esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hardware Development  
The methodology under which the hardware was designed and developed is the \"V\" model. This methodology is used to ensure the probability of success in embedded systems and consists of seven main stages [15]. The development of the PCB was carried out considering some international electronic design standards [16];, which are listed below  
* IPC-2221A [17]: This defines the requirements for PCB design and establishes design principles and recommendations, including component mounting (Through-Hole Technology (THT) or Surface Mount Technology (SMT)), material selection, and thermal management, among others.
* IPC-2231 [18]: This provides guidelines for establishing a best practice methodology for use in developing a formal DFX (design for manufacturing, assembly, reliability, etc.) process for layout of printed board assemblies that utilize THT and SMT devices.
* IPC-A 610 [19]: Pictorial interpretive document that indicates various characteristics of the board and/or assembly as appropriate relating to desirable conditions that exceed the minimum acceptable characteristics indicated by the final item performance standard.
* IPC-D-325A [20]: This establishes requirements and other considerations for the documentation of printed boards and printed board assemblies.  
In addition, the developed device takes into account the following Mexican standards:  
* NOM-259-SE-2022 [21]: Systems for measuring and dispensing LP gas. Requirements and specifications.
* NMX-I-1362-NYCE-2021 [22]: Simple encryption procedure for Internet of Things (IoT) environments.
* NMX-I-4903-NYCE-2021 [23]: Key performance indicators related to smart and sustainable cities, to assess the achievement of sustainable development goals.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 3 The NDMR Approach
### NDMR Operations  
The proposed NDMR approach includes the following steps:  
* Path discovery
* Frame forwarding
* Path maintenance  
Figure 2: The structure of NDMR control messages.  
When a source node needs to communicate with a destination node for which it has no routing information in its route table, NDMR initiates the path discovery process by sending control messages to find two node-disjoint paths between the source and destination nodes. Upon receiving the control messages, intermediate nodes along the discovered paths build and maintain their route table. The source then forwards frames to the destination through the two established paths. During the communication, the path maintenance process is used to check and maintain the two active paths between the source and destination nodes.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
## 2 Methods
### Performance Evaluation
#### 2.4.1 Model Performance  
* Road anomaly classification  
To assess the performance of the URA-VLMs, the presence of road anomalies, and the accuracy rate of the sub-type, and the overall accuracy were defined as follows:  
\[Acc=\frac{\sum_{i=1}^{n}F(i)=\begin{cases}1\;if\;right\\ 0\;if\;error\end{cases}}{N} \tag{1}\]
* Road waterlogging depth estimation  
Additionally, the relative error rate was computed to quantitatively evaluate the accuracy of the URA-VLMs flood depths estimations against the manually determined estimations. The relative error was expressed as a percentage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & \multicolumn{3}{c}{**Model Accuracy (\%)**} \\ \cline{3-6}
**No.** & **Types** & \begin{tabular}{c} **Number of Data** \\ **Points** \\ **(Testing Dataset)** \\ \end{tabular} & \begin{tabular}{c} **ResNet34** \\ **(First Approach:** \\ **Linear Probing)** \\ \end{tabular} & \
\begin{tabular}{c} **ResNet34** \\ **(Second Approach:** \\ **Not Finetuned,** \\ **Training)** \\ \end{tabular} &
\begin{tabular}{c} **ResNet34** \\ **(Third Approach:** \\ **Finetuned, Other Layer** \\ **Weights Are 1 Times** \\ **LR, and the Last FC** \\ **Layer Is 10 Times)** \\ \end{tabular} \\ \hline
1 & Bad road & 214 & 79.44 & 78.04 & 79.91 \\
2 & Fallen tree & 296 & 81.08 & 93.58 & 90.54 \\
3 & Fire & 305 & 91.8 & 84.92 & 82.3 \\
4 & Flooding & 310 & 62.42 & 53.73 & 64.91 \\
5 & Garbage & 364 & 67.03 & 70.88 & 73.08 \\
6 & Normal & 675 & 89.93 & 75.7 & 86.81 \\
7 & Traffic & 433 & 92.15 & 66.74 & 63.97 \\  & accident & & & & \\
8 & Traffic jam & 437 & 79.44 & 78.04 & 79.91 \\  & Overall & & & & \\  & accuracy (\%) & 3034 & 81.48 & 75.31 & 77.38 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Testing ResNet34 of three approaches.  
