esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Optimization Variable
#### 3.3.2 Linux Kernel Scheduler Parameter  
The Linux kernel provides 14 scheduler parameters for optimization. In this study, we applied an optimization on nine of the parameters, excluding five parameters that do not affect the performance. Table 1 shows the parameter value ranges that can change and the default values of the Linux kernel. They can change without a machine reboot using the command _systctl_ provided by Linux. The meaning of each parameter is as follows.  
**sched_latency_ns:** Targeted preemption latency for CPU bound tasks. Increasing this parameter increases the timeslice of a CPU bound task.  
**sched_migration_cost_ns:** This is the amount of time after the last execution that a task considered a hot cache in a migration decision. A hot task is less likely to be migrated to another CPU, and thus, increasing this variable reduces the task migrations. If the CPU idle time is higher than expected when there are runnable processes, it is recommended to reduce this value. If tasks bounce between CPUs or nodes too often, it might be better to increase it.  
**sched_min_granularity_ns:** Minimal preemption granularity for CPU bound tasks. This parameter is tightly related to sched_latency_ns.  
**sched_nr_migrate:** This controls how many tasks can migrate across processors for load-balancing purposes. As load-balancing iterates the runqueue with disabled interrupts (sofitq), it can incur irq-latency penalties for real-time tasks. Therefore, increasing this value may give a performance boost to large SCHED_OTHER threads at the expense of increased irq-latencies for real-time tasks.  
**sched_rr_timeslice_ms:** This parameter can adjust quantum(timeslice) in a SCHED_RR policy.  
**sched_rt_runtime_us:** This is a quantum allocated to real-time tasks during sched_rt_period_us. Setting the value to \(-1\) disables RT bandwidth enforcement. By default, RT tasks may consume 95% of the CPU resources per seconds, thereby leaving 5%, or 0.05 s, to be used by SCHED_OTHER tasks.  
**sched_rt_period_us:** This is the period over which real-time task bandwidth enforcement is measured.  
**sched_cfs_bandwidth_slice_us:** When CFS bandwidth control is in use, this parameter controls the amount of run-time (bandwidth) transferred to a run queue from the control group bandwidth pool of the task. Small values allow the global bandwidth to be shared in a fine-grained manner among the tasks, whereas larger values reduce the transfer overhead.  
**sched_wakeup_granularity_ns wakeup:** This is a wake-up preemption granularity. Increasing this variable reduces the wake-up preemption, reducing the disturbance of computing bound tasks. Lowering it improves the wake-up latency and throughput for latency critical tasks, particularly when a short duty cycle load component must compete with CPU bound components.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology
### Simple Trajectory Gesture Recognition
#### 3.1.3 Uniform Resampling of Gesture Trajectory  
After smoothing and filtering the gesture trajectory, the time length and frame rate of different gestures vary due to different user habits, which can affect the recognition performance. Therefore, it is necessary to resample the gesture trajectory data. Resampling ensures that the gesture trajectory is not influenced by different users or gesture actions, and ensures that the data points of all gesture trajectories are consistent. Trajectory resampling primarily involves interpolation based on the length of the trajectory, ensuring that the data points along each segment of the gesture trajectory are evenly distributed [28].  
The specific method involves first calculating the total length of the data point paths, then dividing the total length by the interval length between new points, and finally adding new points through linear interpolation to achieve uniform resampling of the trajectory. The pseudocode for trajectory resampling is presented in Algorithm 2.  
