esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Pedestrian Inertial Navigation Algorithm Based on RBCN-Net
### IMU Coordinate System Normalization  
Pedestrians use their phones in a variety of ways, making the phone's built-in IMU sensor coordinate system unstable. In extreme cases, the coordinate system of each frame of collected IMU data has different tilt angles relative to the ground, resulting in inconsistent motion representation and making the feature learning of the network very difficult. A stable IMU data frame will make the neural network regression task easier. Therefore, we use the coordinate system normalization method proposed by RIDI [9] to convert all acceleration and gyroscope \([a,w]_{i}\) to a coordinate system aligned with the direction of gravity.  
Figure 3: Diagram of the equipment of the two cell phones: Google Tango is strapped to the chest area and the IMU phone (Huawei Mate 20) is used normally, including entering text, shaking it handheld, making phone calls and placing in the pocket of clothes.  
In this paper, a Gaussian smoothing [27] with a standard deviation of 0.2 is applied to the bit-pose data provided by Google Tango to suppress the high frequency. we assume that the experimental walking ground is very flat, i.e., vertical displacement is zero, and the gesture data are converted to the velocity components \((v_{x},v_{y})\) in the two-dimensional plane. A normal human gait cycle lasts approximately one second [28], and the sampling frequency of the IMU data acquisition application designed for this experiment is 200 Hz, so when the sliding window mechanism is used to slice \([a,w]_{i}^{n}\), the size of the sliding window is 200 frames of data, and the time length of the divided IMU sequence \([a,w]_{i=1\sim 200}^{n}\) is approximately one gait cycle. This is more conducive to the subsequent network training and learning. The label for the \([a,w]_{i=1\sim 200}^{n}\) is the corresponding pedestrian real velocity vector \((v_{x},v_{y})\) collected by Google Tango.
## 3 Experiments and Result  
### Radar and Experimental Processing  
The radar used in the experiment is the Bitsensing MOD630 FMCW RADAR, and Table 2 shows the radar specifications.  
Figure 11 shows the experimental setup and the positions where motion capture sensors were attached. The experiment was conducted in a hallway with a width of 1.85 m. Participants walked approximately 5 m, from the 1-meter mark to the 6-meter mark, with their backs facing a radar installed at a height of 1.15 m. The radar signals were converted into a Doppler map, and the proposed method was used to automatically extract Doppler envelopes corresponding to each body part (Spine, Leg, Foot). Ground truth data was obtained using motion capture sensors, which were attached to the top of Foot, Leg, and Spine, in order to measure the actual speed of each body part during walking. The motion capture system used in this study was the Perception Neuron Studio (Noitom Ltd., Beijing, China), which provides full-body wireless tracking at an output rate of up to 240 Hz. A total of five inertial sensors were used, attached to Spine, both Legs, and both Feet. The marker placement followed a standard full-body tracking protocol to capture the representative motion of each region. The experiment was conducted with five participants, each with different physical characteristics, and Table 3 shows the physical conditions of the participants.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Center frequency & 60 GHz \\ \hline Chirp duration & 240 μs \\ \hline Sampling frequency & 1.2 MHz \\ \hline Scan interval & 50 ms \\ \hline Bandwidth & 3 GHz \\ \hline Number of channels & 8 \\ \hline \end{tabular}
\end{table}
Table 2: MOD630 FMCW radar specifications.  
Figure 10: Envelope extraction process: (**a**) speeds corresponding to level powers 3.19, 6.38, and 9.58; (**b**) speed points corresponding to Spine, Leg, and Foot; (**c**) extracted Spine, Leg, and Foot envelopes.  
\begin{table}
\begin{tabular}{c c c} \hline
**Subject** & **Height (cm)** & **M/F** \\ \hline
1 & 160 & F \\
2 & 170 & F \\
3 & 165 & F \\
4 & 170 & M \\
5 & 180 & M \\ \hline \end{tabular}
\end{table}
Table 3: Physical characteristics of experimental subjects.
## 3 Current Clinical Treatments: Hypothermia  
Hypothermia is considered one of the most effective neuroprotective treatment interventions in both experimental and clinical settings. While the exact beneficial effects of hypothermia are yet to be elucidated in HIE, it is known to suppress many of the neuro-damaging events associated with HI including excitotoxicity, apoptosis, inflammation, oxidative stress and BBB disruption [53].  
In HIE, moderate hypothermia (33.5 \({}^{\circ}\)C for 72 h), applied within 6 h of birth, improves the survival and neurodevelopmental outcomes in infants suffering from moderate and severe HIE. While hypothermia is now considered standard care in the treatment of HIE and is well tolerated in term infants [54; 55; 56; 57], it does not provide complete protection. Of the infants who have moderate or severe encephalopathy, therapeutic hypothermia was shown to decrease mortality from 40% to 28%,while in surviving infants neurological morbidity was reduced from 31% to 19% of infants receiving therapeutic hypothermia [58].  
