esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Monitoring Methods and Principles  
### Composition of the GNSS Monitoring System  
Based on the spatial relationship between the slope body and the two panels, a GNSS real-time monitoring system was established at the landslide above Luhu village. The system consisted of eight monitoring points and a base station, and their location is shown in Figure 1, where the monitoring points M01-M04 were located at the top of the high-steep slope, M06 was located at the bottom of the slope, and M07 and M08 were located at the south of the panel A, while M05 was located at the center of the panel B, which was also at the top of the other slope. At each point, the foundation pit of \(60\times 60\times 60\) cm was excavated on the ground, then the connecting steel bar was embedded, the concrete was poured to form the foundation, and the monitoring station can be fixed to the foundation by screws to be stable. The height of each station was 2.3 m, which ensured it can receive enough strong signals from satellites. To ensure the stability of the base station, it was arranged at a distance of about 4 km from the mining area to form the local area network (LAN) differential positioning mode. The system consisted of a monitoring module, power supply module, video monitoring module, and rainfall monitoring module, and its detailed composition is shown in Figure 2. The monitoring module consisted of a GNSS receiver, antenna, general packet radio service (GPRS) data transmission module, and an early warning and alarm module. The system was solar powered and was equipped with a battery storage system, which ensured continuous operation for five days as well as operation on rainy days. The system could obtain real-time monitoring data, and the accuracy of a 1 h static observation solution could reach the mm level. The precision of GNSS PPP (precise point positioning), RTK (real-time kinematic) and static baseline positioning based on landslide monitoring are better than 6 cm, 6 and 3 mm, respectively [38], which was verified a landslide monitoring.
# A Novel GPU-Based Acceleration Algorithm for a Longwave Radiative Transfer Model
## 2 Related Work  
There were many successful attempts at using GPUs to accelerate physical parameterization schemes and climatic system models. This section describes the most salient work along this direction.  
The WRF Goddard shortwave radiance was accelerated on GPUs using CUDA C [24]. Via double precision arithmetic and with data I/O, the shortwave radiance obtained a 116\(\times\) speedup on two NVIDIA GTX 590 s [25]. The WRF five-layer thermal diffusion scheme was accelerated using CUDA C, and a 311\(\times\) speedup was obtained on one Tesla K40 GPU [26]. The WRF Single Moment 6-classmicrophysics scheme was also accelerated using CUDA C, and obtained a 216\(\times\) speedup on one Tesla K40 GPU [27].  
The WRF long-wave RRTM code was ported to GPUs using CUDA Fortran [28]. The RRTM on Tesla C1060 GPUs attained a 10\(\times\) speedup [29]. The RRTM longwave radiation scheme (RRTM_LW) on the GTX480 obtained a 27.6\(\times\) speedup compared with the baseline wall-clock time [1]. The CUDA Fortran version of the RRTM_LW in the GRAPES_Meso model was developed. It adopted some optimization methods for enhancing the computational efficiency, and obtained a 14.3\(\times\) speedup [10].  
The Fortran code of the WRF RRTMG_LW was rewritten in the C programming language, and then its GPU parallelization was implemented using CUDA C. With I/O transfer, the RRTMG_LW achieved a 123\(\times\) speedup on one Tesla K40 GPU [30]. The Fortran code of the RRTMG_SW was also rewritten in the C programming language. Furthermore, the RRTMG_SW achieved a 202\(\times\) speedup on one Tesla K40 GPU compared with its single-threaded Fortran counterpart running on Intel Xeon E5-2603 [31].  
In a significantly different approach from the previous work, this study first proposes a new and detailed parallel algorithm in the _g-point_ dimension for the CAS-ESM RRTMG_LW. Rewriting the original Fortran code of the RRTMG_LW would take considerable time, so CUDA Fortran rather than CUDA C was adopted in the parallelization implementation. The major concerns addressed by the proposed algorithm include the following: (a) runtime efficiency, and (b) common processes and technologies of GPU parallelization.
