esa-sceva/satcom-chunk-collection huggingface dataset:
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## 3 Results and Discussion
### Impact of Mediator Addition on TrOC Degradation
#### 3.3.3 Effect of Mediators on Enzyme Stability  
In this study, a gradual inactivation of laccase was observed despite the absence of any known chemical inhibitors in the synthetic wastewater (Figure 5). In the absence of redox-mediators, a 37% laccase inactivation was observed over a period of 12 h. This was possibly due to the blockage of the active enzyme sites by the charged metabolites and/or hydraulic stress during membrane filtration [25; 41]. Since the MD membrane can conceptually retain all nonvolatile organics including the transformation products/radicals, laccase inactivation with or without the presence of redox-mediators can be expected. The extent of laccase inactivation increased further when the mediators were added (61%, 66% and 73% for HBT, SA and VA, respectively, each at a concentration of 0.5 mM). The highly reactive radicals generated from mediators can enhance the degradation of TrOCs but at the same time may inactivate laccase [45]. [PERSON] [17] suggested that the metabolites from the oxidation of substrate and/or mediators could react with enzyme to form non-productive complexes, thereby inactivating the enzyme.  
The extent of laccase inactivation also depends on the concentration of redox-mediators. For instance, [PERSON] et al. [45] observed a gradual increase in the inactivation of laccase from _Tranetes trogii_  
Figure 5: Effect of mediator type and concentration on oxidation reduction potential (ORP) and laccase inactivation in the MD-EMBR. Operating conditions of the MD-EMBR are given in the caption of Figure 2.  
following a stepwise increase in the concentration of HBT from 0.1 to 10 mM. In another study, increasing SA concentration from 0.1 to 1 mM resulted in aggravated inactivation of lacase from _Trantes versicolor_[42]. These results suggest that the degree of lacase inactivation is strongly influenced by redox-mediator concentration. Indeed, loss in lacase activity was increased by 7%, 9% and 11% in MD-EMBR due to the increase in the concentration of HBT, SA and VA, respectively, from 0.25 to 0.5 mM (Figure 5). Although lacase activity was greatly affected in the presence of redox-mediators, it was compensated by the improvement in TrOC degradation (Figure 3). For example, the highest drop in lacase activity was observed in the presence of VA (Figure 5), but it outperformed SA and HBT in terms of enhanced TrOC degradation (Figure 3).
## 5 Experimental Results
### ANN
#### 5.5.2 Can News and Tweets Improve the Prediction of the Next Closing Price?
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sentiment and Tweets** & **Sentiment Only** & **Tweets Only** \\ \hline