\[Error=\frac{\sum_{i=1}^{n}\min\left(\frac{|a_{i}-b_{i}|}{b_{i}},1\right)}{N} \tag{2}\]  
Furthermore, the Mean Absolute Error (MAE) was calculated to measure the accuracy of the URA-VLMs estimations in comparison to the manual estimations (refer to Formula (3)). In this context, 'a' represents the described value, while 'b' denotes the labeled value, thereby expressing the average value of the relative error.  
\[MAE=\sum_{i=1}^{n}\frac{|a_{i}-b_{i}|}{n} \tag{3}\]  
* Road safety level To assess the performance of the URA-VLMs for the green, light green, and yellow road safety levels, the level accuracy rate was defined as follows:  
\[Safety\;Level\;Acc=\frac{\sum_{i=1}^{n}F(i)=\begin{cases}1\;if\;right\\ 0\;if\;error\end{cases}}{N} \tag{4}\]
## 3 On-Chip Circuit Implementation
### Electrical Stimulation Current Generator  
A high output impedance 6-bit current digital-to-analog converter (DAC) is used to generate the bi-polar stimulation current and the schematic is shown in Figure 8. Although bi-polar current pulses are used, charge neutrality has to be ensured to avoid damaging the human tissue. To guarantee charge neutrality, usually a large DC blocking capacitor is added to the electrodes [21]. However, it is not easy to integrate large on-chip capacitors, and precise charge-balanced biphasic current stimulator and single-channel bipolar current stimulator have been proposed in the literature [21; 22; 23].  
In Figure 8, the stimulation current pulse amplitude is controlled by a 6-bit digital input from the digital controller. The current enable signal is \"En\" and the current direction indicator is \"Polarity\". When \"En\" = 1 and \"Polarity\" = 1, \"SW1\" = 0 and \"SW2\" = 1, and the stimulation current will go out from S+ and come in from S\(-\). If \"En\" = 1 and \"Polarity\" = 0, then \"SW1\" = 1 and \"SW2\" = 0, and the current flows in from the opposite direction. When \"En\" = 0, \"SW1\" = \"SW2\" = 1, there will be no output current and both S+ and S\(-\) will be shorted to the ground. As the positive and the negative current pulses are generated by the same current source, the residual DC current only depends on the difference of their durations. As their durations are controlled by the 40 MHz recovered clock, the timing mismatch is minimized. Moreover, in every cycle, S+ and S\(-\) will be shorted in non-stimulation time to discharge the residual accumulated charge, and charge neutrality is ensured.  
Figure 8: 6-bit current digital-to-analog converter (DAC) for ES.  
Figure 7: (**a**) The 3-V low dropout regulator (LDO) for electrical stimulation (ES) generator and (**b**) the 1.2-V LDO for digital blocks.  
With reference to Figure 7a, \(\mathrm{V_{dp}}\) is designed such that \(\mathrm{V_{3V}}-\mathrm{V_{dp}}\) = 0.3 V. Now, the amplifier A1 generates \(\mathrm{V_{b}}\) that biases the current sources \(\mathrm{I_{0-6}}\) and the amplifier A2 generates \(\mathrm{V_{s}}\) that is equal to \(\mathrm{V_{dp}}\), to guarantee that the current sources will operate in the saturation region. Meanwhile, A2 and \(\mathrm{M_{P1}}\) act as a gain-boosting stage of the DAC output, which helps improve the output impedance.  
To improve the linearity and reduce the glitches, the two most significant bits are implemented in thermometer code [22]. The output current could be adjusted from 5 \(\mathrm{\mu A}\) to 320 \(\mathrm{\mu A}\) with a resolution of 5 \(\mathrm{\mu A}\)/step; the duty ratio can be adjusted from 2.5% to 20% with a resolution of 2.5%/step; and the stimulation pulse frequency can be set in the range of 10 Hz to 160 Hz with a resolution of 10 Hz/step by the external controller.