Importantly, while hypothermia is safe in pre-term infants with necrosis enterocolitis [59], it has not been adequately evaluated for use in pre-term infants suffering HIE. Information regarding the safety and efficacy of hypothermia to treat pre-term infants suffering HIE is lacking, although information may be available in the near future (NCT1793129). In one study, hypothermia in pre-term infants was associated with higher mortality and increased clinical complications when compared to term infants [60], although the study did not include a morphometric pre-term control group. The inadequate application of hypothermia in the pre-term neonate highlights the need for the development of safer therapeutic interventions in these patients following HI.  
In addition, the induction of hypothermia requires specialized equipment, intensive care monitoring and trained staff, limiting its use to centers that have the facilities and medical personnel to undertake the procedure. This develops uncertainty regarding the timing, safety and efficacy of initiating hypothermia in infants before transfer to a centre equipped to deliver hypothermia. For example, the transfer of infants suffering HIE to a medical centre to provide hypothermia can exceed 6 h from the time of birth and that attempts of passive cooling during transport can result in excessive cooling [61]. In light of this, there is an urgent need for the application of an acute neuroprotective therapy for infants who suffer HIE when hypothermia cannot be applied immediately, or is not available. In addition, as the incidences of mortality and morbidity are still relatively high following hypothermia, there is a requirement for additional therapeutic agents, adjuvant to hypothermia, to further improve outcomes in infants suffering HIE.
## 2 Materials and Methods
### Chemicals and Instruments  
All reagents and standards were of analytical-reagent (AR) grade. L-ascorbic acid (L-AA) (>99.8%), [PERSON]'s phenol reagent 2N solution (for protein analysis), 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) (>98%), 2,2-diphenyl-1-picrylhydrazyl (DPPH) (>95%), (+) catechin hydrate (>99%), metaph phosphoric acid (MPA) (33.5-36.5%), gallic acid (>98%), sodium hydroxide (>98%), acetic acid (>99%), and ethylene-diaminetetraacetic acid disodium salt (EDTA) (98%) were purchased from Sigma-Aldrich (St. Louis, MI, USA) MERCK KGaA affiliate (Darmstadt, Germany); sodium nitrite(>97%) was from Honeywell Fluka (Seelze, Germany); sodium carbonate anhydrous (RPE), methanol (HPLC grade > 99.9%), n-esane (HPLC grade > 99.9%), and acetone (HPLC grade) were from Carlo Erba Reagents (Cornaredo (MI), Italy); aluminum chloride hexahydrate (99%) was from Titolchimica S.p.A (Pontecchio Polesine (RO), Italy); and butylated hydroxytoluene (BHT) (99%) was from AlfaAesar Thermo Fisher (Kandel, Germany). The color parameters were measured by a UV-Vis Varian Cary 100 Spectrophotometer (Varian Agilent, Santa Clara, CA, USA). The ultrapure water was supplied by an ion exchange system, Milli-Q, which was fed by a reverse-osmosis system, Elix 3, both from Millipore (Merck group, Darmstadt, Germany). A digital penetrometer (T.R. Turoni s.r.l., Forl, Italy) was used for fruit firmness; DBR 35 portable digital refractometer (Singerica Soluzioni s.r.l., Pescara, Italy) was used to measure total soluble solids content. The acidity (pH value) was determined by a pH meter (MultiLineTM F WTWTM, Wilhelm, Germany) fitted with a WTW--Low-Maintenance SenTixTM 41 pH electrode and a temperature sensor. A cflx-eztomato2 (Hunter Lab) was used to assess fruit color.
## 2 Motivation
### Bird Nest Monitoring  
Avian ecologists study the behavior of birds during the nesting season to answer biological questions that relate to the laying, incubation and hatching of eggs and the fledging and survival of nestlings, primarily during the breeding season, which can last up to three months during spring. Usually, a minimum of 30 nests of a single species of bird is needed to provide a statistically robust analysis (to achieve this, a researcher studying birds in nestboxes would typically place twice that number of nestboxes in the field to give ample \"choice\" to birds in selecting nest sites, with the hope of achieving 50% occupancy). Each of these nestboxes needs to be checked manually on a regular basis to determine which are occupied by birds and then monitored regularly (in some cases, daily) to take data on the breeding behavior of interest. Such efforts can require a considerable investment of time and labor. The low rate of data sampling makes answering many scientific questions infeasible. In addition, manually opening and inspecting the nestboxes to obtain data can create a disturbance that can result in adult birds abandoning their nest.  
