esa-sceva/satcom-chunk-collection huggingface dataset:
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 5 Conclusions  
This paper proposes the FD-LTDA-MAC protocol, a new protocol which builds on LTDA-MAC but it provides efficient packet schedules for full-duplex based underwater acoustic chain network scenarios. This protocol significantly improves spatial re-use on a time shared channel but fully exploits full-duplex operation to compress frame durations especially for the longer pipelines spanning thousands of kilometres to improve the monitoring. The FD-LTDA-MAC protocol produces a better packet schedule for underwater acoustic chain network scenarios. Linear constraints in FD-LTDA-MAC allow for the  
Figure 14: Percentage reduction in the Frame duration across pipeline scenarios.  
Figure 13: Frame duration cdfs of LTDA-MAC with HD nodes, LTDA-MAC with FD nodes, and FD-LTDA-MAC for large scale scenarios.  
calculation of transmit delays for forwarding packets to reflect full-duplex capabilities, extension to the greedy scheduling algorithm to utilise full-duplex optimised starting point for transmit delay search and modify the initial schedule and evaluation algorithms to accommodate full-duplex capability. The advantage of spectrum re-usability of FD-LTDA-MAC is leveraged by the full-duplex communication mechanisms to deal with long propagation delay and interference patterns to provide a more efficient packet schedules, which, in turn, provides greater network throughput performance for the longer pipeline scenarios. Results that are based on simulation of small scale (2 km, 10 km, and 20 km), medium scale (50 km and 100 km) and large scale (200 km, 500 km, and 1000 km) scenarios show that FD-LTDA-MAC achieves a performance improvement of 44%, 83% and 56% in small, medium, and large scale scenarios, respectively, compared to LTDA-MAC protocol. Useful further research includes the use of other optimisation algorithms in a bid to further shorten the frame durations produced by FD-LTDA-MAC. Additionally, redundancy in the links will be explored to solve the problem of failing nodes.  
Conceptualisation, A.A., N.M. and P.D.M.; methodology, A.A., P.D.M., N.M. and Y.Z.; software, A.A. and N.M.; validation, A.A., P.D.M., N.M. and Y.Z.; formal analysis, A.A. and N.M.; investigation, A.A., N.M., P.D.M. and Y.Z.; writing--original draft preparation, A.A.; writing--review and editing, A.A., P.D.M., N.M. and Y.Z.; visualisation, A.A.; supervision, P.D.M., Y.Z. and N.M.; project administration, P.D.M.; funding acquisition, A.A., P.D.M. and Y.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by Petroleum trust Development Fund (PTDF).  
Not applicable.  
The work has been supported by the Engineering and Physical Sciences Research Council (EPSRC) through [grant numbers: EP/V009591/1, EP/R003297/1 and EP/P017975/1].  
The authors declare no conflict of interest.
# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
## 2 Background and Motivation
### Related Work
#### 2.2.2 Efficient I/O Processing in a Virtualized Environment  
Research for improving the I/O processing performance in virtualized environments can be categorized into two types: one is to use additional hardware devices to increase I/O processing performance and the other is to reduce the data-copying overhead in I/O processing.  
First, single root I/O virtualization (SR-IOV) is a technique that allows VMs to directly access physical network devices without the intervention of the host system [13; 14; 15]. Allowing the VMs to process I/O requests directly increases the I/O processing performance significantly. However, in SR-IOV, I/O processing is based on interrupts; therefore, it incurs a large overhead when the interrupts occur at high speeds. To reduce the overhead, there have been attempts to minimize the VM-Exit and VM-Entry operations using software methods [16] or hardware methods [17]. Furthermore, previous studies propose assigning specific CPU cores to dedicated vHost threads to mitigate the interrupt handling overhead [18; 19]. However, assigning CPU cores for I/O processing degrades the CPU utilization at moderate I/O processing rates [20]. Thus, in existing cloud environments, SR-IOV is only used for users who require very high or stable network performance at high prices.  
The second approach is to reduce the data-copying overhead. To this end, one study proposed a zero-copy technique that eliminates memory copying in the packet forwarding path to speed up processing and reduce CPU usage [21]. Complementary with the proposed method, it would be more efficient if the adaptive polling of NetAP and a zero-copy method were simultaneously used.