**APPL Linear** & & & \\ \hline Random Interval \#1 & 12.92 & 20.97 & 20.97 \\ Random Interval \#2 & 0.00 & 0.00 & 0.00 \\ Random Interval \#3 & 0.00 & 0.00 & 0.00 \\ Random Interval \#4 & 0.00 & 1.57 & 0.00 \\ Random Interval \#5 & 0.00 & 0.00 & 0.00 \\ Random Interval \#6 & 1.52 & 0.00 & 0.00 \\ Random Interval \#7 & 0.00 & 4.56 & 0.00 \\ \hline
**APPL SVM** & & & \\ \hline Random Interval \#1 & 0.00 & 6.52 & 0.00 \\ Random Interval \#2 & 43.90 & 37.40 & 47.85 \\ Random Interval \#3 & 50.49 & 46.30 & 41.83 \\ Random Interval \#4 & 24.89 & 33.73 & 13.49 \\ Random Interval \#5 & 70.92 & 66.03 & 35.56 \\ Random Interval \#6 & 57.96 & 52.69 & 60.56 \\ Random Interval \#7 & 7.33 & 42.67 & 0.00 \\ \hline
**GE Linear** & & & \\ \hline Random Interval \#1 & 26.94 & 26.94 & 26.94 \\ Random Interval \#2 & 0.00 & 0.00 & 0.00 \\ Random Interval \#3 & 0.78 & 0.00 & 0.00 \\ Random Interval \#4 & 0.00 & 0.00 & 0.00 \\ Random Interval \#5 & 0.00 & 0.00 & 0.00 \\ Random Interval \#6 & 0.00 & 0.00 & 5.95 \\ \hline
**GE SVM** & & & \\ \hline Random Interval \#1 & 33.33 & 27.24 & 21.51 \\ Random Interval \#2 & 47.04 & 37.57 & 39.05 \\ Random Interval \#3 & 64.55 & 70.30 & 61.59 \\ Random Interval \#4 & 45.21 & 0.00 & 39.32 \\ Random Interval \#5 & 25.04 & 0.00 & 24.40 \\ Random Interval \#6 & 73.27 & 70.68 & 52.27 \\ \hline
**IBM Linear** & & & \\ \hline Random Interval \#1 & 0.00 & 0.00 & 0.00 \\ Random Interval \#2 & 1.10 & 0.00 & 0.00 \\ Random Interval \#3 & 0.00 & 30.31 & 30.31 \\ Random Interval \#4 & 6.95 & 2.66 & 14.87 \\ Random Interval \#5 & 37.16 & 39.00 & 38.43 \\ Random Interval \#6 & 60.88 & 60.88 & 60.88 \\ Random Interval \#7 & 5.14 & 5.14 & 5.14 \\ \hline
**IBM SVM** & & & \\ \hline Random Interval \#1 & 45.45 & 56.35 & 0.00 \\ Random Interval \#2 & 25.32 & 25.53 & 24.20 \\ Random Interval \#3 & 49.15 & 26.10 & 50.07 \\ Random Interval \#4 & 49.57 & 61.39 & 15.28 \\ Random Interval \#5 & 66.10 & 26.16 & 40.78 \\ Random Interval \#6 & 41.55 & 12.83 & 21.00 \\ Random Interval \#7 & 6.03 & 30.80 & 0.00 \\ \hline
**MSFT Linear** & & & \\ \hline Random Interval \#1 & 72.98 & 74.57 & 74.57 \\ Random Interval \#2 & 25.13 & 27.26 & 27.26 \\ Random Interval \#3 & 57.90 & 57.49 & 57.90 \\ Random Interval \#4 & 65.32 & 65.32 & 65.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The improvement rates of the next closing price of the five companies using random intervals, without using rules (in RapidMiner).
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 4 System Design of PPUR  
### System Setup  
TA firstly uses bilinear mapping technology to generate bilinear parameters (\(q\), \(P\), \(G_{1}\), \(G_{2}\), \(\hat{e}\)) with the security parameter \(k\). \(G_{1}\) and \(G_{2}\) are groups of order \(q\), \(P\) is generators, and \(\hat{e}\): \(G_{1}\times G_{1}\to G_{2}\) are nondegenerated bilinear mappings and can be effectively calculated. Then, the TA chooses a random number \(s\in Z^{*}{}_{q}\) as the main key and \(P_{pub}=sP\) as the public key. Finally, the TA chooses a symmetric encryption algorithm, \(\mathrm{Enc}()\), such as advanced encryption standard (AES), and the hash function \(H_{1}\): {0,1}* \(\to G_{1}{}^{*}\), \(H_{2}\): \(G_{2}\to\) {0,1}\({}^{n}\) to publish system parameters (\(q\), \(G_{1}\), \(G_{2}\), \(P\), \(P_{pub}\), \(\hat{e}\), \(n\), \(H_{1}\), \(H_{2}\), \(\mathrm{Enc}()\)), where \(n\) is the length of the message to be encrypted.  
