esa-sceva/satcom-chunk-collection huggingface dataset:
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
###### Abstract  
This paper presents a method that can be used to detect and identify _Salmonella_ spp. in fresh meat and vegetable samples using a photonic biosensor with specialized bioreceptors. Detection was based on photon transduction. Silicon-nitride-based resonant cavities were used to capture the change in light response when there is specific binding of the immobilized antibody to the sensor surface against the target antigen. A control immobilization experiment was conducted to validate the immobilization process on the biosensor surface prior to biofunctionalization for _Salmonella_ spp. detection. This experiment involved immobilization of pre-selected antibodies on silicon nitride surfaces. Two types of antibodies were suitable. The first was a specific polyclonal antibody with superior antigen-binding capacity across a wide range of concentrations. The second was a monoclonal antibody designed for effective binding at lower concentrations. Rigorous validation was performed. The outcomes were compared with those of the habitual method used to detect _Salmonella_ spp. (reference method). Replicates from different batches of contaminated meat and vegetable samples were analyzed. This comprehensive approach provides a methodologically robust, highly sensitive, and accurate way of rapidly detecting _Salmonella_ spp. in food samples. It has potential implications for improved food safety and quality control.  
Salmonella spp.; food safety; immunoassay; label-free method; pathogenic microorganisms; predictive microbiology 2023
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 6 Experiments and Discussion
### Results Using the Single Hand Features  
We conducted the experiments on two models, MLF LSTM and MLF LSTM with Conv1D and the Conv2D pyramid block, to analyze the influence of single hand features on the DHG dataset with 14 and 28 classes. The results are shown in Table 6.  
Motion features are better with the gestures focusing on global movement. However, when performing the complex gestures using from one finger (14 classes) to all fingers (28 classes), the motion features decrease significantly from 82.5% to 70.23%.  
The performance of the models using the depth shape feature only was reduced slightly from 92.26% and 90.71% down to 87.61% and 88.33%. Depth shape features also give the best accuracy of all the features, because they help the model recognize the local motion and also capture the changes of the depth values between two consecutive frames, enabling the model to learn the optical flow features; therefore, the model can recognize global motion.  
Upon comparison of the performance between the two models, MLF LSTM Conv1-2D gives better results when using joint point-cloud features with 85.11%/68.92% on 14 classes and 70.23%/56.07% on 28 classes. In contrast, MLF LSTM shows better results of 92.26% and 87.61% on 14 and 28 classes as compared to MLF LSTM Conv1-2D with 90.71% and 88.3%. The different between the two models is the training from scratch and the training from the pre-trained weight.  
Figure 13 shows the false cases using the depth shape on DHG 14 and 28. For the 14 classes, the gestures grab and pinch are greatly confused. From 14 to 28 classes, the confusion occurred between grab and pinch in both the one finger gesture and all-finger gesture. Rotation CW(1) and (2) are nearly confused the same between the one finger gesture and the all-finger gesture.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**14 Classes**} & \multicolumn{3}{c}{**28 Classes**} \\ \hline
**No.** & **Features** & **MLF LSTM** & \begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} & \
\begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} &
\begin{tabular}{c} **MLF LSTM** \\ **Conv1-2D** \\ \end{tabular} \\ \hline
1 & Motion & 80.23 & 82.5 & 72.85 & 70.23 \\
2 & Skeleton shape & 74.76 & 74.16 & 70.83 & 69.4 \\
3 & Joint point-cloud & 68.92 & 85.11 & 56.07 & 70.23 \\
4 & Points & 88.33 & 88.09 & 82.97 & 83.09 \\
5 & Depth shape & 92.26 & 90.71 & 87.61 & 88.33 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance results of the two models using the separate hand features.  
Figure 12: Results of hand component segmentation using the Seresnext backbone with the ground-truth depth (**a**), ground-truth labels (**b**), and Seresnext (**c**).