To this end, the goal of our deployment is to demonstrate a system that can be used by ecologists to continuously observe the interior of nestboxes spread over a large area for the duration of the breeding season with minimal disturbance to the breeding birds. Our work is focused on recording still images inside nestboxes using a wireless camera system to record bird behavior. In addition, we are measuring environmental characteristics of the immediate nesting environment (_i.e._, inside the nestbox), including temperature, humidity and dew point, as well as near the nesting environment (_i.e._, outside of the nestbox). The environmental data and associated nestbox images are being used to answer questions about bird breeding behavior and breeding success. The questions fall into the following areas:  
* Nestbox use during the non-breeding season;
* Environmental variation among nestboxes with respect to nest site selection and adult breeding behavior;
* Inter-species competition for nestboxes;
* Laying patterns and behavior;
* Incubation and hatching (pattern/time, asynchrony);
* Fledging (variation among nestlings within a nest).  
Biologists can gain insights into the biology of the birds based on the data that have been collected through our system, and this data can help answer significant biological questions, as discussed below.
## 1 Introduction
### Objectives
#### 1.1.3 Accuracy Optimization  
Our two proposed accuracy optimization techniques are designed to use accuracy assessment information to optimize the overall accuracy of the final map product by ensuring verified mapped change locations and removal of erroneously mapped changes. The purpose of the first technique we propose is to eliminate commission errors in change maps. To achieve this, all locations predicted to have changed need to be verified. Since manual photo interpretation is routinely used as \"ground truth\" data, our process includes photo interpretation of all predicted change locations, in as much as each photo is viewed by an analyst and the presence of an anthropogenic or natural disturbance is noted. With this approach to error characterization all commission errors occur within the predicted change area while all the omission errors remain in the predicted non-change area. The area of predicted change is generally much smaller than the non-change area for the landscapes we are working in over our two- to three-year imagery intervals. Therefore, the task of interpreting just the locations with potential commission error is much smaller than the task of interpreting locations of potential omission error.  
Figure 1: Confusion matrix for mapping land cover change.  
The second technique described here is made possible as a result of interpreting all the predicted changes. For a binary model, logistic regression for example, predicted changes are those for which the model assigns change as the most probable class. With a two class model, 50% is the minimum prediction threshold for assigning an analysis unit to the change category. As previously stated, the problem with lowering this minimum acceptance probability is increasing the model's commission errors. Since we check all potential commission errors, this threshold can be lowered at a cost of additional interpretation time as opposed to increased model error. As the threshold is lowered, additional photo interpretation effort will increase. Thus, the optimal threshold is somewhere between 0 and 50%.
## 4 Results
### Sensor Network Node Simulation  
To analyse the real-time deployment of a sensor network in different aspects of increasing the number of nodes, area length, and width, a simulation model of sensor network nodes was constructed using MATLAB. The sensor readings from time to time, as well as the size of the data packet, can be predicted using the simulation of two alternative numbers of nodes, 20 and 80, in the area to simulate the wireless sensor network. The detached sensor nodes communicate with other nodes or the base station within their working distance. Therefore, the nodes located between the base station and other nodes could be used as retranslators.  
Figure 7 illustrates a sensor network with randomly distributed nodes. Additional criteria considered for the networking simulation include area length and width, projected sensor readings per minute, and data packet size. The solid blue lines represent the forward communication, and the dotted lines represent the acknowledgement sent back. The best location the base station and retransmitting nodes are shown.  
Figure 6: Overview of hosting PHP application and MySQL database.  
As seen in Figure 7a, the information communication path to the base station is relatively easy when a smaller number of nodes is used. When the distance to the base station is short, the network does not use any of the intermediate nodes. However, some nodes are out of reach of the base station, which only communicates with it through the intermediate nodes. Therefore, nodes with longer working distances should be applied for a reliable connection. Figure 7b shows that when the number of nodes is high, there are many ways to transfer the information to the base station. In this particular case, retranslators are planned to implement in multiple places to reduce the workload of the nodes.  
When the nodes have multiple tasks, they are forced to be in an ON state to follow up on the communication protocols. In this case, more energy is consumed. Retranslators are a suitable solution to reduce the power consumption. They reduce the workload on the individual nodes while retaining the specific memory of previously sent data packets, increasing the lifetime of the sensor nodes. Various topologies and data acquisition for rapid deployment will be considered in future work.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 3. Maritime Federated Learning Use Cases
### Security and Privacy Protection  
Security and privacy protection requirements in maritime applications restrict the transmission of sensitive data to centralized nodes for model training purposes. Therefore, FL has received significant attention in the literature. The authors of [17] proposed a part-FL (PFL) approach where the collected data might not be identically distributed, and the algorithm only used a portion of the data that were uploaded to the general global model. Hence, the privacy of the algorithm was improved, while signaling overheads were reduced. PFL divided the original model into a shared model part and a non-shared model part. Optimization included the optimal allocation of the segmentation points to reduce the total communication and computation delay between the client and the server when the channel information is known. Finally, the advantages of PFL in processing non-i.i.d. data were shown through the experimental results, while its feasibility was verified by simulation analysis of the parameter optimization process.  