When a mobile node \(N_{i}\) registers with the system, the TA generates a set of pseudo-IDs, \(\mathrm{PID}_{i}=[\mathit{Pid}_{i}^{1},\mathit{Pid}_{i}^{2},\ldots\mathit{Pid} _{i}^{n}]\), of \(N_{i}\). A pseudo-ID, \(\mathit{Pid}_{i}=\mathrm{Enc}_{s}(N_{i}\mid r)\), is generated by the symmetric encryption algorithm \(\mathrm{Enc}()\), master key \(s\), and a random number \(r\). Based on the pseudo-ID \(\mathit{Pid}_{i}\) and the node's public key \(H_{1}(\mathit{Pid}_{i})\), the TA generates the node's private key \(sk_{i}=sH_{1}(\mathit{Pid}_{i})\) by using the primary key \(s\) and hash function \(H_{1}\) to sign the message. When communicating and signing messages, a node changes to using a different pseudo-ID after a certain period to hide its true ID. Because of having the master key \(s\), the TA can obtain the real ID from a node's pseudo-ID and track the node to guarantee security.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion
### Handover Performance
#### 7.2.3 Handover Failures  
Figure 7a shows the number of HOFs for both algorithms. The result shows that the proposed algorithm has a higher number of HOFs than the 3 GPP scheme, which is somewhat unexpected. However, this absolute number of HOFs can be misleading as the proposed algorithm also performed a much higher number of handovers, as explained in Section 7.2.1. Indeed, if we consider the HOFs as a percentage of the total handovers, the proposed algorithm is found to fail 30% less than the 3 GPP scheme, as shown in Figure 7b.  
This can be attributed to the more confident decision-making by the proposed algorithm as it checks the LUT conditions for the average value of the past seven instances. Additionally, it can be attributed to the faster response of the proposed algorithm to higher mobility UEs due to its variation in the frequency of MRs with mobility.  
Figure 6: (a) Number of ping-pong handovers and (b) ping-pong handovers as a percentage of total handovers.
## 4 Description of the Application Problem and Employed Multi-Fidelity Models
### Determination of Significant Parameters  
#### 5.2.1 Selection of Screening Points and Results of the HF and LF  
The objective of the screening stage is to identify the input parameters whose variation generates significant effects on the variable of interest. To achieve this goal, it is advisable to work with \(2^{N-p}\) factorial fractions, where \(k\) represents the number of factors (\(k=9\) in this paper) and \(p\) the number of partitions (\(p=2\) in this paper) to the total set of simulations using the full factorial design (\(2^{N}=2^{9}=512\)). The results obtained from a set of simulations designed by fractional factorial design always have a lower resolution than those obtained from a full factorial design. When the number of factors is high, the decrease in resolution is usually compensated by a remarkable reduction in the number of simulations. Using this method, 128 simulations obtained as a quarter of the full factorial design (\(2^{9-2}\)) were generated. Simulations were executed for the 3D Model and the 0D Model. Target and intermediate variables were computed on the basis of these simulated values. See the Supplementary Materials.  
High and low levels of characteristic parameters were complemented with several center points to improve the accuracy of the standard error of estimation. When condensation phenomena were not physically feasible due to the influence of local irregular flows or negligible amounts of condensed water, values of condensation mass flow were considered missing. Some other experiments were slightly modified to ensure condensation was produced for a more profitable data analysis. Based on this screening design, the variability in the five metamodeled variables was assessed for different combinations in the initial set of characteristic parameters.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 5 Case Study: In-Process Melt-Pool Monitoring  
### The AM System  
The case study dataset was generated at the Additive Manufacturing Metrology Testbed (AMMT) at the National Institute of Standards and Technology (NIST) [4]. The AMMT is a fully custom-made metrology instrument that permits flexible control and measurement of the LPBF process. An internally  
Figure 3: Interfaces linking function blocks.  
developed AM software (SAM) program [40] was used to evaluate different scan strategies. In this study, an altered scan strategy was selected.  