# Photocatalytic Degradation of Selected Non-Opioid Analgesics Driven by Solar Light Exposure
## 1 Introduction
The optimal conditions were obtained at pH 6.9 with a dose of 1.0 g/L of photocatalyst at 30 \({}^{\circ}\)C. A kinetic study showed that photocatalytic degradation of acetaminophen fits well in the pseudo-first-order model. Competitive reactions from intermediates affected its degradation rate and were more obvious as the initial drug concentration increased. C-modified titanium oxide using lignin as a carbon precursor has been proposed for the solar photocatalytic degradation of acetaminophen, and complete conversion was achieved after only 1 h under solar irradiation [25]. It should be mentioned that the 99% efficiency of this compound's photodegradation at 20 mg/L concentration level was reported in the presence of TiO\({}_{2}\) catalysts with a nanotubular morphology after 100 min ultraviolet radiation [26]. Efficient photocatalytic degradation of ibuprofen under visible light irradiation was obtained using silver and cerium-co-doped mesoporous TiO\({}_{2}\)[27]. The photodegradation reaction of ibuprofen catalyzed by TiO\({}_{2}\) immobilized on an active glass surface achieved 85% compound disappearance after 24 h of simulated solar light irradiation [28]. The degradation reaction was fast during the first 20 min, and then it gradually decreased. The main goal of this study was to investigate the photocatalytic degradation of some non-opioid analgesics that encompass paracetamol (acetaminophen) and the non-steroidal anti-inflammatory drugs (NSAIDs) (ketoprofen, naproxen, diclofenac, ibuprofen), which have been used worldwide among patients. The degradation process of these compounds was conducted under UV/solar light irradiation using nanostructured semiconducting metal oxides such as WO\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\) as well as their composite. These photocatalysts have not been evaluated so far for the degradation of the studied drugs. Nanostructured WO\({}_{3}\) showed a photocatalytic effect for lidocaine, a prevalent anesthetic drug, under visible and sunlight with high efficiency (96-98%) within 1 h [29]. [PERSON] et al. found that the degradation efficiency of antibiotic amoxicillin by simulated solar irradiation with WO\({}_{3}\) decreased with increasing initial drug concentration and pH and increased with increasing catalyst dosage [30]. [PERSON] et al. applied the nanostructured WO\({}_{3}\)/Fe\({}_{2}\)O\({}_{3}\) hybrid system for the photodegradation of some surfactants in water samples [31] as well as for organic matrix decomposition before arsenic [32] and thallium [33] speciation analysis. In the literature, there are also examples of utilizing WO\({}_{3}\)/Fe\({}_{2}\)O\({}_{3}\) systems in the photodegradation of some dyes [34; 35].
## 3 A Centralised Mapping Service
### Discussion  
The centralised approach collects all of the data in a central location and processes it there. In networks of a few nodes, this might even be the most sensible solution. However, in networks of nodes large enough to be useful, the number of transmissions needed to accomplish the task would become so large as to require a more intelligent solution. The cost of communication in this initial system is high because the interpolation step; the Mapping Generation service is fairly simple and requires that all data is collected to a point. For the simplest case, in which the node collecting data is in direct communication with all other nodes in the network, the number of hops is simply \(N-1\), for \(N\) nodes. However, this is an unlikely situation in any real world application. The likelihood is exponential growth with \(N\).  
Although the cost, in terms of power of transmission of data in WSNs far outweighs the usual processing load, the nodes are most often power-limited devices with, at best, a fixed schedule of battery replacement, and so computational drain on this resource can not be completely overlooked. For small collections of data of around \(50\) nodes interpolating \(65,536\) points--a square field \((256\times 256)\)--takes very little time, just a few seconds, on a desktop computer and would take just a little more on modern sensor nodes. In a real deployment, the number of sensor nodes might easily exceed \(1000\), depending upon the required density, drastically increasing computational expense. The acceptability of such a computational load in a WSN is debatable, but in this case would probably be unacceptable for most deployments.  
