esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Virtual Measurement
### Pressure Trace  
During the ignition delay period, the process of vaporization and atomization of the fuel will bring influence on the in-cylinder pressure. This phenomenon can be clearly observed in the pressure trace. By taking the difference between the combusted pressure and the motored pressure, the injection timing, ignition delay, and start of combustion are distinctly demonstrated in Figure 7.  
Figure 4: Comparison between the modeled and simulated ignition delay.  
Figure 5: EGR valve opening and injection mass for model validation.  
Figure 6: Comparison between the modeled and simulation ignition delay.  
Thus, the ignition delay can be obtained from pressure trace (which can be measured using physical sensors), and combining with other measurable parameters used in Equation (17), the ICOC can be calculated accordingly.  
Usually, the ignition delay is within a range of 10 CA degrees, such short period means the sampling rate of the pressure sensor is of critical importance. A stair effect may appear if the sampling rate is too low. The effect of the pressure sampling rate on the reconstructed ignition delay is simulated and shown in Figure 8.  
Figure 8 shows the comparisons of the simulated and reconstructed ignition delay by utilizing the pressure difference (as detailed in Figure 7) with different sampling rates. The pressure is sampled every 1 CA degree in Figure 8a, while the Figure 8b is with a 0.5 CA degree sampling rate. As one can see, if the sampling interval for the pressure is as long as 1 CA degree, it can lead the resolution of the reconstructed ignition delay too low to capture small changes. Although the shorter the pressure sampling interval the better, an ultra-quick sampling rate may bring noise (if sensor filter is not applied). Figure 8b tells, the ignition delay reconstructed by pressure trace with a sampling interval  
Figure 8: Ignition delay reconstructed by pressure trace with different sampling rate: (**Left**) Pressure sampled for every 1 CA degree. (**Right**) Pressure sampled for every 0.5 CA degree.  
Figure 7: Ignition delay demonstrated by pressure difference trace (measured from a medium-duty diesel engine).  
of 0.5 CA degrees has an error around 0.3 CA degree (half of the sampling interval) which is sufficient for calculating the ICOC and is also achievable for commercial pressure sensors. Therefore, the following data association algorithm is derived based on 0.5 CA degree pressure sampling rate.  
Similarly, since the ICOC obtained by the ignition delay model will serve as a special measurement for the following estimation strategy, it is called the \"virtual measurement\" comparing to the \"dynamical model\" in Section 2.
# Touch, Texture and Haptic Feedback: A Review on How We Feel the World around Us
## 4 Haptic Feedback
### Cutaneous Feedback
#### 4.2.2 Skin Indentation  
Indentation is another form of cutaneous stimulation that is also commonly used. It relies on the use of actuators that apply a normal force onto the skin by way of indentation and depending on the method of application can be used to project a sense of weight and shape to an object. These devices are generally attached to the skin itself or mounted to gloves. As the fingers have the most dense concentration of receptors [75], they are usually the focal point of where most devices are attached, either directly onto the skin or through a glove. Depending on the amount of force being applied, one can change the perceived amount of force being applied on a virtual object. One of the most common ways of implementing indentation-based feedback is through the use of motors which drive a series of end effectors directly onto the skin at varying forces [86, 89, 123, 126, 127]. TacTiles apply indentation using pins attached to an electromagnetic actuator which can lock itself in place to simulate grasping [76]. While skin indentation is also a low-cost and effective means of replicating touch, it suffers from problems regarding bulk as the actuators used to drive its systems need to be placed at the fingertips. In addition, it can inhibit a person's movements which, while a non-factor in virtual reality environments, is a negative when taken into augmented reality systems where interaction with both virtual and real-world systems is required.  
Hydroanically amplified self-healing electrostatic devices are a type of dielectric actuator using liquid dielectric instead of elastomers, providing integrated hydraulic amplification [128] wherein applying voltage pushes the fluid to simulate touch. Fluid actuators such as hydraulically amplified taxel (HAXEL) enable dense and flexible cutaneous haptic feedback. By using segmented electrodes, we can not only push the central bump up, but also shift it north/south and east/west and create rotation-like motion [129]. The VHB 4910 acrylic elastomer gave the highest performance in terms of strain and actuation pressure [130]. By using fluid actuation, we can achieve both tactile and kinesthetic feedback sensation, and this is proven in the research in which the electrorheological (ER) fluids are used, and the haptic sensation is controlled by applied electric field [131]. The use of ER fluids also shows promise as a tool for kinesthetic force feedback systems.