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 2 Methodology and Results
### Pressure Variation Model (PVM)
#### 2.2.1 Velocity Estimation
\[\Delta P_{d(T-B)}=D_{1}U^{2}\alpha+D_{2}q^{2}+D_{3}Uq+D_{4} \tag{7}\]  
\[\Delta P_{d(R-L)}=D_{1}U^{2}\beta+D_{2}r^{2}+D_{3}Ur+D_{4} \tag{8}\]  
Then, Table 4 shows the coefficients of determination obtained from regression analysis for each motion condition concerning sensor 1 through 11. Based on the results in Table 3, it can be confirmed that sensors 4 to 7 exhibited better estimation performance for velocity estimation, and based on the results in Table 4, it can be confirmed that sensors 2 to 5 showed reasonable performance for drift angle estimation. Then, sensors 4 to 7 were used for velocity estimation, and sensors 2 to 5 were utilized \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Motion** & **Sensor** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Gliding & \(R^{2}\) & 0.93 & 0.99 & 1.00 & 0.99 & 0.97 & 0.96 & 0.94 & 0.97 & 0.98 & 0.97 & 0.97 \\ (\(D_{1}\), \(D_{4}\)) & MAE & 29.42 & 16.00 & 6.05 & 12.05 & 18.91 & 24.77 & 35.44 & 22.39 & 18.21 & 21.29 & 26.42 \\ Turning & \(R^{2}\) & 0.98 & 0.99 & 0.99 & 0.99 & 0.98 & 0.94 & 0.52 & 0.64 & 0.20 & 0.84 & 0.94 \\ (\(D_{2}\), \(D_{3}\)) & MAE & 10.03 & 14.62 & 2.37 & 11.14 & 22.04 & 49.06 & 947.1 & 1097 & 1567 & 795.9 & 710.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The \(R^{2}\) and the MAE for each motion condition and pressure sensor. Figure 4: Flow chart of AUV state estimation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Motion** & **Sensor** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Straight & \(R^{2}\) & 1.00 & 0.99 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 \\ (\(C_{1}\), \(C_{0}\)) & MAE & 0.24 & 0.24 & 0.28 & 0.27 & 0.25 & 0.23 & 0.21 & 0.38 & 0.42 & 0.42 & 0.42 \\ Turning & \(R^{2}\) & 0.93 & 0.94 & 0.95 & 0.90 & 0.74 & 0.70 & 0.84 & 0.94 & 0.94 & 0.94 & 0.94 \\ (\(C_{2}\), \(C_{3}\)) & MAE & 6.62 & 2.34 & 0.27 & 0.33 & 0.32 & 0.30 & 0.26 & 0.24 & 0.37 & 0.39 & 11.48 \\ Gliding & \(R^{2}\) & 0.12 & 0.92 & 0.97 & 0.97 & 0.96 & 0.96 & 0.97 & 0.97 & 0.97 & 0.97 & 0.97 \\ (\(C_{4}\), \(C_{5}\)) & MAE & 24.87 & 17.28 & 5.25 & 3.18 & 3.30 & 3.22 & 3.30 & 4.36 & 5.60 & 6.26 & 6.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The \(R^{2}\) and the MAE for each motion condition and pressure sensor. for drift angle estimation to construct the inverse matrix. The PVM coefficients for each pressure sensor obtained using regression analysis are shown in Table 5, and details on the state estimation process are described in Section 2.2.2.
