esa-sceva/satcom-chunk-collection huggingface dataset:
# A Collaborative UAV-WSN Network for Monitoring Large Areas
## 4 Discussion  
In this paper we proposed a hybrid UAV-WSN system, which, compared with [19], improves the monitoring of large areas and is dedicated especially to agricultural applications. Monitoring of large geographical areas require efficient data collection strategies, based on collaborative entities, able to overcome the challenges faced and achieve an improved overall system. The main challenges introduced by sparse WSN deployment, which creates an uneven distribution of sensing nodes, were pointed out. Starting from the hypothesis of an unknown sensor distribution over the area of interest, we have defined a strategy that starts with a photogrammetry procedure in which the surface of interest is crossed repeatedly by an UAV to estimate the sensors' position and receive information required for cluster grouping. Once this data becomes available, a scoring system can be called through which most suitable sensor nodes are proposed for clusters' head positions. The next step consists of running the clustering algorithm within the WSN nodes. Then we plan a trajectory which passes through the neighbourhoods of the cluster heads with guaranteed communication time and guaranteed obstacle avoidance.  
As stated earlier, the main interest of this paper lies in the interplay between various components of the overall heterogeneous WSN surveillance. In particular, we are interested in how the trajectory generation procedures combine with the WSN communication restrictions. Beyond the directions studied in this paper we may consider as being worthwhile for further study:  
* The data acquired from the initial, sensor localization, trajectory may be used in the configuration of the sensor clusters (to account for geometrical restrictions: line of sight; distance between nodes, etc.).
* Dynamic reconfiguration of the data-gathering trajectory: each cluster provides a list of potential cluster heads and multiple trajectories are computed (for various combinations of cluster heads).  
Figure 16: RSSI for each unicast along the experimental run.  
Ultimately, we may consider a WSN in which the ground network and the UAV collaborate dynamically to create clusters and design the data-gathering trajectory (with the result depending and being updated by the current UAV position, sensor battery charge and amount of data to be transferred within the network).  
All of the above require advanced optimization tools in order to handle the (usually conflicting) constraints. This raises interesting questions of memory and power resources; feature extraction and communication restrictions (capacity, safety and the like).
# Comparison of Heat Transfer Enhancement Techniques in Latent Heat Storage
## 4 Discussion  
Data on the geometry, thermal properties, and performance under specific conditions of a wide range of technological approaches to LHES were gathered and used to estimate NHITPC and PCM volumetric fractions. Figure 6 summarizes the LHES units in each category to enable comparison of performance across all technologies.  
Considering the overall trends when looking at the calculated KPIs, some general observations can be drawn:  
* Finned tube bundles showed a wide range of performance, and volumetric fractions of PCM. The difference among the heat exchanger structures ranged mainly on terms of tube bundle geometry and fin density. If designed properly, they have a very high potential both in terms of heat transfer and resulting capacities.
* AHEX based units showed the highest average performance parameters across all technologies. This coupled with the advantages of mass production, potential for modularity, and variety of configurations makes them interesting for further study. Long-term stability and issues with flexibility of existing designs will have to be addressed before they are proposed for implementation in commercial products.
* Additionally, CTBs showed the potential to attain performances comparable to average performing finned tube bundle geometries while using a minimal share of the volume in a unit. However, the quality of the outlet temperature stability and round trip efficiency due to required pumping power at low diameters, as well as particularities regarding material compatibility and consequential limited temperature range, persist as some of the challenges for their implementation.
* Metallic foams and carbon-based TCE of all forms showed promising potential and further experimentation on optimized LHES unit structures, or simulations are required to properly assess their thermal performance.
* In contrast, among all the categories, the macroencapsulation techniques considered showed the lowest average NHITPC and \(\Phi_{PCM}\) pairs. However, the studied units were not conceived or optimized for high power applications and used relatively large capsules with a generic geometry. A large room for improvement remains untapped regarding the optimization of capsule shape and size to enhance both heat transfer performance and compactness.
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 5 Tropospheric Model Solution  
In this paper a model considering the signal delay crossing the troposphere space has been implemented into the software simulator.  
The troposphere is the lowest layer of the atmosphere and extends from the ground surface up to about 8 km at the pole and up to about 17 km at the equator. It contains about 75% of the total atmospheric mass. It is full of water vapor and varies in temperature and pressure. The troposphere is a refractive medium, which influences the GNSS signals propagation, see Figure 2. The troposphere induces an excess propagation path length on the GNSS signals given by the difference between the time taken by the signal to traverse the refractive medium, having a refractive index \(n\), and the time it would have taken in vacuum conditions. Much of the delay caused by a signal's trip through the atmosphere can be predicted. Atmosphere mathematical models consider different aspects regarding particles and gas composing the troposphere. In order to realize the purpose of the paper, a well-known model was selected to calculate the effects of the signal based on the parameters available in the simulator.