Wrought nickel alloy 625 powder and substrate, with a dimension of 4\({}^{\prime\prime}\)\(\times\) 4\({}^{\prime\prime}\)\(\times\) 0.5\({}^{\prime\prime}\), was used in this case study. Twelve rectangular parts (with chamfered corners) of dimensions 10 mm \(\times\) 10 mm \(\times\) 5 mm were laid on the substrate, with a minimum spacing of 10 mm between parts. In-process monitoring was based on two cameras: one high-resolution camera for layer-wise images and a high-speed camera to get the melt-pool images. The Galvo mirror system and the beam splitter permit the high-speed camera to focus on the current, laser-melting spot. Emitted light from the melt pool, through an 850-nm bandpass filter (40 nm bandwidth), was imaged on the camera sensor. The trigged time of the melt-pool camera was 500 \(\upmu\)s.
###### Abstract
This improvement is more concise and practical. To validate the efficacy of Auto-T-YOLO in remote sensing images object detecting tasks, experiments on the public SAR Ship Detection dataset (SSDD) are also conducted. There are two types of images in the SSDD: inshorse and offshore. We evaluated the models on each type of dataset. The present study contributes to the literature in the following ways:  
* Auto-T-YOLO is divided into preattention, attention, and prediction stages. The preattention module is introduced into the object detection network. The preattention module automatically classifies the dataset according to the target size. The operation of this module is used to simulate the process of human vision. In the attention module, the image features are selectively focused autonomously and efficiently. Finally, the prediction module performs the prediction frame accuracy evaluation mathematically. * During the preattention stage, Auto-T-YOLO automatically classifies the images in the dataset according to the target size. The definition of target size is learned from the Microsoft Common Objects in Context (MS-COCO) dataset. Two types of images in the dataset are classified: the type with large-size targets and the second type with only small-size targets. In the experimental section, we analyze the definition of the large-size target setting. * In the attention stage, the MHSA module is introduced into the YOLOv4 network framework by replacing the 3 \(\times\) 3 convolution block with the MHSA module after the SPP module. * To analyze the performance of Auto-T-YOLO, two types of accuracy evaluation measures are employed. First, \(P_{d}\) (fault detection rate), \(P_{m}\)(missing alarm rate), and \(P_{f}\) (false alarm rate) are evaluation indices in remote sensing. For deep learning object detection, the evaluation indexes are _precision_, _recall_, F1-score (\(F1\)), mean Average Precision (\(mAP\)), and Frames Per Second (\(FPS\)). The SSDD was divided into two subdatasets: inshore and offshore scenarios. Not only is the whole SSDD detected, but also Auto-T-YOLO detects the two subdatasets mentioned above. The remainder of the study consists of the following: In the second section, this work describes the current status of research on some detection methods. In the third section, this study describes the detection method and specific improvements of the model. In the fourth section, this study proposes the Auto-T-YOLO detector and verifies the model's superiority on public SAR datasets over competitive models. The last part presents the conclusions and possible future extensions of the study.
