esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Object Identification and Tracking
### Object Mapping  
In this section, the results of the previous systems are visualized, where objects were identified and tracked over time. At this stage, a unique color is assigned to each recognized object to enhance clarity and facilitate interpretation. Each color represents a unique person, showing their positions, and trajectories in a bird's-eye-view format. These data are crucial for both the neural network and the robot's decision-making processes. To facilitate visualization, the coordinates are scaled from meters to pixels using a fixed conversion factor, where 1 m corresponds to 100 pixels.  
The experimental results of the radar system for identifying individuals and tracking their movements are illustrated in Figure 4. This figure represents Test 1 and highlights the intermediate steps required to achieve accurate object tracking. The process begins with data collection presented in Figure 4a, followed by the visualization of the point cloud captured by the radar in Figure 4b. This point cloud is subsequently used for filtering within the windows, as shown in Figure 4c. Finally, the trajectories are displayed, with each object assigned a distinct color, as illustrated in Figure 4d.
## 7 Discussion and Results
### PCA-SRP + ANN Comparison Accuracy Testing  
We compared the validity model accuracy using PCA-SRP in an ANN classification problem as suggested by the results presented in Figure 20 and determined its effect upon being subjected to noise and random samples. Tables 6 and 7 display the validation accuracy using ANN + PCA-SRP and ANN only, showing a significant increase as shown in Figure 21 for both noise and random samples.  
The difference between dataset accuracy subjected to noise and random samples are the definition of the accuracy curve line between ANN + PCA-SRP to ANN only,  
Figure 21: Validation model accuracy of the given datasets.  
even though both of them have a significant accuracy increase. The accuracy of datasets under the influence of random samples swings and deviates more and is less defined in comparison with accuracy with noise samples.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 4 The Non-Cross Layer Design Method
### The Propagation Delay  
Due to the high attenuation ratio and low propagation speed of acoustic waves in the underwater environment, the propagation delay is a serious problem in UASNs. Reducing the propagation delay also can reduce the energy consumption. Routing protocols which have small propagation delays also have good energy consumption performance.  
In underwater acoustic sensor networks, one cause of propagation delays is that the propagation speed of acoustic waves is slow, so in [56], the authors propose underwater wireless hybrid sensor networks (UW-HSNs). In UW-HSNs, the communication is divided into two parts: radio communication (which is used to communicate with the base station on the surface) and acoustic communication (which is used for underwater communication with other nodes). The UW-HSN is effective and has small transmission delays. However, the nodes in UW-HSN must be equipped with both radio communication modules and acoustic communication modules, which is not energetically optimal.  
Considering the propagation delay in routing discovery and data transmission, the hop-by-hop dynamic addressing-based routing protocol (H2-DAB) [57] and information carrying routing protocol (ICPR) [57] are proposed. In H2-DAB, the data packets are transmitted hop-by-hop; the algorithm does not need any special devices or information to build the routing path. A novel routing algorithm named ICPR has been proposed in [58], where the routing discovery and data transmission are processed simultaneously, _i.e._, transmitting data packets while building the routing. ICPR is time saving and can greatly reduce the propagation delay. In [59], a routing protocol named low propagation delay multi-path routing protocol (MPR) is proposed. In MPR, all the nodes can send data packets to surface sink nodes; the source nodes divide the data packets into several time slots based on the bandwidth and use a two-hop transmission scheme to transmit data packets to the relay nodes. In MPR, multi-path is utilized during the path construction from the source node to the destination node, which can avoid data collisions in receiving nodes.  
In this section, two methods are used to reduce the propagation delay: one is to separate the overwater communication and the underwater communication [56]; another one is to reduce the processing time in routing discovery and data transmission [57; 58; 59]. All these routing protocols have good propagation delay performance, but the problem is that the energy consumption in these routing protocols is much higher than in other routing protocols due to the heavy overhead [57; 58; 59] or the high energy consumption in the radio communication module.