The simplest improvement is to develop a complete implementation of [PERSON]'s refinements and assess how this affects the accuracy of the interpolated field when compared to a known real surface or phenomenon. Although the algorithm presented by [PERSON] is decades old, it is often only implemented in its most simple form and it would be of great benefit to have empirical evidence of the performance of the various improvements suggested by [PERSON], when applied to data likely to be perceived by a sensor network. A particularly interesting feature of the refinements is that they deal with limiting the data included in the weighted average (the support set), or adjusting weights based on context. These improvements might be instantly applicable as ways to reduce communication costs as well as improve the quality of the interpolation. That is, if local information is required, then only local nodes need be queried. Taking this idea further, we arrive at the idea of a distributed mapping service that allows for local querying.
## 3 Model-Driven Approach to QoS Estimation Using Neo4j Graph Database Aided by ChatGPT
### Simulations and Evaluation  
In this paper, we present a machine learning-based method that aims to estimate the degree of quality of service (QoS) relying on a classification algorithm, considering the previously calculated CC value as one of the model inputs. Apart from this value, several more factors were taken into account as well: (1) base station ID--identifier of the infrastructure unit responsible for a given area, (2) area ID--identifier used to distinguish the region of interest, (3) service consumers--the count of users in the considered area, and (4) season--an ID number corresponding to the part of the year. Additionally, the output of the predictive model represents a label, which is a categorical value: 1--malfunction/anomaly; 2--insufficient; 3--acceptable; 4--high. Therefore, the problem is considered as multilabel classification. Table 4 gives the structure of the underlying dataset header used for the model training, exported from our simulation environment [16; 32]. Figure 5: Prediction problem meta-model customization for Neo4j. Figure 6: Example of ChatGPT code generation in case of GDS classifier training command. In order to evaluate the proposed method, a dataset counting 100,000 records was used, split into training and test subsets (75 vs. 25%). For execution, a laptop with the following specifications was used: Intel i5-10300H, 2.5 GHz, quad-core CPU, 32 GB of DDR4 RAM, and M1 SSD. Regarding the evaluation, it was analyzed from the perspectives of three different indicators: (1) prediction performance of the classification model, (2) processing time, and (3) simulation workflow speed-up compared to the manual approach without automation tools. In Table 5, the columns have the following meanings: (1) aspect of evaluation--the value of interest used for the estimation of the result, (2) considered component under evaluation, (3) parameters relevant to the simulation conditions and the environment, and (4) achieved results and outcomes. For the processing time, the three main steps were considered: CSV import, Neo4j graph construction, and GDS classification command. Figure 7 illustrates the inputs and outputs of the experiments, as we started from the Score model instance based on the meta-model from Figure 5, imported the dataset in graph node form, and finished with QoS estimation thanks to the classification performed by the GDS library within the Neo4j software environment Neo4j Desktop (version 1.5.8) [33]. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Variable** & **Description** & **Data Type** & **Role** \\ \hline CC & Channel capacity & Float & Input \\ \hline Bsld & Base station identifier & Integer & Input \\ \hline Areald & Identifier of area covered by base station & Integer & Input \\ \hline Nusers & Number of users within the observed area & Integer & Input \\ \hline Season & Number denoting part of year & Integer [0–3] & Input \\ \hline QoS & Estimation of QoS value & Integer [1–4] & Output \\ \hline \hline \end{tabular}
\end{table} 
Table 4: QoS prediction dataset layout.
# EEMIP: Energy-Efficient Communication Using Timing Channels and Prioritization in ZigBee
## 4 Results and Discussion
### Evaluation of Scenario B
Table 8 contains a difference in time required for the nonbeacon ZigBee mode to successfully transfer the same amount of data as the EEMIP mode for congestion conditions. Based on these results, we can confirm that the devices in nonbeacon ZigBee mode have to operate longer than in EEMIP mode to successfully transfer the same amount of data in congested conditions. Since they must operate and communicate longer, they consume more energy. Therefore, this experiment has also shown that the EEMIP mode is more energy efficient than the nonbeacon ZigBee mode.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 4 State Estimation
### Joint Model Estimation
#### 4.2.3 Encoder Model  
The graph from Figure 19 displays what was expected: the estimates of the joint model using only encoders follow the baseline obtained from the same encoders. This is an obvious outcome, which has its limitations. The measurements from the encoders do not constitute a reliable ground truth and therefore should not be trusted as an accurate estimate. For example, this model is incapable of detecting overshoots like the ones observed in Figures 16 and 18 of the results of the gyroscope and accelerometer models.