###### Abstract  
In this paper, we consider a coded cooperative communication network with multiple energy-harvesting (EH) relays. In order to adequately address the problem of error propagation due to the erroneous decoding at the relays, as in the case of conventional decode and forward (DF) relaying protocol, we propose coded cooperative schemes with hard information relaying (HIR) and soft information relaying (SIR) strategies. The performance of the relayed communication with EH relay depends crucially on the channel decoding capability at the relay, channel gains at the source-relay and relay-destination links, and ultimately on the power-splitting ratio of the relay EH receiver. The exact closed-form expression for the outage probability performance of the coded cooperative scheme with HIR strategy and relay selection (CC-HIR-RS) is derived for both cases, namely for constant and optimal power-splitting ratios. Concerning the coded cooperative scheme with SIR strategy, a Rayleigh Gaussian log likelihood ratio-based model is used to describe the soft estimated symbols at the output of the relay soft encoder. Directives are provided to determine the model parameters, and, accordingly, the signal-to-noise ratio (SNR) of the equivalent one-hop relaying channel is derived. A closed-form expression for the outage probability performance of the proposed coded cooperative scheme with SIR and relay selection (CC-SIR-RS) is derived. In addition, a fuzzy logic-based power-splitting scheme in EH relay applying SIR is proposed. The fading coefficients of the source-relay and relay-destination links and distance between source and relay node are considered as input parameters of the fuzzy logic system to obtain an appropriate power-splitting ratio that leads to a quasi-optimal SNR of the equivalent end-to-end channel. Monte Carlo simulations are presented to demonstrate the validity of the analytical results, and a comparison between the performance of the CC-HIR-RS scheme with constant and optimized power-splitting ratios and that of the CC-SIR-RS scheme with constant and fuzzy logic-based power-splitting ratios is provided.  
coded cooperation; error propagation; soft information relaying; energy harvesting; relay selection; fuzzy logic +
Footnote †: journal: Applied  
1
## 5 Conclusions  
In this paper, a tunable all-optical pattern recognition system based on NOLM is proposed for bit-flip BPSK targets. Benefiting from the differential encoding and signal reloading, the proposed system can achieve phase-locking-free architecture and recognize two bit-flip BPSK targets simultaneously in the input signal with tunable frequency. We evaluate the feasibility of the proposed system, and the simulation results demonstrate that our proposed system can correctly recognize and locate 4-bit and 8-bit bit-flip BPSK targets in the input data, operating at a transmission rate of 100 Gbps and frequencies of 192.8 THz and 193.4 THz. Similar output performance can be observed when evaluating input noise tolerance of the system based on ER, CR, Q factor, BER, AM, and SNR under the input frequencies of 192.8 THz and 193.4 THz. Our proposed system provides an effective method for pattern recognition and can help to accelerate the deployment of optoelectronic firewalls in optical networks. Additionally, the scalability of the proposed pattern recognition system to support the recognition of QPSK targets is discussed. In actual experiments and deployment, our proposed system may still require precise time synchronization among the input data, target, and initializing pulse to obtain correct outputs.  
Conceptualization, Y.T. and Z.K.; methodology, Y.T. and X.L.; software, Y.T. and Z.K.; validation, Z.K. and N.L.; formal analysis, X.L. and J.C.; investigation, Y.T., Z.K. and X.L.; resources, Y.T., X.L. and G.B.; data curation, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, X.L. and G.B.; visualization, N.L.; supervision, X.L.; project administration, Y.T.; funding acquisition, Y.T., X.L., J.C. and G.B. 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 (62171050, 62476212), Key Research and Development Projects of Shaanxi Province (2023-YBGY-021), Natural Science Basic Research Program of Shaanxi Province (2024)C-YBQN-0685), and the Open Fund of  
Figure 13: Simulation results when the average power of input data with frequencies of **(a)** 192.8 THz and **(b)** 193.4 THz varies from 0.1 mW to 0.9 mW.  
State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications) (IPOC2024B06).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that the data also form part of an ongoing study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 3 Opportunities of NR-U for Industry 4.0
### Security  
The 5G NR-U network is expected to have full end-to-end protections and must ensure that the information and resources of the industry are protected from attacks and threats. This includes three core concepts of security: confidentiality, integrity, and availability. The use of unlicensed private networks provides NR-U with the necessary network isolation, data protection, and device/user authentication to secure key properties. Moreover, NR-U offers classical subscriber identification module (SIM)-based security, as well as non-SIM-based security for its devices. There is significant deployment flexibility, allowing for local credential management or centralized/remote credential management using a roaming-based solution.