## 1 Introduction
A similar hybrid system (combined membrane bioreactor and solar Fenton oxidation) with significant COD removal efficiencies (69-85%) was also examined by [PERSON] et al. [31; 32; 33], but for lower initial COD concentrations (120-210 mg/L). [PERSON] et al. [34] examined the use of long-term, aerated storage combined with coagulation/flocculation and recorded a COD-soluble removal performance of about 87.9%, using WW with an initial COD concentration of over 25,000 mg/L. The combination of activated sludge and chemical processes with significant COD and polyphenol abatements (above 60% and 95%, respectively) was examined by [PERSON] et al., [35] who treated WW with a high initial COD concentration (about 139.25 g/L). Photocatalysis as a post-treatment step after adsorption on bentonites was tested by [PERSON] et al. [18]. In this study, COD removal reached 58.2% for initial total COD values of 12,400 \(\pm\) 700 mg/L. Finally, [PERSON] et al. [36] studied three treatment stages comprising coagulation, a Fenton-like process and biological treatment using activated sludge. The final COD reduction was 74% (145 mg/L) when applying WW with an initial COD concentration of about 5180 mg/L. Although aerobic biological treatment of WW is considered a low-cost method [3], it cannot be used as a single treatment technique, since it leads to inefficient COD removals, especially when treating WW with high initial COD concentrations [16; 29; 35]. For this reason, biological treatment has been examined in combination with other methods, mainly advanced oxidation processes, as pre-treatment or post-treatment steps [16; 37]. CWs have been used for the treatment of WW [38; 39; 40; 41; 42; 43], but their use as an integrated treatment solution for all winery wastes (wastewater, slurry sludge, solid wastes, etc.) has not been thoroughly studied or implemented. According to [PERSON] et al. [41], WW treatment is mainly approached by using extensive aerated ponds, sequencing batch reactors (SBRs) or activated sludge plants, and then adding a tertiary treatment stage comprising CWs for sludge treatment and polishing. To the best of our knowledge, biological trickling filters have not been examined in combination with CWs for the treatment of real WW. Only [PERSON] et al. [44] examined the performance of a vertical-flow-constructed wetland with a trickling filter; however, they applied a mixture of domestic sludge and WW, and also used a further step of chemical precipitation. The objective of this study was to examine the efficiency of a hybrid system comprising a biological trickling filter and a horizontal subsurface flow (HSF)-constructed wetland to treat real winery wastewater originating from a local winery. Experiments in a pilot-scale trickling filter were carried out using different WW feed concentrations (3500-18,000 mg/L) and two different recirculation rates (0.5 and 1.0 L/min). In addition, the possibility of pairing biological treatment in attached growth systems with CWs was investigated for the complete mineralization of WW.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 4 Case Application
### Calculation of Track Prediction Error
#### 4.1.4 Prediction Error Calculation  
Through repeated predictions, it is summarized that the prediction error follows a normal distribution, as shown in Figure 9, where the Mean Absolute Error (MAE) is used to compute the average absolute variance between the predicted value and the actual value.  
The fitted probability density function for the track prediction error is \(N\)(5.47, 1.252).
## 5 Performance Testing
### Transmission Interval Test  
We aimed at determining the accuracy performance differences between different transmission intervals and balance accuracy with battery usage. An identical setup in Room ZB214 with a 10 s collection time was used in this test (Figure 8), and the transmission interval effects on accuracy were tested by altering the settings of the iBeacons. Three transmission intervals of 100 ms, 417 ms and 800 ms were tested. Transmission intervals of over 1000 ms were not used because of potentially unacceptable delays for real-time location-based service applications, such as adaptive learning and exhibitions. For the iBeacon used, 417 ms was the default transmission interval, and 100 ms was selected as a potential improvement over the default in terms of positioning accuracy.  