## 3 Architecture and Problem Formulation
### System Model
#### 3.2.1 Hybrid Interference Model  
Interference experienced by either the HTC and or the MTC users is modeled considering the interactions in the proposed hybrid coexisting environment. Inter-domain is the interference between HTC and MTC nodes whereas intra-domain is the interference between either amongst MTC or HTC devices, respectively. The intra-domain interference received by a \(k_{th}\) transmitting mMTC node from other \(i_{th}\) mMTC nodes in a cluster at any time \(t\) is expressed as:  
\[i_{k}(t)=\sum_{i=1,\ i\
eq k}^{K}p_{i}(t)G_{ijk}(t), \tag{1}\]  
where \(p_{i}(t)\) is the transmit power required for \(i_{th}\) MTCD, \(i\in K\) and \(G_{ijk}(t)\) is the experienced channel gain between \(k_{th}\) and \(i_{th}\) MTCDs, \(i\in K\), respectively. Here, (1) describes the interference caused by a single \(k_{th}\) MTCD transmitting device to all the other MTCDs at time \(t\). The inter-domain interference is defined as an interaction between MTC and HTC users along with any possible MTCGs. The inter-domain interference received by a \(k_{th}\) MTCD from \(j_{th}\) HTC node can be defined as:  
\[I^{\prime}_{k}(t)=\ \sum_{j=1}^{J}P_{j}(t)G_{ijk}(t), \tag{2}\]  
where \(P_{j}(t)\) is the transmit power of the any \(j_{th}\) HTC device and \(G_{ijk}(t)\) is the experienced channel gain between any \(j_{th}\) HTC and the \(k_{th}\) MTCD device. Now, the achievable SINR for any \(k_{th}\) MTCD with a transmit power of \(p_{k}(t)\) and channel gain of \(g_{k}(t)\) with a noise variance \(\delta^{-2}\) at time instant \(t\) is:  
\[\Gamma_{k}(t)=\ \frac{p_{k}(t)g_{k}(t)}{i_{k}(t)+I^{\prime}_{k}(t)+\ \sigma^{2}}, \tag{3}\]  
Figure 1: A coexisting HTC/MTC cellular model.
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
## 3 Fractional Fourier Transform (FrFT)
### Fading  
Fading is the level of the received power, which oscillates due to the nature of multipath propagation in an underwater environment. In shallow water, fading is a result of wave reflection from the surface and bottom, while in deep ocean zones, it is due to a natural phenomenon known as refraction. On the one hand, multipath fading causes the transmitted signal to travel through many paths to reach the receiver with different delay spreads. These delays overlap each other and cause inter-symbol interference (ISI), which in turn points towards a frequency-selective channel. Sound travels at a velocity of 1500 m/s underwater [1]. This introduces additional delay spread. The approximate delay spread of typical underwater channels is 10 ms, but sometimes delay spread can be as large as 50 to 100 ms.  
On the other hand, the underwater acoustic environment is subject to surface scattering as well as internal waves, which introduces the time selectivity of the channel model. Doppler spreads are dominated by sea surface conditions and transmitter or receiver motion. The receiver design of the UWA-OFDM system has to employ effective techniques to compensate the Doppler spread for proper operation.
# Automated Measurement of Grid Cell Firing Characteristics
## 4 Step-by-Step Usage Protocol
### Preprocessing Data  
1. The program smooth_rate_map is supplied to convert spike recordings into a rate map and optionally smooth the results. This step should only be used to preprocess recording datasets that are not yet in a rate map or autocorrelogram format. Creating a rate map requires pos_x.csv and pos_y.csv files that, respectively, report the x and y locations of an an imal during an experiment in two-dimensional space. A spikes.csv file is also required that reports the times spikes occurred during the experiment. Example data can be downloaded from the converted/med_scl_med_fr_example folder at osf.io/ud27p to test the creation of a heat map. A user should enter the location of those files into the Xs_file, Ys_file, and spikes_file variables of config_convert/rate_map_config.txt, respectively. A user should first run smooth_rate_map/config_convert/run_config_convert_win.bat (Windows) or smooth_rate_map/config_convert/run_config_convert_linux.sh (Linux) to create the script to run smooth_rate_map. The user should then run smooth_rate_map/run_rate_map_smooth.bat (Windows) or smooth_rate_map/run_rate_map_smooth.sh (Linux) to generate rate_map.txt in the smooth_rate_map/saved_results folder.  
2. A rate map of neural firing (typically generated by experimental recording or a computational simulation) must be supplied to the software in the form of a 32 \(\times\) 32 matrix of data. Alternatively, a 63 \(\times\) 63 matrix that is an autocorrelogram can be supplied if the use_ac configuration option is set to true. Either of these matrices is the file used in the later-described plot_filepath variable. The example data provided with the release includes a file named \"example_rate_map.txt\" (in the heat_maps_real_custom folder), which can be used to test the program.  