# Perturbations During Gait on a Split-Belt Treadmill: A Scoping Review
## 4 Discussion
### Differences in Used Treadmills  
In studies that used split-belt treadmills, the Grail Motek treadmill is the most frequently used, allowing for perturbations in AP and ML directions, tilting, and offering a realistic simulation of real-life walking conditions [2; 4; 33; 38; 42; 43; 47; 52; 55]. The Caren Motek treadmill, the second most implemented, features 6 degrees of freedom for complex movement simulations, benefiting rehabilitation, sports, military, and aviation training [9; 19; 37; 44; 46; 48; 56; 59]. The Sensly treadmill, though less common [30; 31; 32; 54; 57], is valued for its independently moving sections and advanced sensors, providing precise control and detailed analysis of gait dynamics. A notable feature of the Sensly treadmill is its use of the Froude number (Fr) to determine walking speed and intensity.  
The Froude number is a ratio of kinetic-to-potential energy used to characterize natural movements of animals that are geometrically similar but of different sizes. It is defined as \(\mathrm{F_{r}=v^{2}/(gL)}\), where \(\mathrm{v}\) is velocity [m/s], \(\mathrm{g}\) is the acceleration due to gravity [9.81 m/s\({}^{2}\)], and \(\mathrm{L}\) is the leg length of the participant [60]. According to [PERSON] and [PERSON] [61], \(\mathrm{Fr}=0.25\) represents optimal walking speed, \(\mathrm{Fr}=0.5\) denotes the walk-to-run transition, and \(\mathrm{Fr}=1.0\) indicates the physical walking limit. For most studies, the initial velocity level was calculated using the following formula: \(\mathrm{v}=\sqrt{g\cdot L\cdot F_{r}}\), with a typical \(\mathrm{Fr}\) of 0.15 for a leisurely walk. It allows for personalized walking speeds based on leg length [32]. Perturbation intensity is categorized as soft (\(\mathrm{Fr}=0.10\)), medium (\(\mathrm{Fr}=0.20\)), and strong (\(\mathrm{Fr}=0.30\)), with higher \(\mathrm{Fr}\) values increasing treadmill speed and perturbation intensity [54; 57].  
In the case of the Grail and Caren Motek treadmills, perturbation intensity depends on the treadmill's acceleration and the duration of the perturbation. Variations in these parameters across different studies complicate direct comparisons. Thus, the choice of treadmill will depend on research needs. The Grail Motek is the best for simulating single-leg slips/trips and tri-axial perturbations, the Caren Motek excels in advanced multi-directional simulations and complex rehabilitation, and the Sensly treadmill is preferred for precise perturbation control and a detailed force analysis. Each treadmill's unique capabilities are crucial for advancing gait biomechanics research, rehabilitation, and fall prevention strategies.
## 3 Results
### Real-Time Case Study Evaluation  
Next, we present the results for the evaluation of four scenes at a home in naturalistic conditions using the CNN + MFCC model which performed learning under the ad hoc ambient audio dataset. The six scenes comprised the following sequences of activities:  
* (Scene 1) The inhabitant arrived home, went to the kitchen and started talking, then started using cutlery, then turned on the extractor fan for a long while, then turned on the tap, turned on the microwave, and was called on the phone.
* (Scene 2) The inhabitant arrived home, went to the living room and started talking, then started vacuuming, then opened and closed the window blinds and then was called on the phone.
* (Scene 3) The inhabitant arrived home, went to the bedroom and started talking, then started vacuuming, then the alarm clock went off for a long while, then printed some documents, and finally, the individual opened and closed the window blinds.
* (Scene 4) The inhabitant went to the fourth bathroom and started talking, then turned on the tap, then took a shower for a long while, then vacuumed and, finally, flushed the toilet.
* (Scene 5) The inhabitant was talking in the kitchen, then started vacuuming, then talked again and started using cutlery, then opened and closed the window blinds, then turned on the tap and, finally, used the microwave.