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 6 Conclusions  
In this paper, we showed the effect of the Doppler on IEEE 802.15.4g SUN OFDM PHY with a high mobility application scenario. From the trajectory of the Doppler frequency with the velocity between Tx and Rx, we proposed a novel method to enhance the performance of the MR-OFDM signal. Our method takes advantage of the CP part to estimate and compensate for the fractional CFO. The performance of this method shows its effectiveness for the mobility of the IEEE 802.15.4g MR-OFDM system.  
**Author Contributions:** All authors contributed to this paper: [PERSON] and [PERSON] proposed the idea and simulation methodology; [PERSON] optimized software, proposed and designed the proposed scheme, performed all experiments, and wrote paper; [PERSON] verified the experiment process and results, reviewed and edited the paper; and [PERSON] supervised the work and provided funding support.  
**Funding:** This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-0-01396) supervised by the IITP (Institute for Information & communications Technology Promotion).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 5 Numerical Results
### Average and Total Number of RR  
Figure 5 further illustrates the specific value of \(m_{N}\) considering different network configurations. As shown in the figure, for the given active sensor and collector intensities \(\lambda_{a}\) and \(\lambda_{b}\), respectively, assuming a maximum delay of 1 ms, the SISO system always requires the highest mean number of RRs.  
With the use of SIMO and MU-MIMO, multiantenna techniques achieve higher data rates through increased spectral efficiency, with a corresponding reduction in the required RRs. The multiple receive antennas system has the potential to enhance signal robustness and increase system capacity. In particular, the MU-MIMO system significantly reduces the mean number of RRs required in a typical cluster. Meanwhile, as collector intensity \(\lambda_{b}\) increases, the mean number of RRs required for various configurations decreases. When \(\lambda_{b}\) increases, the radius of the typical cell will decrease, while the number of active sensor nodes per unit area remains the same, which means that each collector needs to serve fewer nodes in the cell and thus the number of RRs required decreases.  
\begin{table}
\begin{tabular}{c c} \hline
**RR Per Node** & **SINR Range (dB)** \\ \hline
1 & [–20.6; +\(\infty\)] \\ \hline
2 & [–22.6; -20.6] \\ \hline
3 & [–23.6; -22.6] \\ \hline
4 & [–23.7; -23.6] \\ \hline
5 & [–23.9; -23.7] \\ \hline
6 & [–25.1; -23.9] \\ \hline \end{tabular}
\end{table}
Table 2: MCS table: SINR versus RR required to achieve R = 500 bps.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\lambda_{b}\) & **SISO** & **1 \(\times\) 8 SIMO** & **1 \(\times\) 8 SIMO** & **1 \(\times\) 8 MU-MIMO** & **1 \(\times\) 8 MU-MIMO** \\  & & **Selection** & **MRC** & \(\mathbf{n_{u}=2}\) & \(\mathbf{n_{u}=4}\) \\ \hline
0.1 & 0.0012 & 0.0120 & 0.0417 & 0.0157 & 0.0392 \\ \hline
0.3 & 0.0395 & 0.0435 & 0.0239 & 0.0144 & 0.0050 \\ \hline
0.5 & 0.0201 & 0.0041 & 0.0398 & 0.0208 & 0.0916 \\ \hline
0.7 & 0.0006 & 0.0080 & 0.0070 & 0.0206 & 0.2001 \\ \hline
0.9 & 0.0206 & 0.0593 & 0.0292 & 0.0757 & 0.2811 \\ \hline \end{tabular}
\end{table}
Table 3: The percentage change \(\Delta m_{N}/m_{N,s}\) of the theoretical values \(m_{N}\) and the empirical values \(m_{N,s}\).  
Figure 6 presents the total number of required RRs in a typical cell versus the collector intensity. It maintains the same trend as in Figure 5, with MU-MIMO being the best performing case, followed by SIMO and then SISO.