```
RESAMPLE (points, n) \(I\gets PATH-LENGTH(points)\) / (n-1) \(D\gets 0\) newPoints\(\gets points0\) foreach point pi for \(i\geq 1\) in points do \(d\gets DISTANCE(pi-1,pi)\) if(\(D+d)\geq I\)then \(q_{x}\gets p_{i-1_{+}}+((I-D)\) / \(d)\times(p_{i_{+}}-p_{i-1_{+}})\) \(q_{y}\gets p_{i-1_{+}}+((I-D)\) / \(d)\times(p_{i_{y}}-p_{i-1_{y}})\) \(APPEND(newPoints,q)\) \(INSERT(points,i,q)\) //q will be the next pi \(D\gets 0\) else\(D\gets D+d\) returnnewPoints \(\mathbf{PATH-LENGTH(A)}\) \(d\gets 0\) for i from \(1\) to \(|A|\) step \(1\)do \(d\gets d+DISTANCE(Ai-1,Ai)\) return\(d\)
```  
**Algorithm 2** Trajectory resampling  
Resample the filtered gesture motion trajectory and plot the trajectory before and after resampling in the _xoy_ plane. The images are illustrated in Figure 12, where the blue dots represent the starting positions of the gesture trajectory and the yellow dots represent the ending positions. From the graph, it can be observed that after resampling, the non  
Figure 11: Median filtering results of gesture trajectory. (**a**) X-dimensional median filtering. (**b**) Y-dimensional median filtering. (**c**) Z-dimensional median filtering.  
uniformly distributed motion points of the gesture trajectory are evenly spaced. The resampling process converts gesture trajectory with varying speeds into uniform trajectory.
## 2 Related Work  
Many research studies have focused on propagation path loss models for vegetation at frequencies of 200 MHz to 95 GHz within a distance of 400 m, such as in a review by [PERSON] et al. in [14]. Studies on radio propagation were performed on short and tall grass environments to support animal grazing in large-scale farming at a frequency of 2.4 GHz by [PERSON] et al. in [15] and [PERSON] et al. in [16]. Wireless communication near the ground was proposed with the plane-earth model for VHF and UHF bands by [PERSON] et al. in [17]. The researchers limited their interest to specific phenomena, such as the impact of near-ground or surface components on the signal propagation in different environments. Additionally, path loss models with breakpoint distance on-ground, near-ground and aboveground (5 cm, 50 cm, and 1 m) were measured and analyzed at a frequency of 470 MHz by [PERSON] et al. in [18]. The path loss models for WSN in a palm garden used a near-ground LOS model and tree attenuation factors by [PERSON] et al. in [19]. A path loss model on a grass field was proposed at WiFi 2.4 GHz by [PERSON] et al. in [20]. Additionally, the effects of people motion on WSN 433 MHz and 868 MHz in building monitoring were analyzed by [PERSON] et al. in [21]. In addition, a fuzzy system has been used in path loss modeling. The three input fuzzy membership functions, namely, residual power of sensor nodes, distance of the node from the base station node, and RSSI, together with two output fuzzy membership functions, focused on the influence of the RSS on the cluster head nodes and rounds [22]. Lastly, AI with ANFIS (adaptive neuro fuzzy inference system) for near-ground WSNs in forest environments were proposed by [PERSON] et al. in [23]. This ML path loss model provided the lowest RMSE at 433 MHz for an open dirt road environment compared with the empirical models such as optimized FITU-R near ground model, Okumura-Hata model, and ITU-R maximum attenuation free space model.  
In this study, propagation models in the form of RSSI-distance relationships at 433 MHz for WSN at antenna heights of 0.2 m, 0.7 m, and 1.2 m are proposed. Furthermore, we apply fuzzy linear regression (FLR) with \"initial membership level\" for fitting RSSI data in a short-grass field. The results also cover RSSI data in long grass and with people motion within extended boundaries.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 2 Communication Networks for UAVs
### Fanet
#### 2.1.3 Routing Strategy  
According to [PERSON] et al. [13], a FANET architecture is directly related to the objective of the application, the flight plan and the path associated with the aircraft. After such priorities are defined, a routing strategy can be chosen. A network where the routing is performed by aircraft must take into account the following aspects in order to offer high quality services: location of nodes, energy saving, good robustness to intermittent links and topology changes.  
For a FANET which aims at scanning a specific area, it is possible to define an appropriate routing protocol because the main characteristics are known, such as the synchronized movements and constant velocities of the nodes. Moreover, resources such as storage, signal strength and throughput  
Figure 2: Simple example of a FANET (Flying ad hoc network).  
are limited. The number of hops, interference and network conditions depend on the distance and movements of the nodes, which also affect the bandwidth limit. [PERSON] and [PERSON] [39] present a study about routing protocols applied to FANETs, where the following protocols can be considered more important: optimized link state routing protocol (OLSR), destination-sequenced distance-vector routing (DSDV), ad hoc on demand distance vector (AODV) and better approach to mobile ad hoc networking (BATMAN).  