In [18], the authors deal with intrusion detection in maritime transportation systems. Compared with centralized servers, IoT devices are more vulnerable to cyber-attacks due to power, memory, and processing limitations. Here, a convolutional neural network (CNN) model based on a multi-layer perceptron (MLP) was used, where the CNN provided data feature extraction and the MLP performed attack classification. Moreover, the straggler problem of FL was studied, referring to the inability of the involved devices to upload their data in a timely manner. As a mitigation measure, batch federated aggregation was proposed and evaluated in both i.i.d. and non-i.i.d. scenarios, showing that batch federated aggregation suppressed model parameter oscillations during FL.  
Next, ref. [19] used FL to protect a maritime transportation system against Byzantine attackers. A privacy-preserving gradient aggregation algorithm was developed using secure two-party calculation. A key novelty of the proposed method was the introduction of credibility to measure the trustworthiness of a ship's sharing knowledge, updating itscredibility based on the sharing information in each epoch. Then, a guiding gradient vector relying on the credibility was designed, comparing the similarity between a ship's gradients and the guiding gradients to efficiently determine whether the ship was a Byzantine node or not.  
In a maritime IoT setting, ref. [20] combined FL with blockchains to ensure privacy protection and proper selection among the edge nodes. The blockchain served as a decentralized method, which stored FL workers to guard against tampering and increase security. FL processed the original data and only stored the calculation results, thus reducing the cost of storage resources and addressing the issue of the limited energy of edge devices. Also, a proof-of-parameter quality consensus mechanism for the opinion blockchain was designed, aiming to add higher-quality workers to the blockchain.
# Preparation of an Asymmetric Membrane from
## 3 Results and Discussion
### Characterization of Asymmetric Membranes
#### 3.3.5 Performance and Fouling Evaluation of the Membranes  
The performance and rejection of the prepared membranes were studied through filtration experiments. The permeate flux of two membranes at a pressure of 0.1 MPa is summarized in Table 5. The results match with the results of the contact angle and water content tests, which indicate the hydrophilicity and the high flux of the prepared membranes. The BSA rejection of the CAD membrane is 85.7% and that of the CADA membrane is 87.3%. These results are better than the standard CA membranes which synthesized from commercial CA and NMP solvent in publication [48] as well as DMAc solvent [49].  
A disadvantage in practical application of membrane technologies is the deterioration of permeability and selectivity because of membrane fouling. The antifouling properties of the synthesized membranes have been elucidated by a two-step filtration test (see experimental section for details) has been carried out using BSA as a model protein. A higher FRR and \(\mathrm{R_{r}}\) indicate that a large fraction of the fouling effect is reversible and the greater part of the flux could be recovered after the protein filtration process with hydraulic cleaning of weak foulant deposits [46; 50]. The calculated flux recovery data are summarized in Table 6. This indicates that the prepared membranes have a great antifouling property, in which the CAD membrane has a much higher antifouling ability than the CADA membrane. Moreover, the FRR value of CAD and \(\mathrm{R_{r}}\) values are 80% and 35% higher, respectively, which indicates the high efficiency of hydraulic cleaning.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Membranes** & \(\mathbf{W_{C}}\) **(\%)** & **Contact Angle (\({}^{\circ}\))** \\ \hline CAD & 78.34 & 55.6 \(\pm\) 1.6 \\ CADA & 75.09 & 58.4 \(\pm\) 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Water content and contact angle of as-prepared membranes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Membrane** & \begin{tabular}{c} **Pure Water** \\ **Flux Jw1** \\ **(L m\({}^{-2}\) h\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Permeation** \\ **BSA Flux Jp1** \\ **(L m\({}^{-2}\) h\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Pure Water** \\ **Flux after** \\ **(L m\({}^{-2}\) h\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Permeation** \\ **BSA Flux Jp2** \\ **(L m\({}^{-2}\) h\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Pure Water** \\ **Flux after** \\ **(L m\({}^{-2}\) h\({}^{-1}\))** \\ \end{tabular} \\ CADA & 314.41 & 136.24 & 288.21 & 128.38 & 248.91 \\ CADA & 301.31 & 131.01 & 248.91 & 99.56 & 196.51 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the pure water flux, permeation bovine serum albumin (BSA) flux, and water flux after cleaning of as-prepared membranes.  
Figure 9: A snapshot of the contact angle of prepared membranes: (**a**) CAD and (**b**) CADA membranes.