## 4 Precoding Schemes
### Precoding Schemes for VLC
#### 4.2.5 Blind Interference Alignment  
The lack of CSI at the transmitters usually means the use of orthogonal resource allocation schemes such as TDMA. In this context, a novel transmission scheme referred to as blind interference alignment (BIA) is proposed in [169] in order to achieve multiplexing gain without CSI at the transmitters. The main idea of BIA is based on exploiting the channel correlation among users, which are able to modify their radiation pattern based on the concept of reconfigurable antenna, during a period of time in which the physical channel does not vary. In [29], the concept of a reconfigurable photodetector is proposed in order to implement BIA for VLC systems. Basically, a reconfigurable photodetector consists of an ADR where the PDs are connected to a single signal processing chain and provide a linearly independent channel response each due to the angle diversity. Interestingly, it is shown that beyond the lack of need for CSI at the transmitters, BIA provides satisfactory data rates avoiding data sharing among optical APs, only requiring synchronization among them, therefore reducing the complexity of the backhaul. However, the received signal for BIA suffers a noise enhancement that is proportional to the number of users, and it requires a coherence period that is exponentially proportional with base the number of optical APs and exponent equal to the number of users. As a consequence, BIA results in being more useful for small VLC networks. As discussed in the following section, this issue makes the use of clustering strategies mandatory when applying BIA in large VLC networks.
# Optimal Model-Free Mean-Square Consensus for Multi-Agents with Markov Switching Topology
## 4 Neural Network Methods  
This part uses actor-critic NNs to execute the algorithm. To create actors and critics, NNs are utilized.  
The value function (27) is approximated using the critic network, i.e.  
\[\hat{V}_{i}^{s}(k)=\Psi^{T}(e_{i}(k),u_{i}(k))\hat{W}_{c,i}^{s}(k), \tag{55}\]  
where \(\Psi(\cdot)\) is the activation function and \(\hat{W}_{c,i}^{s}(\cdot)\) is the weight matrix. In view of the approximation error, the error function of critic networks can be formulated as  
\[\varepsilon_{V_{i}}^{s}(k)=\hat{V}_{i}^{s}(k)-\gamma\hat{V}_{i}^{s}(k+1)-r_{E }^{s}(e_{i}(k)). \tag{56}\]  
To reduce the error performance index \(E_{c,i}^{s}=\frac{1}{2}(\varepsilon_{V_{i}}^{s})^{T}\varepsilon_{V_{i}}^{s}\) as much as possible, we update the weights using gradient descent as follows:  
\[\hat{W}_{c,i}^{s+1}=\hat{W}_{c,i}^{s}-\beta_{c}\frac{\partial E_{c,i}^{s}}{ \partial\varepsilon_{V_{i}}^{s}}\cdot\frac{\partial\varepsilon_{V_{i}}^{s}}{ \partial\hat{V}_{i}^{s}}\cdot\frac{\partial\hat{V}_{i}^{s}}{\partial\hat{W}_{ c,i}^{s}}. \tag{57}\]  
The actor networks designed to approximate control policies are subsequently proposed; that is,  
\[\hat{u}_{i}^{s}(k)=\Phi^{T}(e_{i}(k))\hat{W}_{a,i}^{s}(k), \tag{58}\]  
where \(\Phi(\cdot)\) is the activation function and \(\hat{W}_{a,i}^{s}(\cdot)\) is the weight matrix. Similarly, the error performance indices of actor networks for both algorithms are defined as  
\[\varepsilon_{u}^{s}(k)=\hat{V}_{i}^{s}(k)-\hat{V}_{i}, \tag{59}\]  
where the ultimate utility objective is denoted by \(\hat{V}_{i}\), and its value is set to 0. To minimize \(E_{a,i}^{s}=\frac{1}{2}(\varepsilon_{u}^{s}(k))^{T}\varepsilon_{u}^{s}(k)\), we adjust the weights of actor networks as  
\[\hat{W}_{a,i}^{s+1}=\hat{W}_{a,i}^{s}-\beta_{a}\frac{\partial E_{a,i}^{s}}{ \partial\varepsilon_{u}^{s}}\cdot\frac{\partial\varepsilon_{u}^{s}}{\partial \hat{u}_{i}^{s}}\cdot\frac{\partial\hat{u}_{i}^{s}}{\partial\hat{W}_{a,i}^{s}}. \tag{60}\]  
Figure 1 illustrates the algorithmic process using actor-critic NNs. Initially, the actor network receives the current state information \(e_{i}(k)\) and outputs the control signal \(\hat{u}_{i}(k)\)  
These control signals are then transmitted to the real system, influencing its state changes. The critic network simultaneously receives the control input and state information and it also produces a value function \(\hat{V}_{i}(k)\) that is used to modify the actor network's parameters. The critic network then calculates the difference between the value function from the previous time step \(\hat{V}_{i}(k-1)\) and the current value function \(\hat{V}_{i}(k)\). Compare this difference to the actual reward \(r_{E}(e_{i}(k))\), and then adjust the parameters of the critic network. The entire process forms a closed-loop control system, which iteratively refines the control strategy through continuous learning, gradually converging to the optimal solution.