3. The software comes with pre-converted example rate map data in the heat_maps_real and heat_maps_sim directories from our previous work [10], and additional example data are also available if desired [14; 15]. These resources contain the original animal recordings and other files used to create real and simulated rate maps [10]. That simulation work used Hippocamp.org as a primary source of evidence for modeling settings [16; 17]. Most data in these directories are in a binary format; therefore, the option use_binary_input should be set to 1 in config.txt to correctly input those files.
## 3 Results and Discussion
### Data Analysis  
#### 3.3.1 First Arrival Picking  
For illustration purposes, we applied the proposed method to the 12 receivers between shots 1 and 3 (Figure 3b,c). Far-offset receivers were not used because the signal became too weak to pick confidently. Figure 4 shows the first and third shot gathers of the real data set. Arrival travel times were picked manually for both direct and head waves. For the direct wave, the first arrival travel time was picked only on the two receivers closest to the source since direct waves were observed only on these traces. The first picked arrival travel times of the direct wave from all shots are shown in Table 1, whereas the picks of the head wave from the down-dip (shot 1) and up-dip (shot 3) are shown in Table 2.
# A Robust Intelligent Controller for Autonomous Ground Vehicle Longitudinal Dynamics
## 2 Vehicle Modeling
### Simplified Vehicle Longitudinal Model  
To synthesize the controllers, the bicycle model, which presents a simplified version of the four-wheel model, is often used (see Figure 3).  
By considering only of the vehicle's front wheel, using Equation (1) the bicycle model front-wheel dynamics are expressed as [34]:  
\[\mathfrak{h}_{V}\dot{\mathcal{W}}_{f}=-\mathfrak{f}_{xf}\mathfrak{v}_{w}+ \mathcal{T}_{p}-\mathcal{T}_{b} \tag{8}\]  
where \(\mathcal{W}_{f}\) is front-wheel rotation speed, \(\mathfrak{h}_{V}\) is the front-wheel moment of inertia, \(\mathfrak{v}_{w}\) is the front-wheel radius, and \(\mathcal{T}_{p}\) and \(\mathcal{T}_{b}\) are the propulsion and braking torque, respectively.  
From Equation (9), we can write \(\mathfrak{f}_{xf}\) as:  
\[\mathfrak{f}_{xf}=\frac{1}{\mathfrak{v}_{w}}\Big{(}\mathcal{T}_{p}-\mathcal{T }_{b}-\mathfrak{h}_{V}\dot{\mathcal{W}}_{f}\Big{)} \tag{9}\]  
Figure 3: Four-wheel vehicle model and bicycle model.  
Figure 2: The vehicle’s longitudinal tire–road contact forces.  
Based on the hypothesis defined in [34], Equations (10) and (11) are given:  
\[v_{x}=v_{w}\mathcal{W}_{f}\] \[\mathcal{W}_{f}=v_{g}\mathcal{W}_{e} \tag{10}\] \[\mathcal{T}_{e}=v_{g}\mathcal{T}_{p} \tag{11}\]  
where \(v_{g}\) denotes the transmission gear ratio, \(\mathcal{W}_{e}\) is the engine speed, and \(\mathcal{T}_{e}\) is the engine torque.  
Based on Equations (9)-(11), Equation (1), which represents the longitudinal vehicle dynamics, is written as:  
\[\tilde{x}=-\frac{\tau_{w}}{m\tau_{w}^{2}+\mathrm{t}_{\mathcal{W}}}\Big{(} \mathcal{T}_{p}+\tau_{w}\Big{(}\mathfrak{f}_{zero}+\mathcal{R}_{xf}+mg\sin \theta\Big{)}\Big{)}+\frac{\tau_{w}}{\mathrm{r}_{g}(m\tau_{w}^{2}+\mathrm{t}_ {\mathcal{W}})}\mathcal{T}_{e} \tag{12}\]