As the increase of the transmission interval reduces the power consumption of iBeacons, we aimed to locate a balance between accuracy and power consumption. Thus, the experiment was conducted in the same room (ZB214) with 12 iBeacons. The hypothesis in this test was that reducing the transmission interval would improve the positioning accuracy, due to a greater number of Bluetooth signals in a fixed period of time. We expected that a short transmission interval (i.e., 100 ms) would have better performance than long transmission intervals (i.e., 417 ms and 800 ms) because of the high number of signal packets. Yet, the CDF of RMSE did not support this hypothesis (Figure 13). The CDF curves of 100 ms and 800 ms interval were very similar, and most of the parts overlapped. The differences between their overall RMSE were only 0.01 m, where the RMSE of 100 ms interval was 1.81 m and the RMSE of 800 ms was 1.80 m. As shown in Figure 13, the interval of 417 ms outperformed significantly and the overall RMSE was only 1.54 m.  
Figure 12: CDF comparison of the collection time by RMSE under different collection times (in seconds).  
One of the main advantages of the BLE protocol over standard Bluetooth is its lower power consumption over the same range, enabling battery life extensions of months to years. When altering the transmission interval of the iBeacons, the battery life is affected. Frequent transmissions lead to shorter battery life, whereas less frequent transmissions prolong it. Our results show that decreasing the transmission interval to 100 ms was actually detrimental to the positioning accuracy, relative to the default setting of 417 ms. Furthermore, this resulted in significantly faster depletion of the iBeacon battery. We recommend that the transmission interval be set at 417 ms, as this ensures a long battery life and provides two signal packets per second, which can fulfil the requirements of most usage scenarios.
## 3 CENTERA: Our Centralized Trust-Based Efficient Routing Protocol with Authentication
### Bad Nodes Isolation Block  
After calculating the quality metrics and trust values for all the nodes, the Bad Nodes Isolation Block is initiated. Any node detected as bad in the previous block will be isolated from the network for a number of activity periods according to its Data Trust (dtrust) level and its banNum value. The banNum is an indicator showing the bad level of the node through time. BanNum is set to one for all nodes and is incremented every time a node is detected as bad.  
This block utilizes an effective and efficient method to isolate the detected bad nodes based on its history and current actions according to the following:  
* if (dtrust \(>\) 0.8) then banRem = 1
* else if (dtrust \(>\) 0.7) then banRem = 2
* else if (dtrust \(>\) 0.6) then banRem = 3
* else if (dtrust \(>\) 0.5) then banRem = 4
* else if (dtrust \(>\) 0.4) then banRem = 5
* else if (dtrust \(>\) 0.3) then banRem = 6
* else if (dtrust \(>\) 0.2) then banRem = 7
* else banRem = 8  
The BS increments the number of successive good periods for every active node not detected as bad nor put on probation in this block. When this number reaches a certain threshold (preset by the administrator in the Initialization Block), the BS rewards the node by decrementing its banNum if it were greater than one, or decrementing its probation number otherwise.
## 6 Open Issues and Future Research Directions
### Energy Consumption  
Most commercial UAV applications rely on batteries for routing, data transmission, data processing, UAV mobility, and payload applications. As a result, the flight length of UAVs for specific missions is restricted, and UAVs often leave and rejoin a network for energy replenishment, which has a direct influence on the network's communication performance. As a result, while developing routing protocols for FANETs, the energy level of the UAVs must be taken into account to keep the UAV connections stable. However, only a tiny percentage of contemporary FANETs routing systems take energy into account as a routing measure. As a result, energy-conscious routing and energy preservation require more research work. For instance, in FANTs, a cluster-based routing protocol is employed to decrease energy consumption [117]. Furthermore, optimizing the performance of standard protocols such as LEACH can be adopted in FANETs to provide energy efficiency routing protocols [118].
## Materials and Methods
### Measurements  
The USANS data were measured at the S18 interferometry and USANS facility of the Institut Laue-Langevin, Grenoble, France (Figure 1).  