## 5 Results and Discussion
### Voltage Regulation Performance Analysis Under Best PMVO Solution  
Figures 8 and 9 present the hourly voltage regulation profiles for a 33-node distribution system optimized using the PMVO algorithm under the two operational modes. Each curve corresponds to the optimal voltage regulation profile obtained when minimizing one of the two evaluated objective functions: energy losses and CO2 emissions, respectively. Although the vertical axis represents voltage regulation (in %), these profiles illustrate the system's voltage behavior resulting from two distinct optimization goals. In both cases, the voltage deviations remain consistently below the critical 10% limit, confirming the algorithm's effectiveness in maintaining power quality regardless of the selected objective.  
joint benefits for both objectives. A slight rise is observed from hour 17 to hour 20, where voltage regulation approaches its highest values (a peak at 7.84% for energy losses and 7.70% for emissions), likely due to increased demand during night peak hours. However, the algorithm effectively constrains the rise within the permissible operational margins.  
In contrast, the self-powered scenario (Figure 9) represents a more restrictive condition where the system operates without grid support. Despite this limitation, the PMVO algorithm maintains robust voltage regulation, ranging from approximately 5.4% to 7.74%, exhibiting only a marginal deviation from the grid-dependent case. As in the previous mode, hours 17-20 represent the most demanding interval, but the algorithm ensures compliance with the voltage regulation standard. The slight differences observed (particularly the slightly lower performance in the early hours) reflect the added constraint of managing local resources without external reinforcement.  
Comparing both operational modes reveals that the PMVO algorithm maintains a consistent and technically sound voltage regulation profile, with no hour surpassing the 10% regulatory threshold in either case. From a practical perspective, this analysis suggests that PMVO offers a reliable solution for voltage regulation across diverse microgrid configurations.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 2 Basics of BS Information and LTE Performance Metrics
### LTE Performance Metrics  
After identifying a cell, the user terminal calculates performance metrics to facilitate communications. A few signal strength and quality parameters for an LTE network, as calculated in a mobile terminal, are briefly illustrated below. These metrics will be used in this work to investigate the impact of rainfall on LTE signal quality.  
1. RSRP indicates the power level of the LTE reference signals. It is generally calculated by averaging the power of reference signals in a downlink frame [27]. In a frequency-division duplexing LTE system, one downlink frame consists of ten subframes, and each subframe contains two time slots with seven symbols. The cell-specific reference signals are placed in symbols indexed at 0, 4, 7, and 11 in each subframe. These reference signals are distributed across subcarriers, with every sixth subcarrier carrying a reference signal. The placement of these signals depends on the physical cell ID.
2. RSSI measures the total average signal power, including both reference symbols and other co-channel interferences. Thus, RSSI is the sum of RSRP and any additional interference present in the channel. While RSRP focuses on the reference signals, RSSI provides a broader measure of the overall received signal strength.
3. RSRQ reflects the quality of the reference signals. It jointly considers RSRP, RSSI, and the number of resource blocks (N) over the same bandwidth for measuring RSRP and RSSI. The relationship between these parameters is given by [27] \[\begin{split}&\text{RSRQ}=(\text{N}\times\text{RSRP})/\text{ RSSI}\\ &\text{RSRQ}(\text{dB})=\text{RSRP}(\text{dBm})-\text{RSSI}(\text{dBm })+10\times\text{log}_{10}\text{N}\end{split}\] (1) Specifically, RSRP and RSRQ are calculated by LTE user terminals and reported to the BS, acting as the criteria for the cell selection and handover algorithm [28].
4. SNR is a metric used to indicate the quality of the received signal. It is defined as the linear average over the power contribution of the resource elements carryingcell-specific reference signals divided by the noise power. The noise power could be estimated by the sum of the average linear power values of signals transmitted by non-target BS over the OFDM symbols carrying cell-specific reference signals [29]. A higher SNR indicates better signal quality, while a lower SNR suggests greater interference or attenuation.  
In this work, these LTE performance metrics, including RSRP, RSSI, RSRQ, and SNR, will be analyzed to determine how rainfall affects signal quality and whether they can be used as reliable indicators for rainfall sensing.