## 2 Hybrid Treatment Methods
### Membrane Separation/Oxidation Treatment  
Nanofilitration (NF) stands out as a highly efficient technology capable of eliminating over 99% of perfluorooctanoic acid (PFOA) from groundwater [33]. Nonetheless, an inherent drawback of employing membrane filtration lies in the accumulation of concentrated PFOA within the membrane reject. To achieve complete eradication of PFOA and other PFASs, it becomes imperative to neutralize them within the reject phase.  
In a study conducted by [PERSON] et al. [49], a trial showcased the application of advanced oxidation for dismantling PFOA within NF reject through photocatalysis [Figure 3]. Nanoscale zero-valent iron was harnessed as the catalyst for this process. Subsequent to the photocatalysis, the nanoparticles were removed via ultrafiltration (UF) prior to discharging the treated water into the environment. The findings highlighted a remarkable 99.62% efficiency in PFOA removal by NF and an additional 59.64% degradation of the rejected portion through photocatalysis.  
In a parallel study by [PERSON] et al. [30], an NF membrane (N90) was demonstrated to effectively eliminate 99.5% of hexafluoropropylene oxide dimer acid (HFPO-DA) from a synthetic contaminated water matrix. This was accomplished while working with background electrolytes of 500 mg/L Na\({}_{2}\)SO\({}_{4}\) and 100 mg/L NaCl, alongside a 1 mg/L HFPO-DA solution. At a water recovery rate of 80%, the resulting concentrations were 4.98 mg/L in the reject and a mere 0.005 mg/L in the permeate. Electrochemical oxidation (EO) treatment of the NF reject facilitated the breakdown of HFPO-DA, leading to a substantial reduction in energy and electrode costs, surpassing the efficiency of EO treatment alone on raw water by over an order of magnitude. The role of electrochemically activated sulphate was identified as a catalyst for oxidation. The study concluded that the combination of NF and EO represents a viable and sustainable strategy for the cost-effective elimination of HFPO-DA and other PFASs from contaminated water. It was also recommended that measures be adopted to address the byproducts resulting from electrochemical oxidation, with options such as biological reduction within the NF reject. Impressively, the study showcased that the use of reverse osmosis coupled with under-vacuum evaporation can drastically curtail the volume of regeneration effluents requiring inc  
Figure 3: Removal of PFOA by NF followed by photocatalytic oxidation (modified from [49]).  
In summation, these investigations underscore the effectiveness of synergizing NF with advanced oxidation as a potent and economically viable approach for eradicating PFASs from polluted water. While the NF phase handles the bulk of PFAS removal, advanced oxidation effectively disposes of the residual compounds. This integrative approach not only achieves high removal efficiencies but also significantly mitigates the volume of PFAS-laden waste, necessitating proper disposal.
## 3 Routing Protocols for UWSN
### Protocols Featuring Energy Balancing
#### 3.2.1 Reliable Energy Efficient Pressure Based Routing (RE-PBR)  
[PERSON] et al. propose Reliable Energy Efficient Pressure Based Routing (RE-PBR) to improve energy efficiency of the routing. Moreover, it does not need location information of the nodes for routing. It takes the link quality and depth into account along with the residual energy for reliable transmission [56]. The scheme architecture is depicted in Figure 7. The network comprised of base station, sinks and underwater wireless sensors. Underwater sensor-nodes are provided with acoustic modem and pressure sensors which are placed in different depths of sea. The vertical movement of these nodes are negligible whereas the parallel motions are considered upto 0-3 m/s [52, 68]. Sinks are placed at the top of the water. All the nodes aggregate the data and deliver it to the sinks. As the nodes are placed at different depths of the sea, they employ multi-hop technique to deliver the data to the sink. The main feature of RE-PBR protocol is that, it measures link quality precisely with triangle metric in its forwarding process. No other depth based routing protocols have used triangle metric to find the link quality during data forwarding.  