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.8 Cache Optimization  
In the last decade, multimedia data became dominant in mobile data traffic. This raised additional challenges in transporting the big volume of data from the content providers to the end users with high-rates and low latency. The main bottleneck point is the severe traffic congestion observed in the backhaul links, specially in 5G scenarios, where several small base stations will be scattered [53]. To mitigate this issue, the most popular content can be stored (cached) at the edge of the network (e.g., in the base stations) in order to free backhaul link usage [54]. However, finding the best strategy for the cache placement is a challenge. The best content to cache and the best location for storing this content are both decisions that can impact the cache scheme performance.  
Two works addressed the cache placement problem in 5G environments using deep learning models. In [55], authors proposed a collaborative cache mechanism in multiple RRHs to multiple BBUs based on reinforcement learning. This approach was used because rule-based and metaheuristics methods suffer some limitations and fail to consider all environmental factors. Therefore, by using reinforcement learning, the best cache strategy can be selected in order to reduce the transmission latency from the remote cloud and the traffic load of backhaul.  
In [56], the authors considered ultra-dense heterogeneous networks where the content cache is performed at small base stations. The goal is to minimize energy consumption and reduce the transmission delay, optimizing the whole cache placement process. Instead of using traditional optimization algorithms, a deep learning model was trained to learn the best cache strategy. This model reduces the computational complexity achieving a real time optimization.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 4 Positioning and Communication for a Fleet of UAVs  
Once the fair distribution of the resources among all the users in the coverage area corresponding to a single UAV has been addressed (see the previous section), we consider now the fair distribution among all users in the multi UAV scenario while taking also into account the restrictions imposed by the backhaul links. To facilitate the notation, in the following, we assume that the intra-cell resource allocation is based on the equal rate scheduler (Section 3.2), although the proposed algorithm could be applied to any other intra-cell scheduling approach.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Experimental Setup  
For the validation of our proposed SBAODV routing protocol, we conducted simulations in NS2. As part of our work, we modified the ad hoc on-demand distance vector (AODV) routing protocol. We updated it with the proposed Stackelberg game theory scheme, resulting in the SBAODV routing protocol for hybrid IoMT network simulations. We designed a hybrid network topology for an IoMT scenario, assuming six fixed medical servers at different locations connected with wired topology and 110 wireless nodes, including UAVs, black holes, access points, medical nodes, and mobile nodes. During the experiment, we introduced a rogue device, also known as a black hole attacker, to disturb the network. The simulation area was set to 125 \(\times\) 125 \(\times\) 50 (m\({}^{3}\)), and 110 nodes were randomly distributed within this area using a uniform random distribution policy, as commonly performed in the literature [38]. Although other distribution policies could have been considered for network topology development, we chose to use a uniform random distribution policy as a simple baseline for evaluating the performance of the IoMT network. We utilized the IEEE 802.15.4 Media access control (MAC) protocol, a low data rate wireless personal area network standard widely used in various low-power, low data rate, and low-cost communication applications [39; 40].  
To evaluate the efficiency of our proposed SBAODV scheme, we compared it with several other routing protocols, including low-power and noisy routing protocol (LPNRP) [32], tree protocol for healthcare (TPH) [41], a routing protocol for healthcare (RPH) [42], fuzzy dynamic trust-based routing protocol (FDTRP) [43], and dual attack detection routing protocol (DADRP) [44]. We assumed a reactive gateway as the IoMT gateway in the network and simulated the network for 600 s using a constant bit rate (CBR) traffic model and UDP application. Quality of service was evaluated based on several parameters, including average delay, jitter, packet deliver ratio % (PDR%), network routing load% (NRL%), throughput (kbps), and detection ratio %. A Drop Tail-Priqueue was used as the queue type. These parameters are summarized in Table 4, and the network environment is depicted in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of nodes & 110 \\ Simulation area (3D) & 125 \(\times\) 125\(\times\) 50 (m\({}^{3}\)) \\ Node placement & Uniform random distribution \\ Transmission range & 100 m \\ MAC protocol & IEEE 802.11.15.4 \\ Routing protocols & SBAODV, LPNRP, TPH, RPH, FDTRP \& DADRP \\ Rouge device & As a black hole attacker \\ IoMT gateway node & Reactive gateway \\ Fixed medical server Nodes & 6 \\ Data rate & 100 Kb \\ Packet size & 512 Bytes \\ Simulation time & 600 s \\ Traffic model & CBR with UDP application \\ QoS & PDR\%, NRL\%, throughput (kbps), detection \\ ratio \%, end-to-end delay (ms), \\ Queue type & Drop Tail–Priqueue \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Trickle timer-Based Solutions
#### 4.1.2 Enhanced Trickle Algorithm  
According to the author [57], when the network is consistent, the DIO messages flow slower according to the Trickle timer algorithm. If a mobile node is moving to an alternative location it will not able to enter a network due to slow DIO messages transmission. Instead, this node will send a DIS message to its neighboring nodes from a new location to request a DIO message. Therefore, the node must wait until the DIO message is received from the neighboring router nodes or artificially re-configuring the Trickle timer. Hence, the authors [57] proposed an enhanced version of the Trickle algorithm to solve the increased latency problem occurring from a 'listen-only' period in the Trickle timer algorithm. The proposed algorithm supports node mobility by adjusting DIS message transmission period dynamically based on Doppler frequency which is used to measure mobile node moving speed and signal strength which is calculated to measure the distance between router node and the mobile node. When a mobile node wants to enter a network to find its parent node, it sends a DIS message to neighboring router nodes.  