[PERSON] et al. [40] discuss how information interaction among UAVs plays an essential role, while the communication mission involving data sharing, transmission and retransmission can be carried out cooperatively. In this sense, this work focuses on performing simulations on the efficient transmission of information and ensure the quality of UAV communication. [PERSON] and [PERSON] [15] is based on the rapid change of topology that affects the performance of routing protocols in FANETs to propose an appropriate routing scheme in complex scenarios. Furthermore, a suitable routing protocol is selected for maintaining network performance at a high level. The concerned performance metrics are packet delivery ratio, network throughput, average end-to-end delay and average jitter.
## 3 Establishment of Numerical Model
### Model Construction and Boundary Conditions  
In the active freezing stage, the range of soil affected by the freezing pipe is usually 3-5 times the thickness of the freezing wall when the outer freezing pipe extends outward, and the temperature field beyond this range is basically not affected by the freezing construction according to the study of the freezing temperature field. Therefore, the influence range of the upper and lower directions of the three-dimensional calculation model is selected as 20 m from the center line of the tunnel based on the engineering prototype of this paper.  
The solid heat transfer physical field control mesh generation method is used in the numerical analysis of the freezing temperature field. The accuracy and efficiency of the calculation are considered in the meshing. The mesh far away from the freezing area is large in size and small in density, and the mesh size near the frozen area is small and the density is large. The computational model is divided into 140,347 tetrahedral elements, 22,932 triangular elements, 3053 edge elements, and 347 vertex elements. The three-dimensional calculation model of the divided grid is shown in Figure 3.  
Figure 3: Schematic diagram of geometric model.  
The stratum where the shield is located is mainly the sand layer, the water content of the stratum is 35%, and the original ground temperature is 27.8 \({}^{\circ}\)C, according to the geological exploration data. The thermal physical parameters of the shield machine and sand are shown in Table 2.  
The temperature drop of salt water in the actual project is referred to, and appropriate adjustments are made in combination with the research problems to make the numerical simulation closer to the prototype project. The brine temperature is reduced to below \(-18\)\({}^{\circ}\)C after 7 days of active freezing, to below \(-25\)\({}^{\circ}\)C after 15 days of active freezing, to below \(-28\)\({}^{\circ}\)C after 20 days of active freezing, and to below \(-30\)\({}^{\circ}\)C when the slurry chamber is opened at atmospheric pressure. The cooling of brine during freezing is shown in Table 3.
# Development of a Combined Orchard Harvesting Robot Navigation System
## 2 Materials and Methods
### Communication System for a Combined Orchard Harvesting Robot  
The picking and transporting robots in a combine orchard harvesting robot needs to navigate continuously during turns, and repeatedly stop between rows. The picking robot needs to ensure a minimum distance between it and the transporting robot in front of it without collision and be able to work with the transporting robot when stopped between rows. Therefore, the two robots need to communicate with each other and carry out cooperative picking and transportation based on exchanged information. In the above software system architecture for picking and transporting robots, the information interaction layer of the robots helps the master (transporting robot) and slave (picking robot) to transfer information, including GNSS coordinates, direction, speed, and navigation mode of the picking or transporting robot. The data frame format of the inter-robot communication protocol is shown in Figure 5.  
Figure 4: Software system structure of the combined orchard harvesting robot.  