Figure 6: Data transmission in Virtual Tunneling Protocol (VTP) [53].  
Every node aggregates information like ID, remaining power and depth in a data packet. This data packet is sent to each of its one hop neighbor. So, each node can compare its own distance from the surface with that of sender. If the depth of the sender is more than its own depth, the node saves the data of the hello packet. Else, the data packet is discarded by the node. In this way, each node collects the data of one hop neighbor and stores them to select the best forwarding node. Then by computing the distance, the link quality based triangle metric is estimated for each stored data. Two things are considered while selecting the next forwarding node--route cost basing on the residual energy and the distance which depends on the triangle metric. Nodes eventually repeat this process till the data finds any sink for transmission.  
The network lifetime of RE-PBR is longer when compared with DBR and EEDBR. This is because in RE-PBR the forwarding node is chosen depending on their higher residual power and better link quality. The packet delivery ratio of RE-PBR is also better than that of DBR and EEDBR. Moreover the energy efficiency of this scheme is better than that of DBR and EEDBR. As there is no provision for holding or storing the data, the delay is also minimal in this routing protocol.
# A Heuristic Approach for a Real-World Electric Vehicle Routing Problem
## 4 Solution Approach
### Insertion Heuristics
#### 4.4.3 Regret-\(k\) Insertion Heuristic  
Let \(r_{k}\) denote the route for which customer \(i\) has the \(k\)th lowest insertion cost, that is, \(\Delta_{i,r_{ik}}\leq\Delta_{i,r_{ik}}\) for \(k\leq k^{\prime}\). In the regret heuristic, the regret value \(c_{i}^{*}\) is defined as the difference in the cost of inserting customer \(i\) in its best route \(r_{i1}\) and its second-best route \(r_{i2}\), i.e., \(cost_{i}^{*}=\Delta_{i,r_{i2}}-\Delta_{i,r_{i1}}\). In each iteration, the regret heuristic chooses customer \(i\) having the maximum global regret value, i.e., \(\min_{i\in D}\{cost_{i}^{*}\}\), and inserts it into its minimum cost position. Ties are broken by selecting the lowest cost insertion. The process is repeated until all removed customers are reinserted into the partial solution.  
The above technique can be extended naturally to define a class of regret heuristics by computing the difference in cost of inserting customer \(i\) in its best, 2 nd best, \(k\)th best route, where \(k\) is a user-defined parameter. The resulting heuristic is called the _regret-k_ heuristic. The regret-\(k\) heuristic selects customer \(i\) based on \(max_{i\in D}\{\sum_{j=2}^{k}\left(\Delta_{i,r_{ik}}-\Delta_{i,r_{i1}}\right)\}\) and then inserts it in its least cost position. Regret-2, regret-3, regret-4, and regret-all are used in our ALNS. The regret-all heuristic considers all routes when selecting customers to be inserted.
## 3 Results and Discussion  
### Data Quality and Count Matrix  
During the analysis of the RNA-Seq data, the per-sample mean number of reads was found to be 32,051,322, with a wide range of 4004 to 98,797,584 reads per sample. The average read length was determined to be 88.5, with a range of 76 to 294. In terms of read mapping, it was observed that, on average, 57.41% of the reads across all samples were successfully mapped to the reference genome, with a minimum of 9.42% and a maximum of 94.51%. After filtering out genes that were not expressed in any of the samples, 4.8% of genes (849 out of 17,559) were removed from the resulting read count matrix.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 5 Discussion: Application, Challenges and Future Perspectives
### The Future of Building IOT (Internet of Things) Technologies  
The sensor and communication technologies discussed in previous sections of this review reside at the forefront of 'Internet of Things (IoT)', comprised of a network of interconnected devices including sensing and communication technologies, and associated software and electronic hardware instrumentation, which enable these devices to rapidly acquire, process, and transmit and receive data over fast networks. IoT sensors, also referred to as'smart sensors', are able to not only detect and output physical events and changes in the environment but also generate much larger amounts of data with increased granularity and communicate the acquired information efficiently throughout a connected domain.  
