esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization
###### Abstract  
We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness.  
Keywords:extreme learning machine; LC-ELM; particle swarm optimization; LC-PSO-ELM
## 2 System Description
### Precoding
\(\zeta\) for \(k\)th UE, \(\zeta_{k}\) is approximated as \(\zeta_{k,m\
eq}\approx\frac{1}{\sqrt{N_{t}K}}\) for MF precoding and \(\zeta_{k,ZF}\approx\sqrt{\frac{N_{t}-K}{K}}\) for ZF precoding [35]. Both ZF and RZF precodings show very similar performance, even in the relatively practical number of massive TX antennas (i.e., \(N_{t}\approx 10K\) ).
## 3 Building Load Type
### Classify Based on Building Type
#### 3.2.1 Industrial Buildings  
Industrial buildings, designed specifically for activities such as industrial production, warehousing, and transportation, encompass a wide range of types, including factories, warehouses, logistics centers, and more. These structures must not only meet the comfort and safety needs of indoor operators but also ensure the efficiency and safety of production processes. Given the diversity of industrial sectors, there are significant differences in the indoor environmental characteristics and industrial requirements for industrial buildings across various industries [80].  
The energy consumption of industrial buildings is primarily concentrated in building equipment systems, such as heating, ventilation, and air conditioning systems [81]. Notably, the energy usage of industrial buildings is closely related to weather conditions, while energy usage in production is directly linked to equipment productivity. To accurately predict these variations, it is common to combine features such as air enthalpy, solar radiation, and wind speed to predict weather dependency, while considering factors like job progress, productivity, the number of employees, and the occupied floor area to determine production dependency [82]. [PERSON] [83] reviewed 203 scientific papers and summarized the events related to industrial buildings. Figure 6 illustrates the relationship between industrial buildings and various events, where the size of the spheres indicates the frequency of occurrence and the distance between them represents the degree of correlation. It is evident from the figure that energy saving and consumption is an area of significant concern in industrial buildings.  
However, due to the complexity of manufacturing processes and industrial equipment, industrial buildings often generate a significant amount of energy loads that are unrelated to weather fluctuations or building activities. Therefore, accurately predicting the energy usage of industrial buildings is both a necessary task and a complex challenge.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 3 UWB Localization System Design
### MAC Design  
The MAC protocol uses a TDMA channel access method, which partitions time in repeating superframes. A superframe is a sequence of slots, where each slot corresponds to a specific action to take (e.g., receiving or transmitting a UWB message). This requires all participating devices to be synchronized. Since UWB radios are more energy-hungry than common narrowband IoT transceivers, a separate transceiver is used for all communication that is not related to ranging such as synchronization and reporting.  
(i) Synchronization is initiated by the drone during the first slot of the superframe where the mobile tag transmits a sub-GHz beacon message which is received by all nearby anchor nodes ('Beacon' slot in Figure 2). The main advantage of choosing the tag as beacon transmitter is that no multi-hop synchronization is required, because only the in-range anchors of the mobile tag need to be synchronized. Furthermore, the beacon contains a list of anchors with which the mobile tag wants to range, as well as their assigned ranging TDMA slot. All anchor nodes receiving the beacon know if and when they have to activate their UWB radio. In case the anchor node address is not included in the list, the anchor node can go to sleep mode until the next superframe.  
(ii) Next, a single UWB polling message is first broadcasted ('Poll' slot in Figure 2) to all nearby anchor nodes that turned on their UWB radio (The ranging scheme is discussed in Section 3.2.1.)  
Figure 1: High-level system diagram of the UWB localisation system with battery powered anchor nodes.  
(iii) Afterwards, UWB slots are provided for sequentially calculating the distance to all selected anchor nodes ('Ranging slot' in Figure 2).  