After receiving the DIS message from the mobile node, the neighboring router nodes transmit a DIO message to the mobile node. Upon receiving DIO, the mobile node stores an OF value and selected node ID for choosing the parent node from the parent list table. In addition, the mobile nodes save the information of neighboring routers as Preferable Parents (PPs) in the parent list table. When the mobile node moves from one location to another and its interval for selecting a new parent node expires, it selects a router node with the highest OF value as a new parent among the PPs, and parent entry is updated in the parent table list. Following, the mobile node evaluates the value of Doppler frequency and signal strength from the DIO message to determine the time interval dynamically. Subsequently, the mobile node sends a DAO message to a selected router node which becomes a new parent node once the selected time interval is initialized. The simulation results show 40.1% and 52.7% lower DIS message transmission count as compared to the original Trickle timer algorithm. In addition, the average packet loss rate for mobile node speed of 1.25 m/s in the proposed algorithm evaluates to 0.3% as compared to 19.4% of the original algorithm, in addition, 0% of E-Trickle algorithm [58], and 11.3% of ME-RPL algorithm [42]. The energy consumption of a mobile node evaluates to 72.5 mJ for the proposed algorithm compared to 78.9 mJ for the original algorithm, 79.2 mJ for the E-Trickle algorithm, and 77.5 mJ for the ME-RPL algorithm.
# Design of Wing Root Rotation Mechanism for Dragonfly-Inspired Micro Air Vehicle
## 4 Design of Flapping Mechanism  
### Analysis of Kinematics  
In this work, we used the spatial four-bar-based flapping mechanism to convert from rotational motion to a couple of flapping motions using a rotary actuator. The spatial four-bar-based flapping mechanism consists of two spherical joints, a crank link, and a flapping link. In this mechanism, the factor considered for the design is the limited range of spherical joints. Figure 8 shows the angle limit of the spherical joint; when the \(y\)-axis is the joint axis, it has an angle limit of \(-30^{\circ}\) to \(30^{\circ}\) in the \(x\)-axis direction and \(z\)-axis direction.  
The schematic of the flapping mechanism is shown in Figure 9, where \(O_{1}\), \(O_{2}\) are the three-axis spherical joints, \(O_{0}\), \(O_{3}\) are one-axis joints; \(\theta_{1}\) is the crank angle; \(\theta_{3}\) is the flapping angle; and \(\theta_{2,x}\), \(\theta_{2,z}\) are the angles of the spherical joint. The kinematics of the flapping mechanism was analyzed by vector analysis; the input variable is \(\theta_{1}\), which is the crank angle. The flapping angle (\(\theta_{3}\)) is defined by the crank angle and expressed as (8).  
\[\theta_{3}=\sin^{-1}\left(\frac{l_{2}^{2}-(k_{x}^{2}+k_{y}^{2}+k_{z}^{2}+l_{3 }^{2})}{2l_{3}\sqrt{k_{x}^{2}+k_{z}^{2}}}\right)-\sin^{-1}\left(\frac{k_{x}}{ \sqrt{k_{x}^{2}+k_{z}^{2}}}\right) \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Parameters** & \(\mathbf{s_{1}}\) & \(\mathbf{s_{2}}\) & \(\mathbf{s_{4}}\) & \(\mathbf{\theta_{1}}\) & \(d_{2,\text{min}}\) \\ \hline
**Value** & 6 mm & 6 mm & 12.62 mm & 72.97\({}^{\circ}\) & 13 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of wing root rotation mechanism.  