Recent reports have forecasted that the number of IoT-connected devices, sensors and actuators are expected to exceed 46 billion by 2021 [143] and the IOT market has a total potential economic impact of $3.9 trillion to $11.1 trillion a year by 2025 [144]. Despite these exciting propositions, its adoption and implementation in the building sector has been slower than expected so far. As discussed in the previous sub-section, the security of sensor networks is a significant challenge at present as the network becomes more prone to hacking or loss of data, and restricts data privacy and sharing, when billions of sensor and communication devices are connected together over the internet. At the same time the storage, tracking and analysis of the sheer amount of data produced by billions of devices becomes increasingly challenging. Key factors such as device scalability, cost and power consumption,installation complexities, selection and placement of sensing and communication technologies for optimum coverage and performance, and some manufacturers keeping their devices proprietary to other hardware for market dominance, also continue to hinder the wide-scale adoption of the technology at present. This highlights the need for the integration of all-intelligent systems and digital networks within buildings which can enable wide-range networking, digitization, and the capability to collectively acquire and analyze data to make real-time building-wide decisions. In the future, we can expect an increase in cutting-edge intelligent sensor technologies consisting of low-cost, low-power processors and inbuilt energy harvesting capabilities. Furthermore, enhanced embedded support for various communication protocols, and further development of intuitive machine learning algorithms and user-friendly software applications will also play an essential role in addressing the aforementioned challenges in the future.
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 6 Evaluations
### Performance of Layer Dropping in a 56 Gbit Network Environment  
The second experiment was carried out using four machines. One of the four machines had a parameter server and two workers running, and the other three machines had two workers each. The second experiment was performed using KISTI's (Korea Institute of Science and Technology Information) Neuron [25]. Each machine consisted of an Intel Xeon Skylake and two NVIDIA Tesla V100s. The network was configured with 56 Gbit Ethernet. As a training model, the ResNet v1 50 model was used. ImageNet was used as the data set for training the model. Furthermore, like the previous experiment, a distributed deep learning environment was implemented using OpenMPI 3.0.0 and TensorFlow 1.13.1.  
Figures 10 and 11 show the loss per epoch and training accuracy. In Figure 10, when layer dropping is applied, the overall training loss is higher than the baseline. In addition, looking at Figure 11, it can be seen that the overall training accuracy decreases when layer dropping is applied.  
Figure 11: Training accuracy per epoch in a high bandwidth network.  
Figure 10: Training loss per epoch in a high bandwidth network.  
Figure 12 and 13 show the training loss and accuracy over time. We conducted experiments until the accuracy in the case of applying layer dropping became similar to the baseline. In Figure 11, it can be seen that there is a loss in training accuracy due to layer dropping. However, even in a 56 Gbit Ethernet environment, the time required to perform each iteration decreases due to the decrease in communication time. Therefore, as shown in Figure 12, when layer dropping is applied, the training loss decreases faster than the baseline. Furthermore, Figure 13 shows that the training accuracy when layer dropping is applied increases faster than the baseline.  
Figure 14 is a graph showing the computation time and communication time per iteration. When layer dropping is applied, the training time of the worker increases by 3.6% from 0.222 s to 0.230 s. Furthermore, the computation time of the parameter server increases by 23% from 0.048 s to 0.06 s. However, in the case of communication time, an 80.8% decrease from 1.951 s to 0.375 s can be seen. Therefore, the time to perform one iteration is reduced by about 70%, so that learning can be performed faster than the existing distributed learning.  
Figure 12: Training loss over time in a high bandwidth network.  
Figure 13: Training accuracy over time in a high bandwidth network.
## 2 Methods
### The Design of the Data Compression Algorithm
#### 2.3.3 Huffman Algorithm  
The Huffman algorithm assigns encodings based on the probability of characters appearing in the data [21]. A shorter code is assigned to higher probabilities and a longer code to less frequent characters, thus achieving data compression as shown in Figure 6. This algorithm commences with a preliminary data traversal, constructing a Huffman tree in which higher frequency characters are positioned closer to the root. A binary system marks the encoding direction--\({}^{\prime}0^{\prime}\) denotes a leftward pathway; while \({}^{\prime}1^{\prime}\) indicates a rightward trajectory. After the shore base receives the compressed data, it is decompressed according to the constructed Huffman tree, revealing the buoy profile detection data.  