(iv) After each ranging slot, a reporting slot is provided to send the calculated distance to the drone and/or a back-end system. Border routers are continuously listening for reporting messages and forward them to a RTLS (Real-time Localization system) to calculate the current position. In contrast to beacon messages, which are used for local anchor node selection and TDMA synchronization, reporting messages should be transmitted over a long distance. As such, the sub-GHz radio is configured for long range communications (For example, the IEEE 802.15.4 CC1200 868 MHz receiver can transmit over a large distance (varying from 292 m to 1902 m depending on the bitrate)).  
The complete structure of the superframe and an overview of the transmitted messages are illustrated in Figure 2.
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 2 Basics of NIRS
### Measure of Brain Perfusion  
Finally, it worth mentioning that all the approaches described above rely on the fact that the changes in absorption are due to endogenous oxygen-dependent contrast (i.e., haemoglobin). This principle is at the heart of the NIRS technique as it enables us to follow the blood volume (i.e., via HbT), and StO\({}_{2}\), which reflects the balance between the oxygen delivery and the oxygen consumption. This implies that NIRS cannot directly measure the cerebral perfusion. However, this parameter can be assessed by using alternative methodologies and instruments.  
Indeed, the use of indocyanine green (ICG), a contrast enhancing agent, makes the assessment of cerebral perfusion with NIRS possible. ICG is a non-toxic light-absorbing dye that has a high absorption and fluorescence emission in the NIR [52]. By following the transit time of an ICG bolus injected intravenously, one can estimate the cerebral blood flow (CBF) [53]. Although, this transit time can be monitored in depth with DOT-CW-NIRS systems [52], it has been showed that when using only one channel, the use of TD-NIRS enhances the depth sensitivity of the ICG bolus tracking and enables us to distinguish between intra- and extra-cerebal layers. Moreover, the use of TD-NIRS gives also access to the measurement of the fluorescence signal of the ICG, which also increase the depth sensitivity [54]. The measured signal of fluorescence is often referred to as the distributions of times of arrival of fluorescence (DTAs). The main advantage of this technique is that it can provide a CBF value in standard physiological units (i.e., mL of blood/100 g/min) [55]. The main drawback of this technique is that it cannot continuously monitor the CBF as the injection of ICG is limited by a maximum recommended daily dose. We will then refer to this technique as DCE-TD-NIRS, where DCE stands for dynamic contrast enhanced.  
On the other hand, diffuse correlation spectroscopy (DCS) is a technique that enables continuous monitoring of a CBF index called blood flow index (BFI) without the injection of a contrast agent. Briefly, in DCS, the tissue of interest is illuminated by coherent near-infrared light, which causes a speckle interference pattern to form after the light scatters multiple times through the tissue. Thus, the changes in light scattering due to the moving red blood cells causes the speckle pattern to fluctuate rapidly. Those speckle patterns are then analysed to extract blood flow information. In order to quantify the fluctuation of the speckle pattern, the optical properties of the tissue need to be assumed or measured. Therefore, the use of TD-NIRS is well-suited to provide such information, especially since the two techniques are compatible and can be combined. A more detailed explanation of DCS principles can be found in [56].