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 7 Radar-Based Methods
[176] reported in 2013 on their results improving the signal quality of CW Doppler radar vital sign tracking at 2.4 GHz using the MUSIC (Multiple Signal Classification) algorithm. For automotive applications, various sensor locations are possible, including integration into the cockpit, the safety belt, the steering wheel and into the backrest (see Figure 15). In 2015, [PERSON] et al. proposed using continuous wave (CW) radar sensors for monitoring vital signs while driving [177]. The authors of this paper investigated two measurement scenarios: in the first scenario, a 24 GHz six-port configuration of the radar system is placed approximately half a meter in front of the driver, with the antenna pointing directly on its thorax. This distance between sensor and driver would allow future integration of the antenna, for example into the steering wheel. In the second measurement scenario, the same radar sensor has been installed on the backrest of the car seat (which is more difficult for such high frequencies, as mentioned above). Here, the authors intend to create a \"biometric driver seat\" while at the same time eliminating the necessity of a sensor integrated in the steering wheel. Therefore, the radar antenna is not aiming on the thorax of the person under test, but on his or her back. A similiar approach was proposed by [PERSON] and colleagues in 2016 [178]. Similiar to [PERSON]'s second scenario, the authors proposed to place a 24 GHz CW sensor in the backrest of the car seat. These authors also point out that, using CW radar, motion artefacts may significantly interfere with the frequency characteristics of the displacement signal and may hence be difficult to extract by signal analysis in the frequency domain (FFT). Figure 15: Potential locations of radar sensors inside a car. In 2016, [PERSON] et al. presented a 24 GHz microwave Doppler sensor circuit placed on the chest of a volunteer sitting in a car seat. As demonstrated, the location is proper for seat belt integration [179]. Very recently, [PERSON] and colleagues [180] demonstrated feasibility of heart and respiratory rate tracking using a new UWB radar design placed in the backrest of a car seat. A _Ventricor Impulse Radar_ module manufactured by the Norwegian company Novelda (Novelda AS, Gjerdrums vei 8, 0484 Oslo, Norwegen) was employed. In order to increase the penetration depth of the electromagnetic impulses, the authors chose a carrier frequency of 3.9 GHz using pulses of approximatively 2 GHz bandwidth.
# Design and Analysis of Systematic Batched Network Codes
## 3 Systematic Outer Codes
### Computation Cost
#### 3.3.1 Encoding Computation Cost  
The encoding computation cost depends on the number of coded packets generated. For the ordinary outer encoding, the computation cost of encoding \(k\) packets is \(kb\), where \(k=1,2,\ldots\). For the systematic outer code, we assume \(n_{s}M=K\) (we will discuss how to design such a code). As the first \(K\) packets are the message packets, the encoding of the first \(K\) packets requires no computation. To encode more packets, the systematic outer code needs to execute \(\text{DEC}^{*}_{n_{s}}\), which has a computation cost \(c\), and \(\text{ENC}_{n_{s}^{+}}\), which takes computation cost \(b\) on average to generate a packet. Therefore, when \(k>K\), the computation cost of generating the first \(k\) coded packets using the systematic outer code is \((k-K)b+c\approx kb\). See the illustration in Figure 3a regarding the computation cost of generating the first \(k\) packets.  
Figure 2: Illustration of the approach to systematic outer codes. (**a**) shows the normal use of a consistent pair of the outer code encoder \(\text{ENC}_{n_{s}}\) and decoder \(\text{DEC}_{n_{s}}\). (**b**) shows the encoding of the systematic code, where \(\text{ENC}_{n_{s}^{+}}\) is the outer code encoder that generates the coded packets beyond the first \(n_{s}\) batches. (**c**) shows the decoding of the systematic code, where \(\mathbf{Y}^{\prime}\) is the received coded packets generated by inner coding.  
