}} \tag{6}\]  
Here, the delay of authentication is not measured, since the tunnel is recognized after accepting MN's request. Unless the handover is not efficient enough and 3 GPP interface is utilized again, it is still worthwhile to continue with handovers rather than leave traffic on the 3 GPP interface. Of course, the bigger the number of handovers conducted, the more likely the quality of service would deteriorate, necessitating the use of optimal decision-making strategies [19]. Moreover, the AAND field continues preceding the MAG details as well as giving the details of MN-ID to M-MAG. Consequently, there is refusal necessity of authentication and its delay. The concurrent action of RS and RA occur in the projected method. In comparison to other techniques, full authentication of a node is not required when it transfers to a new domain, allowing for secure and seamless handovers across domains. [20] Since the P-MAG details are preserved by the LL-ID, the router solicitation occurs animatedly, and this information is stored by the AAND fields. Therefore, in the proposed method, the maximum time of RS and RA is estimated for the handover of PMIPv6/AAND.  
PMIPv6/AAND--Handover Simulation Result  
Here, the MN begins with the present interface if_1 i.e., WLAN. Then, the next interface if_2 i.e., Wi-Max, comes into the simulation at the 13 th second, yet the MN hands over its sign to if_2 i.e., Wi-Max, at 13.1 second. Then, the next 3 rd interface if_3 i.e., 3G,  
Figure 16: PMIPv6/SAND—Handover Simulation result.  
comes into the simulation at 24.5 seconds, yet the MN hands over its sign to 3G at 24.9 seconds. Figure 17 portrays the handover diagram of PMIPv6/AAND.
## 3 Underwater Routing Protocols
### Ehcar  
The energy hole and coverage avoidance routing (EHCAR) in [44] overcomes energy and coverage holes. When the residual energy of a node falls below a certain threshold, it broadcasts its status to the neighbor nodes. All the nodes receiving the broadcast message further forward the message. Other nodes then move to the position of the node with energy lower than the threshold. The movement of nodes is controlled in a manner that new holes are not created in response to covering the already created holes. However, a hole may change its position before a high residual energy node takes its position due to high propagation delay and communication among the nodes as to which node will move to the hole position. This leads to false position estimation of the node with residual energy below the threshold.  
Pam  
The position-aware mobility (PAM) of underwater autonomous vehicles (AUV's) to avoid void zones is proposed in [45]. From the predefined positions of the gliders at the start of the sojourn tour, the direction of motion, current positions and distance from the neighbor gliders and the mobility patterns are defined and estimated, so no void zone is left. The transmit power of the gliders is also varied in the defined mobility patterns. However, the mobility model consumes too much energy of the gliders and causes high propagation delay when gliders are far apart.
## 5 Conclusions  
1. Taking the standard deviation of M3C2 distance as the index, the repeatability of the SfM-UAV time-series data was evaluated via airborne LiDAR data. The results showed that the standard deviation of the M3C2 distance was between 0.14 and 0.19, which shows that the repeatability of the multi-temporal photogrammetric data of the UAV was good;
2. The dynamic subsidence basin constructed by DoD analysis can reveal clearly the development process of surface movement in the basin. As the working face advances, the range of influence of the surface will expand correspondingly, and the maximum subsidence value will gradually increase;
3. The RMSE of the difference in elevation between the measured monitoring points and that from the subsidence DEM extraction is mostly between 0.2 m and 0.3 m, with the highest accuracy being up to 0.17 m. The relative error between the maximum subsidence value fitted by the profile line of the main section and the measured value was less than 20%, and the minimum value was 0.7%. The accuracy of UAV subsidence DEM monitoring the maximum subsidence value is high;
4. The DEM derived from the dense matching point cloud of the UAV has the potential to estimate the uncertainty and detect changes in elevation. The DoD with a minLoD threshold is helpful for detecting small changes in elevation that may be related to experimental errors, thus permitting us to quantify reliably topographic changes caused by subsidence.  
Conceptualization, J.L.; methodology, X.L. ([PERSON]) and X.L. ([PERSON]); software, X.L. ([PERSON]); validation, X.L. ([PERSON]), B.W. and X.L. ([PERSON]); formal analysis, J.L.; investigation, X.L. ([PERSON]) and X.L. ([PERSON]); resources, J.L. and X.L. ([PERSON]); data curation, X.L. ([PERSON]); writing--original draft preparation, X.L. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, [PERSON] and [PERSON]. ([PERSON]); project administration, [PERSON] and [PERSON]. ([PERSON]); funding acquisition, [PERSON] and [PERSON]. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Scientific Research Fund of Guizhou Provincial Education Department of China, grant number KY [2017] 097; Guizhou Province High-level Innovative Talents Project, grant number Graduation Talents [2021] 09; National Natural Science Foundation of China, grant number 42101414; National Natural Science Foundation of China, grant number 51704205; Natural Science Foundation of Shanxi Province, grant number 201901D111074.  
Not applicable.  