Figure 8: Limitation of spherical joint.  
Figure 7: (a) Kinematics of input motion (slide motion) and output motion (wing root rotation). (b) Graph of relationship between \(\theta_{r}\) and \(d_{2}\) to define \(d_{2}\) with respect to desired \(\theta_{r}\) (\(30^{\circ}\)).  
In the equation, \(k_{x}=l_{4,x}\), \(k_{y}=l_{4,y}-l_{1}\cos\left(\theta_{1}\right)\), and \(k_{z}=l_{4,z}-l_{1}\sin\left(\theta_{1}\right)\). Then, using the defined \(\theta_{3}\), the spherical joint angles \(\theta_{2,x}\), and \(\theta_{2,z}\) are described by (9) and (10), respectively.  
\[\theta_{2,x}=\cos^{-1}\left(\frac{k_{x}+l_{3}\cos\left(\theta_{3}\right)}{l_{ 2}}\right) \tag{9}\]  
\[\theta_{2,z}=\cos^{-1}\left(\frac{k_{y}}{l_{2}\sin\left(\theta_{2,z}\right)}\right) \tag{10}\]
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 2 Related Work
### Cluster Construction  
Clustering is a typical method of node management in vehicular networks and thus is a target of research [10; 11]. These methods, however, are designed for general vehicular communication and are not optimized for CCN.
## 3 Ambient Vibration Analysis
### Research on the Effect of Power Increase on Vibration  
Based on actual requirements, the running of a PSPS often necessitates the simultaneous functioning of multiple units, which means that multiple units collaborate under power generation conditions or pumping conditions. The z-direction vibration acceleration in each section of the diversion pipeline undergoes variations with the increase in the number of units running. This section mainly researches the variation with the increase in power. The main working conditions are shown in Tables 5 and 6. The analysis results are shown in Figures 9 and 10.  
As shown in Figure 9, the root-mean of vibration at each measuring point increased with increasing power under power generation conditions. The measuring point in the lower flat tunnel obtained a maximum value of 0.54 cm/s\({}^{2}\). Moreover, the upper inclined shaft obtained a minimum value, and the vibration energy was attenuated by approximately 90% when it reached this measuring point. At this time, there was no discrepancy in the root-mean of vibration compared with shutdown conditions, indicating that the upper inclined shaft location was unaffected by vibration. Therefore, the vibration in this part was negligible.  
As shown in Figure 10, the propagation law under pumping conditions resembled that under power generation conditions with increasing power. The largest root-mean of vibration occurred in the lower flat tunnel, with a value of 0.55 cm/s\({}^{2}\) at -1687 MW, and it decreased rapidly with increasing distance. Considering the measuring point of the upper reservoir, the pumping condition exhibited discrepancies compared with the power generation condition. The maximum root mean of vibration did not exceed 0.1 cm/s\({}^{2}\) in power generation conditions; on the contrary, the minimum root mean of vibration was close to 0.15 cm/s\({}^{2}\). This shows that the upper reservoir was affected more under pumping conditions. The vibration acceleration of the upper inclined shaft remained relatively stable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Conditions** & **Power (MW)** \\ \hline Unit 4\# under power generation conditions & 350 \\ Units 1\# and 3\# under power generation conditions & 700 \\ Units 1\#, 4\#, and 5\# under power generation conditions & 1050 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Power generation working conditions.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge
### LoRaWAN Protocol Architecture
In such environments, the minimization of energy consumption and related costs are of particular interest. As documented in the latest version of the LoRaWAN protocol [23], there are two methods to provide access to end-devices:  
* Over the air activation (OTAA): in this case, end-devices complete a join procedure which involves an exchange of a set of authentication messages with the NS, before initiating data exchanges. Specifically, the join procedure requires a join-request from end-device to the NS and a join-accept from NS to the end-device. Before the join procedure starts, an end-device is characterized by the following information: a globally unique end-device identifier (DevEUI), the join server identifier (JoinEUI),  
Figure 1: LoRaWAN protocol architecture. and an advanced encryption standard key (AppKey). Whenever an end-device loses its network session info, it initiates a new join-procedure. Finally, the OTAA method is the most widely used because it offers a secure way to join a network as the network session info, such as application and network session keys (AppSKey and NwkSKey), is dynamically assigned by the network. * Activation by personalization (ABP): Activation is established through two session keys (AppSKey and NwkSKey), and a device address (DevAddr) that are prestored on end-devices. Therefore, this method enables direct communication between devices and servers, through all network GWs without initiation of join procedure. However, in this method, security level is lower than OTAA, since the keys may be violated. To avoid data packet replay attacks, a mechanism is used that changes session keys each time the end-devices restart [24].