The non-polarized neutron beam from the top left is diffracted by M to pass the sample S at normal incidence. Portions of the beam that are scattered by S will be diffracted by analyzer A and detected by detector D\({}_{1}\) at angular positions of A slightly different from the Bragg position \(\theta_{\rm B}\). The accuracy of the piezo system controlling A's angular position is 0.036\". The \(Q\)-values in our USANS experiments refer to variation of A's angular position and are calculated from measured absolute piezo encoder values. Low-background conditions and the excellent suppression of diffraction-peak side lobes enable us to perform measurements at \(Q\)-values as small as a few \(10^{-5}\) 1/A. The wavelength of the incident beam was 1.9 A. Neutrons are counted in detectors D\({}_{1}\) and D\({}_{2}\) as a function of angular position of A with and without sample. The values are normalized by the orders of magnitude larger count rate at a monitor detector upstream to account for power fluctuations of the neutron source (reactor) during the measurement. The scattering curve measured without sample was interpolated and subtracted from the curves measured with samples.  
The dc magnetic field is applied vertically (\(z\)-direction) and it is in the plane of MAE samples. It can be varied from 0 to 350 mT. The neutron beam is directed perpendicular to the sample's plane and to the magnetic field vector. No visible deformation of MAE samples in the maximum magnetic field has been observed. The magnitude of the scattering vector \(Q\) refers to the horizontal plane. It can be expected that the restructuring (formation of elongated aggregates) occurs in the vertical direction and we mainly observe the scattering in the perpendicular direction. Unfortunately, due to the specific features of the USANS setup, it was not possible to record the 2D scattering patterns, because these measurements would be prohibitively long.
# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
## 3 Network Architecture  
The MEDIWARN system, as shown in Figure 1, includes patients, doctors, sensors, monitors, some wireless nodes (including mobile handheld devices), the MEDIWARN Virtual Biosensor and a monitor station.  
The sensors acquire the physiological parameters of the patient. Two kinds of vital parameters are considered, i.e., both waveforms and single values.  
The sensory data are collected and shown on a monitor close to the patient. The monitor is equipped with a wireless transceiver and transmits the vital parameters to the MEDIWARN Virtual Biosensor for data processing.  
The wireless network consists of a number of wireless nodes, organized in a mesh topology, that act as relay nodes in the data exchange between the monitors and the MEDIWARN Virtual Biosensor.  
The MEDIWARN Virtual Biosensor (Figure 2) is the heart of the proposed model and consists of a dedicated server that stores and processes the vital parameters of the patients. Such a server runs four software components:  
* A database that stores all the vital parameters sampled from the patients and the processing results.
* A data acquisition component that is in charge of the communication with all the monitors, which collects the vital parameters sampled by the monitors and stores them in the database.
* The fuzzy algorithm, which communicates directly with the database, reads the vital parameters, processes data and stores the results in the database.
* The web portal, i.e., a web server that maintains the GUI for the workstation and the mobile devices held by the medical staff. The traffic of mobile devices is web traffic with no real-time guarantees.  
The medical team can remotely monitor the patients through the mobile devices, which show the patients' current clinical status. Moreover, the medical staff is promptly informed of any patient's condition deterioration through an alarm that is sent by the  
Figure 1: The MEDIWARN logical network architecture and the system components.  
Figure 2: The MEDIWARN Virtual Biosensor software architecture.  
MEDIWARN Virtual Biosensor to the mobile devices. This way, the medical staff members are alerted by an acoustic signal generated by their mobile devices.  
The monitoring station shows a detailed view of each patient's conditions, while the mobile handheld devices (e.g., tablets) of the medical staff provide a summarized view of the patients' status.  
The MEDIWARN communication system involves a wireless network architecture such as the one in Figure 1 for each hospital ward. In the case multiple hospital wards would use the MEDIWARN system, the wireless networks should exchange data through wired technologies (e.g., Ethernet).
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 6 Performance Evaluation
### Link Quality-Based Forwarding Strategies  
For the evaluation of the forwarding strategies described in Section 4, the delivery ratio, the mean end-to-end (E2E) delay, and the total network overhead are presented. The proposed strategies are also compared with the well-known Epidemic strategy where each node broadcasts the packet to maximize the delivery ratio and minimize the end-to-end delay, making this a flooding-based protocol. As a result, constraints such as nodes' buffer size limitation restrict the performance of this strategy. All strategies are evaluated considering the DTN-based architecture mOVE [23].  