Figure 5: LZW data compression algorithm implementation flowchart.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 5 Ongoing R&D
### Putting an IoT Gateway in Space  
Differently from geostationary satellites, for LEO IoT satellite constellations, it is not always possible to ensure continuous ground-based connectivity by using a limited number of GWs. In this case, the uplinked packet demodulation shall take place on board the satellite in order to reduce the requirements on the feeder link. Decoded packets shall then be stored on board and dumped to ground via the feeder downlink when the ground station is in view, or using inter-satellite link (ISL)-based connectivity if this is available.  
According to public information, this demodulate-and-store approach is adopted by various LEO systems and, in particular, using the E-SSA protocol by the Dutch company Hiber on its CubeSats [28; 29].  
Figure 19: UC#3 OAED constraint.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 1 Introduction
As discussed above, in this study, we focus on developing the target speech enhancement algorithm with a single AVS from a new technical perspective in which both the ambient noise and non-target spatial speech interferences can be suppressed effectively and simultaneously. The problem formulation is presented in Section 2. Section 3 shows the derivation of the proposed SE algorithm. The experimental results are given in Section 4, and conclusions are drawn in Section 5.
## 4 Experiments
### Main Results  
We first compare our \"performance first\" model with common BERT-like models on five test datasets. For easy comparison, three existing pre-trained models, BERT_base, RoBERTa_base and RoBERTa_large, are used as baselines and backbones of our models. Table 1 shows the experimental results of our model and the baselines on three MRC datasets, and Table 2 shows the results on two classification datasets. As we can see, in almost all datasets, our model outperforms the baselines by 0.1-2.7% accuracy. These results show that different questions can be handled by the representations in different blocks instead of the top block only. The representations in lower blocks can bring competitive effects as the top layer. We also compare our method with DUMA [34], a representative MRC model with a complex decision module (for a fair comparison, both our model and DUMA are implemented by the BERT_base model). It can be shown that our model also works when incorporating with DUMA, indicating that the intermediate-layer information is useful for different decision modules. These results again show the effectiveness of our method.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Datasets/Models**} & \multicolumn{3}{c}{**RACE**} & \multirow{2}{*}{**ReCO**} & \multirow{2}{*}{**Dream**} \\  & **All** & **Middle** & **High** & & \\ \hline BERT\_base & 65.0 & 71.7 & 62.3 & 77.1 & 63.2 \\ RoBERTa\_base & 74.1 * & 78.1* & 72.5 * & 79.8 * & 70.0 * \\ RoBERTa\_large & 83.2 & 86.5 & 81.8 & 79.2 & 85.0 \\ DUMA & 66.1 * & 71.8 * & 6  
Then we evaluate the accuracy and speed of our \"speed first\" model. Floating-point operations (FLOPs) ([[https://github.com/Lyken17/pytorch-OpCounter](https://github.com/Lyken17/pytorch-OpCounter)]([https://github.com/Lyken17/pytorch-OpCounter](https://github.com/Lyken17/pytorch-OpCounter)) (accessed on 13 January 2021)) is used to measure the computational complexity. Tables 3 and 4 show the accuracy and FLOPs. Increasing the threshold will no doubt speed up the inference process since the inference is more likely to be terminated at lower layers. As the table shows, compared with the BERT_base model, our model not only accelerates the inference process but also achieves better accuracy on all datasets in most cases. For the MRC task, our model is faster than the BERT_base model by 1.02-1.12 times on RACE, 1.11-1.39 times on ReCO and 1.00-1.02 times on Dream. For the classification task, our model accelerate the inference by 1.29-1.81 times on the Book Review dataset and 2.06-2.82 times on the Ag.news dataset. Moreover, our speed first model with \(v=0.1\) also achieves comparable accuracy to our performance first model (\(v=0.0\)) with a higher inference speed.