* (Scene 6) The inhabitant was in the bathroom vacuuming and started talking, then he took a shower for a long while, then was called on the phone and afterward turned on the tap; finally, the individual left the room closing the door.  
In this context, a new label is necessary to recognise _idle_ as an event class, which corresponds to the absence of target events, including silence and other ambient sounds produced by the inhabitant. The addition of the _idle_ label is key for AR learning in real-time conditions [11,15]. For evaluation purposes, idle activity has been included using a scene cross-validation, where each scene is learned with idle audio samples from other scenes, together with the offline dataset of target events.  
In Table 5, we detail the performance of the CNN + MFCC ambient sound recognition model, comparing the ground truth against the inferred classification by means of F1-score, accuracy, precision, and recall for each scene.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 6 Conclusions  
The paper proposes a passive location for the OFDM radiation source based on the virtual synthetic aperture. The algorithm utilizes the characteristics of the moving platform and the principle of virtual aperture forming, using the coherent data to achieve the high-precision location. According to the multi-carrier modulation technology of the OFDM signal, phase information at a specific frequency can be extracted by FFT, which contains the location information of the radiation source. The reference signal of the 5G communication system is used for phase compensation and noise reduction. The processed signal is equivalent to an LFM signal in the azimuth domain, thus the azimuth location can be completed by pulse compression, while the range location can be completed by range searching. The algorithm achieves high-precision location by a single antenna, which can realize large-area coverage and turn the hardware structure into software signal processing to reduce system complexity and cost. In addition, the paper deduces the azimuth resolution and range resolution and verifies them by Monte Carlo simulations. Simulations were conducted to verify the effectiveness of the algorithm and analyze the effect of satellite vibration on resolution and location error. In subsequent studies, the effects caused by satellite vibration will be compensated according to the simulation results.  
Figure 10: The effect of satellite vibration. (**a**) The effect on azimuth resolution. (**b**) The effect on range resolution. (**c**) The effect on location error.  
We will further analyze the performance of the proposed location algorithm, especially in the more complex scenarios such as multi-target or moving target, and improve the performance in a follow-up study.  
[PERSON] and [PERSON] provided the core idea for this study. [PERSON] and [PERSON] implemented the method and carried out the experimental evaluation. [PERSON] wrote the main manuscript. [PERSON], [PERSON] and [PERSON] provided comments and suggestions for this paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant 62031014 and the Key R&D Program of Hainan Province under grant ZDYF2019195.  
The authors declare no conflict of interest.
# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 4 Proposed Technique
### ANN-Based Prediction for Cooperative Computing Vehicle Selection
#### 4.3.6 Configuration Parameters for the Adam Optimization Algorithm  
The following are the configuration parameters for the Adam optimization algorithm that were used for the ANN model training:  
* \(\eta\) represents the learning rate or the step size;
* \(\gamma_{1}\) and \(\gamma_{2}\) are the exponential decay rate parameters that are to be initialized close to 1 (such as 0.9 and 0.9999, respectively);
* \(\epsilon\) is given as a tolerance factor (usually taken as the very small value of \(10^{-8}\));
* \(m\) and \(v\) represent the moving averages of the first and second moments of the gradient, while their corresponding corrected biases are further introduced by \(\widehat{m}\) and \(\widehat{v}\);
* At the end of the algorithm, \(W\) and \(b\) are the updated adaptive weights and biases.  
and potential computing vehicles and the range of the distance is taken as 5-100 m. The last input parameter is the channel gain, which indicates the conditions of the vehicle-to-vehicle channel. The channel gain is generated using complex Gaussian random variables of zero mean, thus providing a Rayleigh fading channel.
* Computation availability model: There are two inputs for the computation availability model. The first input is the number of tasks or requests to offload at a particular time slot \(t\). The Poisson distribution is used to generate the number of tasks, with an average rate of \(\lambda\). The second input is the task size, the value of which lies in the range of 0.5 MB-16 MB. This range is used to realistically model the computation availability as at a particular time slot; however, there may be scenarios in which there are fewer tasks but the tasks are larger or more tasks but the tasks are smaller, etc.