To further understand how the encoding computation cost affects the operation at the source node, we consider two models of message packet arrival at the source node. In the first model, the message packets arrive one-by-one with a unit time interval between two consecutive packets. The ordinary outer code encoding can only start to generate the first coded packets from the time \(K\) when a precode with HDPC is employed. Let \(\Delta\) be the time taken by the ordinary encoder to generate \(K\) coded packets, where \(\Delta\propto Kb\approx c\). The systematic outer code can generate a coded packet upon the arrival of each message packet. At the time \(K\), the systematic outer code executes \(\text{DEC}^{*}_{n_{s}}\), which also takes \(\Delta\) time. In the second model, all the \(K\) message packets arrive together at the same time, e.g., time \(K\). For this model, the ordinary outer code behaves in the same way as for the previous model, and the systematic outer code can generate the first \(K\) coded packets at time \(K\).  
We see that for both message packet arrival models, the systematic outer code generates the first \(K\) packets earlier than the ordinary outer code. When \(k>K\), both encoders generate the \(k\)th packet at the same time. See an illustration of this in Figure 3.
# Wet Snow Flashover Characteristics of 500-kV AC Insulator Strings with Different Arrangements
## 3 Results
### The Flashover Test of II-String  
In this study, wet snow flashover tests of II-string with different distance between two insulator strings were performed to find an appropriate insulator distance in wet snow conditions.  
Figure 10: The arcs of \(\Lambda\)-string and V-string with anti-icing shed. (**a**) The 60\({}^{\circ}\)\(\Lambda\)-string, (**b**) The 60\({}^{\circ}\) V-string.  
Figure 9: The comparison of voltage change ratio \(\beta\) between \(\Lambda\)-string and V-string.  
For insulator strings with common sheds, it was observed that when the distance between two high-voltage terminals is not less than 600 mm, the development of flashover arcs is relatively independent. From the test results, it can also be confirmed that the flashover voltages of two arrangements, 650 mm II-string and 2\({}^{\circ}\)\(\Lambda\)- string (450-600 mm string), are almost the same due to the absence of floating arcs. Because of an increased flashover probability, as mentioned above, their flashover voltages are 1.3%-2.4% lower than those of single strings.  
For the insulator string with anti-icing sheds, examples of discharge paths are shown in Figure 11. From the experiments, it was noticed that there were always multiple floating arcs bridging two insulator strings for the 450-mm II-string arrangement. These floating arcs shorten the discharge path greatly and lead to lower flashover voltages among all arrangements in this study.  
For the 450-600 mm II-string arrangement, there was only one flashover arc floating between to insulators as shown in Figure 11b, making the flashover voltage higher than that of the 450 mm II-string arrangement.  
Above all, a longer distance between two insulator strings can prevent floating arcs. Whether the flashover arc of an individual insulator can develop relatively independently or not is a key factor to judge whether the distance between the two insulator strings is appropriate.  
According to the test results, it is suggested that, for the insulators under study, the distance between the two high-voltage terminals should be at least 600 mm for the insulator string with common sheds and at least 650 mm for the insulator string with anti-icing sheds.
## 3 Results and Discussion
### RT-qPCR Results
In our findings, the down-regulation of survivin and C-myc expressions, along with the induction of Caspase-3 expression, suggest that combined treatment with L-methioninease and etoposide has the potential to induce apoptosis and inhibit the survival and proliferation of glioblastoma cells. These findings support the therapeutic potential of L-methioninease and the combination treatment approach in oncology. Although L-methionineinase shows promise as a targeted therapy, its effectiveness can be influenced by the complexity of glioblastoma tumors and the tumor microenvironment. Challenges include tumor heterogeneity leading to varied responses, limitations in delivering therapeutic agents due to the blood-brain barrier, and potential interference from the immune system, which could degrade or neutralize enzymes. To address these challenges, a comprehensive understanding of glioblastoma biology, its microenvironment, and the  
Figure 23: The relative expression of Survivin (**a**), C-myc (**b**), Caspase-3 (**c**). Survivin and C-myc expressions were decreased by L-methionineinase and Etoposide. The combination of both resulted in a maximum reduction in expression. On the contrary Caspase-3 expression increased likewise. The combination of L-methionineinase and etoposide resulted in a maximum increase in Caspase-3 expression (\(p<0.001\)). interactions between cancer cells and the surrounding cells is needed. This knowledge could lead to the development of combination therapies that target multiple aspects of tumor growth and resistance mechanisms. Additionally, advancements in drug delivery techniques can improve the delivery of L-methioninease or other therapeutics to the tumor site.