# Improving Sentence Retrieval Using Sequence Similarity
## 5 Experiments and Results
To compare the difference between methods, we used a two-tailed paired \(t\)-test with a significance level of \(\alpha=0.05\)[2, 4, 6]. The results of our tests are presented in tabular form. Statistically significant differences in relation to the baseline methods are marked with a (*). In each of the tested methods, we used stop word removal as pre-processing step. Table 3 shows the results of our tests on two different versions of the TF-ISF method using TRECs 2002, 2003, and 2004. Table 3 shows that the TF-ISF\({}_{part}\) methods provided better results and statistically significant differences in relation to the base TF-ISF method when the MAP measures was used in all three collections, and R-prec. in TREC 2003 and TREC 2004 collection. Better results were not achieved when using the P@10 measures. Table 4 shows the results of our tests on two different versions of the BM25 model using TRECs 2002, 2003, and 2004. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Collection** & **Measures** & **TF-ISF** & **TF-ISF\({}_{part}\)** \\ \hline \multirow{3}{*}{TREC 2002} & P@10 & 0.304 & 0.32 \\  & MAP & 0.196 & * 0.204 \\  & R-prec. & 0.245 & 0.250 \\ \hline \multirow{3}{*}{TREC 2003} & P@10 & 0.692 & 0.714 \\  & MAP & 0.576 & * 0.591 \\ \cline{1-1}  & R-prec. & 0.547 & * 0.560 \\ \hline \multirow{3}{*}{TREC 2004} & P@10 & 0.434 & 0.468 \\ \cline{1-1}  & MAP & 0.324 & * 0.335 \\ \cline{1-1}  & R-prec. & 0.336 & * 0.355 \\ \hline \hline \end{tabular}  
* Statistically significant differences in relation to the baseline methods.  
\end{table} 
Table 3: Test results of methods TF-ISF and TF-ISF\({}_{part}\). The parameters settings for the BM25 model were \(k1=1.5\), \(b=0.75\), \(k3=0\). Table 4 shows that the BM25\({}_{part}\) method (method using sequence similarity) provided better results and statistically significant differences in relation to the base method in all measures and collections. Table 5 shows the results of our tests on two different version of the LM model using TREC collections from 2002, 2003, and 2004. The parameter settings for LM were \(\mu=100\). Table 5 shows that the LM\({}_{part}\) method, as well the BM25\({}_{part}\) method, provided better results and statistically significant differences in relation to the base method in all measures and collections, except for the P@10 measure for TREC 2003 collection.
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
## 6 Comparison  
Table 2 compares this study to the prior works published in the literature that also featured horizontal polarization. All works tabulated were designed with external open feed and lack lighting protection capability. The polarization purity was also not so high. This work proposes for the first time an antenna that could produce horizontal polarization utilizing slot radiators and form a stable gain at the azimuth plane like traditional omnidirectional design topologies such as loop or printed dipole antenna arranged in the form of a circle. Due to vertical slot radiations, its cross-polarization levels are extremely low, as shown in the Table. The proposed antenna is novel because it has an internal axis-symmetric feeding system. Due to its enclosed nature, the feed does not radiate and interfere with the main radiating slots. So low-gain ripples in the azimuth plane are achieved as compared to the listed works. Moreover, it is DC-grounded, which is essential for any practical deployment.