We would like to thank the reviewers for their helpful comments.  
The authors declare no conflict of interest.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 2 Technology Enablers for the Internet of Verticals
### Semantic Communications  
Semantic communication, widely regarded as a potential breakthrough in the 6G era beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning. In contrast to the Shannon paradigm, semantic communications only transmit necessary information relevant to the specific task at the receiver. This can empower a truly intelligent and autonomous system with significant reduction in data traffic. For many vertical applications in the age of 6G empowered by artificial intelligence (AI), the agent, such as smart terminals, robot, and smart surveillance, is able to understand the scene and executes the instruction automatically using semantic communication. Hence, the technology will be widely used in the Industrial Internet, connected and autonomous driving and vehicles, and, as we describe further, for robotic communications.
# Reliability of Social Networks on Activity-on-Node Binary-State with Uncertainty Environments
## 3 Overview of BAT and Fuzzy Set
### Fuzzy Set Theory
#### 3.3.1 Fuzzy Number  
Let \(x\) be a real number allocated to \(\underline{A}\), and \(\underline{A}\subset\) the universe set \(U\). The fuzzy set \(\underline{A}\) is defined as follows [43]:  
\[\underline{A}\ =\ \{(x,\ u_{\underline{A}}(x))|\ x\ \in\ X\} \tag{1}\]  
where \(u_{\underline{A}}(x)\): \(X\to[0,1]\) denotes the membership function of fuzzy set \(A\).  
There are many forms of fuzzy numbers that represent fuzzy sets. In this study, triangular fuzzy numbers are applied. A triangular fuzzy number \(\underline{A}\) = (\(a\), \(b\), \(c\)), where \(a\leq b\leq c\), is defined in Equation (2); it is depicted in Figure 3[46]:  
\[u_{\underline{A}}(x)=\begin{cases}(x-a)/(b-a),&a\leq x\leq b,\\ (x-c)/(b-c),&b\leq x\leq c,\\ 0,&\text{otherwise},\end{cases} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{i}\) & \(\mathbf{Q}_{i}\) & \(\mathbf{Q}_{i+1}\) & \(\mathbf{V^{*}}\) & **Remark** \\ \hline
0 & \(\{1\}\) & \(\{2,\ 3\}\) & \(\{1,\ 2,\ 3\}\) & \\
1 & \(\{2,\ 3\}\) & \(\{5\}\) & & \(X\) is connected \\ \hline \hline \end{tabular}
\end{table}
Table 3: Process from the proposed PLSA for Figure 2.  
Let \(\underline{A}_{1}=(a_{1},b_{1},c_{1})\) and \(\underline{A}_{2}=(a_{2},b_{2},c_{2})\) be two triangular fuzzy numbers. The fuzzy addition, subtraction, multiplication, and division of triangular fuzzy numbers are as shown in Equations (3)-(6), respectively [46]:  
\[\underline{A}_{1}\ \oplus\ \underline{A}_{2}\ =\ (a_{1}\ +\ a_{2},\ b_{1}\ +\ b_{2},\ c_{1}\ +\ c_{2}) \tag{3}\]  
\[\underline{A}_{1}\ \ominus\ \underline{A}_{2}\ =\ (a_{1}\ -\ c_{2},\ b_{1}\ -\ b_{2},\ c_{1}\ -\ a_{2}) \tag{4}\]  
\[\underline{A}_{1}\ \otimes\ \underline{A}_{2}\ =\ (a_{1}\ \times\ a_{2},\ b_{1}\ \times\ b_{2},\ c_{1}\ \times\ c_{2}) \tag{5}\]  
\[\underline{A}_{1}\ \odot\ \underline{A}_{2}\ =\ (a_{1}/c_{2},\ b_{1}/b_{2},\ c_{1}/a_{2}) \tag{6}\]  
The \(\alpha\)-cut is another common way to represent the fuzzy set; it is also an important approach for transforming a fuzzy membership function into a crisp subset [46]. The \(\alpha\)-cut is a horizontal representation of fuzzy sets. Let \(\underline{A}_{\alpha,\text{UB}}\) and \(\underline{A}_{\alpha,\text{LB}}\) be the upper and lower bounds of the \(\alpha\)-cut, respectively. The \(\alpha\)-cuts of fuzzy set \(\underline{A}\) can be defined as in Equation (3) [46]:  
\[\underline{A}_{\alpha}\ =\ \{(x,\ u_{\underline{A}}(x)\ \geq\ \alpha)|x\ \in\ U\}\ =\ [\underline{A}_{\alpha,\text{LB}}(\alpha)\ \underline{A}_{\alpha,\text{UB}}(\alpha)], \tag{7}\]  
For example, as shown in Figure 4, the \(\alpha\)-cut for the triangular fuzzy number \(\underline{A}=(a,b,c)\) is:  
\[\underline{A}_{\alpha}\ =\ [\alpha(b\ -\ a)\ +\ a,\ \alpha(b\ -\ c)\ +\ c]. \tag{8}\]