## 3 Materials and Methods  
### Collection of Naturalistic Driving Data  
Intelligent transportation systems (ITSs) can be pushed forward by V2X communications with cooperative operations. Vehicle-to-everything (V2X) communication technology, which encompasses vehicle-to-vehicle (V2V), vehicle-to-pedestrian (V2P), and vehicle-to-infrastructure (V2I) communication, is widely recognized as the fundamental building block for the development of intelligent vehicle infrastructure cooperative systems (IVICSs), because it has great potential to enhance road safety and optimize traffic efficiency [46].  
To accelerate research on how to better utilize V2X information for autonomous vehicle, DAIR V2X datasets are jointly built by Tsinghua AIR and Baidu Apollo. The DAIR-V2X datasets consist of a series of real-world vehicle-infrastructure cooperative datasets, including the following: DAIR-V2X, a large-scale multi-modal multi-view 3D object detection dataset for research on vehicle-infrastructure cooperative autonomous driving, which comprises a total of 71,254 frames of image data and 71,254 frames of point cloud data; Rope3D, focusing more on roadside pure-vision monocular 3D target-detection tasks, which includes a total of 50,009 frames of image data; and V2X-Seq, a large-scale sequential vehicle-infrastructure cooperative dataset, which include the sequential perception dataset and the trajectory prediction dataset [47].  
To push forward the cooperative sequential perception and forecasting, a large-scale sequential V2X dataset, V2X-Seq, was created. All elements of this dataset were captured and generated from real-world scenarios. V2X-Seq is the first sequential V2X dataset that includes such a large-scale scenario, making it an ideal resource for developing and testing cooperative perception and forecasting algorithms.  
The V2X-Seq trajectory forecasting dataset (V2X-Seq TFD) comprises 210,000 scenarios, including 50,000 cooperative-view scenarios, all trajectories, and traffic lights, which are captured and generated from naturalistic driving scenarios [48].  
* This dataset contains 80,000 infrastructure-view scenarios, 100,000 vehicle-view scenarios, and 50,000 cooperative-view scenarios mined from 672 h of data from 28 intelligent perception intersections in the Beijing High-Level Automated Driving Demonstration Area.
* For research convenience and privacy protection, all trajectories, and traffic lights are uniformly transformed to a virtual-world coordinate system, and the origin of the virtual-world coordinate system is located at a random point on earth, with the x-y plane parallel to the ground plane, and the z axis positive in the upward direction, shown in Figure 1.
* Each scenario contains 10 s with 10 Hz from 8 classes of object tracks generated automatically from raw sensor data, which includes Car, Truck, Van, Bus, Pedestrian, Cyclist, Tricyclist and Motorcyclist.
## 2 Related Work
### Segmentation of Ultra-High Resolution Images: Efficiency and Quality  
As the dependency on image segmentation for real-time/low-latency tasks increases, the need to efficiently and qualitatively perform image segmentation on ultra-high resolution images becomes paramount. ENet [34] successfully reduces floating-point computations by adopting an asymmetric encoder-decoder structure and early downsampling. ICNet [12] integrates multi-resolution feature maps for model compression to enhance efficiency. Recently, context aggregation has been a key tactic for overcoming the difficultiesassociated with ultra-high resolution image segmentation jobs. ParseNet [35] pools scene contexts globally at various levels to apply context aggregation techniques. To aggregate global contextual and high-resolution details, the deep/shallow branches were integrated into ContextNet [36], BiSeNet [37], and GUN [38]. However, these models are not specifically tailored for ultra-high resolution images. The challenge of balancing memory usage and segmentation accuracy remains unresolved. In contrast to the aforementioned studies, our objective is to develop a customized model that tackles the challenges in ultra-high resolution image segmentation tasks.
# Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions
###### Abstract  
Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount importance for both User Equipment (UE) to achieve battery prologue and base stations to achieve savings in power and operation cost. This paper presents an exhaustive review of power-saving research conducted for 5G and beyond 5G networks in recent years, elucidating the advantages, disadvantages, and key characteristics of each technique. Reinforcement learning, heuristic algorithms, genetic algorithms, Markov Decision Processes, and the hybridization of various standard algorithms inherent to 5G and 5G NR represent a subset of the available solutions that shall undergo scrutiny. In the final chapters, this work identifies key limitations, namely, computational expense, deployment complexity, and scalability constraints, and proposes a future research direction by theoretically exploring online learning, the clustering of the network base station, and hard HO to lower the consumption of networks like 2G or 4G. In lowering carbon emissions and lowering OPEX, these three additional features could help mobile network operators achieve their targets.  
5G; energy efficiency; sleep modes; renewable energy; traffic offloading; clustering +
Footnote †: journal: Sensors