Where x and y are global coordinates, the robot heading and speed are obtained by the GNSS and odometer, respectively, and the navigation mode is divided into straight ahead (0) and turning (1). The data frame format of said communication protocol consists of a start character, an identifier, a data bit, a check bit, and a termination character. The data frame starts with the \"$\" symbol and ends with the cyclic redundancy check (CRC). The CRC is divided into the robot identity document (ID), the current time, the global coordinates X, Y, the heading, the navigation mode, and an error code indicated by \",\". The identifier of the data corresponds to the number of robots, i.e., the robot ID. The ID of the transport robot is 1 and the ID of the picking robot is 2.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 3 Results
### Feature Extraction and Selection  
As discussed in the \"Feature Selection\" section, we are using the mRMR algorithm to determine the importance of the extracted features. To determine the number of features that yield the best activity recognition accuracy in general and fall detection in particular, we used six principal components as described in the \"After segmentation processing\" section. To perform the developed experiment, we trained our model multiple times by selecting a different set of features for each recorded signal. The features we selected are based on the importance score that was generated by the mRMR algorithm. The experiments' results are shown in Figure 17, which show that our proposed approach achieved its best performance when the number of used features is between 90 and 380 out of 565 features for both the hallway and the office environments. It is worth mentioning that tuning the parameters of the SVM classifier has the potential of further enhancing the performance of our proposed approach as described in the \"Data Preparation\" Section. For the office environment, the  
Figure 16: The output of each of the signal processing stages.  
feature selection results show that using 250 features out of the 565 features (i.e., 44.24% of the features) yields an accuracy of 91.23%. For the hallway environment, the feature selection results show that using 150 features out of the 565 features (i.e., 26.55% of the features) yields an accuracy of 86.4%. When the data from both environments are used to train the model, the results of the feature selection show that using 410 features (i.e., 72.56% of the features) achieved an accuracy of 88.8%.  
The aforementioned observations suggest the following: First, the number of features that yield the highest accuracy differs when the activities are performed in different environments. Second, the accuracy of the system depends on the performed activities as well as the noise in the environment. For example, when the participant moved from the office environment to the hallway environment, the accuracy of the system has decreased and the number of needed features has increased.  
The selected features with the highest mRMR score that yielded the best accuracy for both the office and the hallway environments are summarized in Table 4. We also included the features with the highest mRMR scores when we combine the data from both the office and the hallway environments.
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 2 From WBANs to BBNs
### Body-to-Body Network Concept  
A BBN is a set of WBANs that communicate with their neighboring WBANs, through the coordinator device, which plays the role of a gateway sharing the sensor data among the WBANs. Then, a BBN is theoretically a mobile ad hoc network that uses humans to transmit data within a bounded geographic zone. Thus, the wireless signal keeps transmitting from one WBAN to the nearest WBAN, using BBN-enabled Smartphones or Smartwatches, and so on until reaching the destination.  
The BBN is an emerging solution for communication between patients and the medical staff, and exchange of data between WBANs, which provides the possibility to share data in order to perform estimation, statistics or just route to a given destination. A BBN network for the monitoring of a group of cyclists is depicted by Figure 2.  
In a BBN, all users should cooperate and contribute of their network resources to relay data to the other network users, creating thus vast cooperative body-to body communications, for the great benefit of the network. In densely populated areas, a BBN could allow cellular providers to truly achieve \"anytime and anywhere\" connectivity and gigabit data rates, especially with the emergence of the IoT, giving rise to real-time applications with growing demands on data rates. For example, authors of [45] investigate IoT-based approaches to enable smart healthcare solutions, redesigning modern healthcare by using WBANs with technological, economic, and social prospects. A Health-IoT ecosystem is also formulated in [23] within a healthcare-based cloud, accessed by remote patients through an in-home healthcare station (IHHS). In a more recent study [46], an energy-efficient body-to-body communication was implemented for cloud-assisted WBANs to relay the patients' sensor data to the nearest APs in order to access the cloud resources. Indeed, the link quality between WBANs and APs deteriorates as the distance increase. Thus, body-to-body communications are necessary to maintain the connectivity and increase the data transmission rate in such situations.  