## 2 Materials and Methods
### Simulation of FOS/TAC Titration Curve  
It was possible to numerically simulate the titration curves based on the acid-base equilibrium theory. This allowed finding the relations that determined FOS and TAC accurately (see Section 2.3). The simulations were developed with the Scilab software (ESI Group, Rungis, France). The simulated results were validated experimentally with the titration of various solutions of pure sodium bicarbonate and sodium acetate mixture as presented in Section 2.1. The buffering effect of H\({}_{2}\)CO\({}_{3}\)/HCO\({}_{3}\)\({}^{-}\), H\({}_{3}\)PO\({}_{4}\)/H\({}_{2}\)PO\({}_{4}\)\({}^{-}\), H\({}_{2}\)PO\({}_{4}\)\({}^{-}\)/HPO\({}_{4}\)\({}^{2-}\), HPO\({}_{4}\)\({}^{2-}\)/PO\({}_{4}\)\({}^{3-}\) and NH\({}_{4}\)\({}^{+}\)/NH\({}_{3}\) was ignored since these acid/base couples were much less present at pH below 8 for most anaerobic digested samples.  
The simulation code is available in Supplementary Materials, as well as the flowchart of programming (Figure S1).
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 7 Conclusions  
This work proposes an IoT application designed to classify specific actions performed by older adults. It utilizes an embedded sensor in the STM32 WB55G microcontroller. The Android STBLE sensor app can also access the data on smartphones. This solution is notable for its simplicity, as it does not require additional wearable sensors that may inconvenience users, while remaining low-cost and energy-efficient.  
The STM32 WB55G microcontroller significantly simplifies the design process by providing an internal programming environment that allows for quick application creation, compilation, and debugging. The results indicate strong performance, confirming that the proposed system can accurately recognize the studied activities.  
The study aims to improve motion data transfer within smart homes, expanding capabilities beyond vital sign monitoring and fall detection. This work employs the Random Forest algorithm to recognize multiple activities from a device worn on the body, demonstrating reasonable accuracy across all activity classes.  
Integrating this IoT application into remote care services can broaden its utility for a larger population of patients. Healthcare professionals can monitor patient behavior through this system, offering valuable insights into their neurological status as part of a comprehensive human activity recognition system. The effectiveness of the chosen forecasting model encourages further investigations to expand the range of recognizable activities.  
In the future, there are plans to develop features that allow system customization to meet each user's specific needs, enhancing usability and acceptance. Additionally, creating more intuitive and accessible user interfaces for older adults will help reduce the discomfort and stress associated with using technology. This expansion aims to improve the overall management of users' lives.  
Conceptualization, L.B. and M.V.; methodology, L.B. and G.A.; software, L.B. and F.C.; validation, L.B., G.A., F.L., D.P., F.C. and M.V.; formal analysis, L.B., D.P. and F.L.; investigation, L.B. and F.L.; resources, L.B. and D.P.; data curation, D.P.; writing--original draft preparation, L.B. and M.V.; writing--review and editing, L.B., G.A., F.L., D.P., F.C. and M.V.; visualization, L.B., G.A. and F.L.F.; supervision, L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
Author [PERSON] was employed by the company Nophys srl. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Experimental Analysis and Implementation  
### Implementation Setup  
A WoO based Implementation setup for healthcare data interoperability has been developed (as shown in Figure 13). It constitutes a suite of microservices including a semantic annotation microservice, the semantic query processing microservice, and semantic alignment microservice which are deployed in the implementation setup. The annotation microservice enriches the contents from a domain object by linking the information with the semantic ontology. It involves associating extra information with the contents of an object corresponding to an ontology. The annotation of information helps the content to be machine-readable and interpretable in other domains. The query processing microservice handles the queries from the data analytics layer. It checks and maintains query logs and executes the query if the entry is available; otherwise, it generates a new query specific to the service requirement. The alignment microservice is invoked in case two objects, which need to interoperate in service, are using different data models to represent similar information. In this case, the alignment microservice aligns the concept from these data models to make them interoperable and usable to handle the service  
Figure 12: (**a**) A model of CDM as an Interoperable Shared Data Model (ISDM) with transformation process to shared data representation along with standard vocabulary, core and meta schemas. (**b**) An example of transformation to the Interoperable Shared Data Model (ISDM) through extracting entities from the domain-specific data model with their metadata description and their translation from the concepts of a domain to the generic entities.  
request. Besides, for the semantic description, a validator microservice has been defined that checks the description of annotated contents to verify its correspondence with the semantic ontology concepts and their relationships.  