Figure 21 shows the delivery ratio obtained by each forwarding strategy in Scenario A, for simulated (A1), laboratory (A2) and real environment (A3). As expected, the Epidemic protocol has the highest delivery ratio, because data packets are broadcasted to all nodes in the network without any control. By comparing the three link quality-based strategies with the Epidemic, we can see that PAmuLQE-B-E2E and PAmuLQE-NACK achieve the same delivery ratio, which is also the same as the one achieved by the Epidemic.  
In agreement with Figure 21, strategies with lower delivery ratios are the ones with higher delivery delay. We observe in Figure 22, where the network E2E delay is illustrated, that PAmuLQE-U-E2E has a higher delay. This is due to acknowledgement messages being sent as data packets, thus having a larger amount of data to send in the storage, delaying the actual data packets.  
Regarding the network overhead, Figure 23 shows, as expected, that all proposed variants have lower network overhead when compared to Epidemic, since they do not transmit data packets in broadcast mode. In other words, the proposed variants have a lower replication rate.  
Figure 21: Delivery Ratio for Scenario A. The plots represent the ratio between the delivered data packets to a gateway and the overall data packets.  
the simulated results have smaller variance (and confidence intervals) than the real results, due to the absence of random factors experienced during the real experiments, such as environmental conditions.
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 3 Numerical Simulation Scheme of Dust Removal Fan
### Performance Evaluation Index of Dust Removal Fan  
The performance evaluation index of a dust removal fan mainly includes total pressure efficiency, static pressure efficiency, and dust removal efficiency. The calculation methods for total pressure, static pressure, driving impeller shaft power, total pressure efficiency, static pressure efficiency, and dust removal efficiency are as follows:  
Total pressure of fan:  
\[\Delta p=p_{2}-p_{1} \tag{8}\]  
where \(\Delta p\) represents total pressure of the fan, Pa; \(p_{1}\) represents fan inlet total pressure, Pa; and \(p_{2}\) represents fan outlet total pressure, Pa.  
Static pressure of fan:  
\[\Delta p_{st}=\Delta p-p_{d2} \tag{9}\]  
where \(\Delta p_{st}\) represents static pressure of the fan, Pa; \(p_{d2}\) represents fan outlet dynamic pressure, Pa.  
Shaft power of the fan:  
\[P=\frac{T\times n}{9550} \tag{10}\]  
where \(P\) represents the shaft power of the fan, kW; \(T\) represents impeller torque, N\(\cdot\)m; and \(n\) represents impeller speed, r/min.  
Total pressure effective power of fan:  
\[P_{e}=\frac{\Delta p\times q_{v}}{1000} \tag{11}\]where \(P_{e}\) represents the total pressure effective power of the fan, kW; \(q_{v}\) represents fan flow, m\({}^{3}\)/s.  
The total pressure efficiency of the fan:  
\[\eta=\frac{P_{e}}{P}=\frac{\Delta p\times q_{v}}{1000P} \tag{12}\]  
where \(\eta\) represents the total pressure efficiency of the fan.  
Static pressure effective power of the fan:  
\[P_{est}=\frac{\Delta p_{st}\times q_{v}}{1000} \tag{13}\]  
where \(P_{est}\) represents the static pressure effective power of the fan, kW.  
Static pressure efficiency of fan:  
\[\eta_{st}=\frac{P_{est}}{P}=\frac{\Delta p_{st}\times q_{v}}{1000P} \tag{14}\]  
where \(\eta_{st}\) represents the static pressure efficiency of the fan.  
Dust removal efficiency of fan:  
\[\eta_{c}=1-\frac{c_{2}}{c_{1}} \tag{15}\]  
where \(\eta_{c}\) represents the dust removal efficiency of the fan, \(c_{1}\) represents discrete phase content at fan inlet, and \(c_{2}\) represents discrete phase content at the fan outlet.