Alike the single WBAN, a BBN uses different communication standards such as 802.15.4 (Zigbee), 802.15.6, 802.15.1 (Bluetooth), 802.11 (WiFi) or 4G. Moreover, the emerging Long Range (Lora) technology [47] could be involved in the BBN space. Especially, LoraWAN is a low cost communication protocol based on chirp spread spectrum (CSS) modulation, to keep the power level characteristics, instead of the frequency shifting keying (FSK) modulation which commonly increases the communication range. LoraWAN is projected to support billions of IoT devices, and BBN could be considered as one of the promising solutions to be integrated to such systems in order to extend and enhance the connectivity of the IoT-based health services.
## 2 The Light Calibration Method for Line Structure based on Multiple Geometric Constraints  
On the basis of the original calibration model, a light plane calibration method based on multiple geometric constraints is proposed in the paper, and the constraints included the subordination relationship of the point and surface and the parallel relationship between space planes. First, the initial values of the equation parameters were solved by the calibration model of the light plane. Second, the data points were filtered by the characteristics that the data points should simultaneously be on the light plane and the plane target. Finally, several parallel light planes were obtained by moving the laser line, and the parameters of the plane equation were optimized using the parallel relationship.
# Polystyrene-Sepiolite Clay Nanocomposites with Enhanced Mechanical and Thermal Properties
## 3 Results and Discussion  
### Structural Analysis  
FTIR analyses of pristine sepiolite and modified sepiolite (m-SP) are shown in Figure 2, while the spectra of polystyrene/sepiolite nanocomposites are exhibited in Figure 3, respectively. The structural analysis of pristine sepiolite shows the characteristic peak of (H-O-H) stretching vibrations of water molecule appearing in the range of 3650 to 3070 cm\({}^{-1}\), respectively [37]. The stretching vibrations of (Si-O) are visible at 1210, 1020 and 970 cm\({}^{-1}\), while (Si-O-Mg) shows bending vibrations at 552 cm\({}^{-1}\), which can be attributed to octahedral-tetrahedral linkage [38]. Further, the characteristic peaks of sepiolite clay also appear at 1650, 1210, 1020 and 690 cm\({}^{-1}\). The modified sepiolite (m-SP) spectra show the broadening of O-H stretching vibrations in the region of 3510 to 3650 cm\({}^{-1}\) and a bending vibration of O-H at 1670 cm\({}^{-1}\), which can be associated with the successful modification of the sepiolite surface [39]. The Si-O vibrations were also observed at 1210, 954, 771, 695 and 670 cm\({}^{-1}\) in both pristine and modified sepiolite spectra [40; 41]. The siloxane linkage (Si-O-Si) stretching vibrations are visible at 1015 and 471 cm\({}^{-1}\). The characteristic peak of Si-O-Mg can also be observed at 525 cm\({}^{-1}\)[42]. As the modification was performed with VTES, a small peak at 2971 cm\({}^{-1}\) is present, confirming the asymmetrical stretching vibration of the vinyl C-H group.  
Figure 2: FTIR spectra of pristine sepiolite and modified sepiolite (m-sepiolite) clay.  
The FTIR spectra of pristine polystyrene and polystyrene-sepiolite nanocomposites with varying compositions, are shown in Figure 3. Pristine PS (S0) shows the characteristic peaks of C-H stretching vibrations at 3050 and 2810 cm\({}^{-1}\) for aliphatic and aromatic carbon, while three absorption bands of aromatic C=C stretching vibrations also appear at 1610, 1580 and 1450 cm\({}^{-1}\)[43]. The corresponding bending vibrations in the fingerprint region of 770-789 cm\({}^{-1}\) confirm the substitution on the benzene ring due to C-H being out of plane [44].  
In Figure 3, PS/m-sepiolite nanocomposites show the absorption bands in the range of 3000-3300 cm\({}^{-1}\), attributed to the O-H stretching vibration [45]. The characteristic peaks of carbonyl C-O in the range of 1600 to 1712 cm\({}^{-1}\) are sharper and more prominent in the spectra of S2 and S4 with higher filler contents in the nanocomposites. A sharp band at 1075 cm\({}^{-1}\) due to Si-O-C appearing in polystyrene nanocomposites confirms the interaction between the Si-OH group of sepiolite and polystyrene [46]. All the related peaks of polystyrene and sepiolite are present, as in the pristine samples.