Semantic alignment of data with base ontology has been achieved in these settings. A base ontology catalog has been formulated in a cloud-based setup of repositories implemented using the Jena Fuseki Server [59]. Also, linking with standard terminologies has been provided. An Interoperability processing server (IPS) has been set up to support these processing functions. The IPS provides functions to retrieve data elements and allow query processing services. Also, a SPARQL endpoint to support query processing interfaces have been set up. An Object Abstraction Management (OAS) server has also been established to deploy abstraction processing microservice which instantiates and maintains VO templates and provides an interface to retrieve VO profiles.
###### Abstract  
The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.  
Keywords: state-of-charge; equivalent circuit model; parameter identification; fuzzy fusion; multi-model combination +
Footnote †: journal: Applied Sciences
# DMCNet-Pro: A Model-Driven Multi-Pilot Convolution Neural Network for MIMO-OFDM Receivers
## 2 Dataset and Channel Model  
This paper focuses on the design of a \(2\times 2\) MIMO-OFDM wireless communication receiver. The \(2\times 2\) MIMO system strikes a balance between performance and simplicity with two transmit and two receive antennas. It effectively improves system capacity, reliability, and signal coverage, making it widely adopted across wireless communication standards. The required dataset for the experiments includes a \(2\times 2\) MIMO channel model and simulation programs for transmission and reception using the channel model. The wireless channel model follows the Wireless World Initiative New Radio (WINNER II) [11], and the channel scenario represents a typical urban street environment. In the simulation programs, the communication frequency was set to 1.85 GHz. The sampled channel data consist of 340,000 data samples, with each channel dataset containing 256 real numbers representing the channel matrix of a \(2\times 2\) wireless channel. Each element in the matrix consists of 32 complex numbers. The generated data are divided into training, testing, and validation datasets, consisting of 300,000, 20,000, and 20,000 samples, respectively.  
Multi-carrier modulation is implemented using OFDM with 256 subcarriers. The transmitter includes QAM of the transmitted bits X, insertion of pilot symbols, IFFT, addition of a cyclic prefix (CP) with a length of 32, and optional peak clipping. The settings for cyclic prefix and peak clipping can be adjusted through flags in the simulation program.  
Two transmit antennas generate random bit sequences of length 512. The combined bit stream of the two sequences is denoted as X, with a length of 1024. The random bit  
Figure 1: The link of a DL-based MIMO-OFDM receiver.  
signals are input into the simulation link, and the receiver obtains the signal y after passing through the channel. Since pilot symbols are added at the transmitter, the received signal y consists of two time slots: one for pilot symbols and one for data. Taking eight pilot symbols as an example, the transmitter first reads the known pilot sequence from a pre-generated pilot file, which contains 32 random bits (8 \(\times\) 2 \(\times\) 2 = 32) in total. The pilot data remain unchanged throughout the training, testing, and validation processes. Then, a random channel sample is selected from the channel samples. The signal after passing through the channel is a result of the data sequence and pilot sequence combined, containing 2048 floating point numbers.
## 2 Related Work
described a heuristic approximation algorithm for underwater data collection using an AUV as the mobile sink. They employed a probabilistic acoustic communication model which progressively decreases with distance. An AUV treated as the mobile sink was utilized to travel along the designed Traveling Salesman Problem-tour (TSP-tour) and stop at specific locations for data collection. Ultimately, a time-division multiple access (TDMA) protocolwas employed, which allows the AUV to collect data packets after identifying nodes within current probabilistic neighborhood. This scheme can efficiently save transmission energy costs, reduce data delay, and improve data collection performance of UASNs. However, the authors did not examine resolution for 3D UASNs. As summarized above, underwater data collection has been preliminarily studied, but most of the studies rely on ideal conditions or 2D UASNs. Therefore, there remains a significant need for additional research to address the mobile data collection problem for 3D UASNs.