## 2 Materials and Methods
### Analysis of the Flow Field near the Ozone Valley over the TP Influenced by EN
[30] conducted correlation calculations on the three elements of total radiative heating, divergence and vertical velocity of ozone in the UTLS layer and the atmosphere. Their results suggest that when the ozone concentration in the SAH region has a negative anomaly (positive anomaly), the radiative heating is a negative anomaly (positive anomaly) in the lower (upper) ozone valley, causing abnormal convergence (divergence) in the lower (upper) layer. Thus, the ascending lower layer exhibits positive anomalies. The upper-level divergence and the positive anomaly of upward movement finally lead to positive anomalies in the South Asian High. Conversely, the SAH also has an impact on the ozone valley [57]. When the SAH increases, the isotropic surface temperature of 120-50 hPa rises, corresponding to the atmospheric updraft. This additional updraft drives the low-level, low-concentration ozone to rise and reduce the ozone concentration in the region. Much discussion has been had surrounding how El Nino signals are transmitted to the SAH. [PERSON] et al. [58] analyzed the correlation between the SAH and SST in the tropical Pacific Ocean and the Indian Ocean, and the results showed that the SAH has a very close positive correlation with the SST in the Middle East Pacific Ocean and the Indian Ocean, whereas the correlation with the Western Pacific Ocean is very weak. Therefore, we propose the following possible mechanism: El Nino signals first affect the Walker circulation in the vertical direction and cause SST changes in the Indian Ocean through the transmission of the ocean-atmosphere interaction. The rise in the Indian Ocean SST strengthens the SAH through two-stage thermal adaptation and then decreases the ozone concentration near the SAH through the SAH-ozone interaction, especially in the southern region of the TP. From the above discussion, we find that in the summer of the following year after EN, the low-level updraft corresponds to the negative vorticity anomaly at a low latitude at 200 hPa, and the changes in the SAH will affect the ozone valley over the TP. In the following section, we will further verify this process through the simulation of WMCCM4.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 4 Results  
We deploy our design on the field-programmable gate array (FPGA) Virtex-7 and Virtex-6 devices. Two different designs are used in the experiments. We use Xilinx ISE 14.7 to implement our design on Virtex-6 device, and Vivado 2020.1 for Virtex-7 device. The first one is integrated with Ed25519 and ECDSA with NIST P-256. The second one is the combination of Ed25519 and ECDSA with NIST P-256, P-384, and P-521. The results and comparison are shown in Table 1. The input message and parameters are taken from [2] for the ECDSA mode and from [6] for the Ed25519 mode. It is clear that our design consumes significantly lower resources in comparison with other proposals. The four-mode design also uses fewer resources than the ECDSA P-256, ECDSA P-384, and ECDSA P-521 single-functional core of [11]. In addition, our design
## 5 Conclusions  
In summary, employing a microscopic model and [PERSON]'s function technique, we conducted a comprehensive examination of the magnetization, \(M\), specific heat \(C_{p}\), and polarization, \(P\), of CBCO, scrutinizing their dependencies on temperature, magnetic field strength, and doping effects. Notably, [PERSON] et al. [12] previously investigated the magnetic and electric properties of pure CBCO utilizing a modified Heisenberg model, while to the best of our knowledge, the influence of doping on the magnetic and electric properties of CBCO remains unexplored in existing literature. Our analysis reveals a distinct kink in the temperature-dependent specific heat curve near the critical temperature (\(T_{\rm C}\)), indicative of a phase transition. By applying strong magnetic fields, this peak disappears. Furthermore, the observed augmentation in polarization, \(P\), with increasing external magnetic field strength serves as compelling evidence for the multiferroic nature of CBCO. Additionally, the magnetization, \(M\), of CBCO displays a notable increase upon substitution  
Figure 8: Dependence of the polarization on the dopant concentration \(x\) of CBCO for \(T=60\) K for different doping ions: (1) Ni, (2) Mn, (3) Zn.  
of Co ions with Fe, whereas doping with Zn, Mn, or Ni ions induces a decline. Similarly, doping CBCO with Y or Sr ions at the Ca site elicits contrasting effects on magnetization, \(M\), with an increase in the former and a decrease in the latter case, attributable to disparate strains induced by the doping ions, thereby altering the exchange interaction constants. If magnetic Ni or nonmagnetic Zn atoms occupy the kagome layer Co sites in CBCO, then the polarization, \(P\), increases with the increasing Ni, Mn, or Zn dopant. We can conclude that Ni, Mn, or Zn doping enhances the magnetoelectric effect of CBCO. Thus, our study offers insight into the microscopic mechanisms underlying the macroscopic properties of CBCO and demonstrates qualitative agreement with experimental observations. The good agreement with many experimental data points is evidence that our model and [PERSON]'s function method are suitable for describing the various properties of pure and ion-doped CBCO.  
It should be mentioned that the multiferroic properties of CBCO nanoparticles, including co-doping (for example, with Fe and Zn, CaBaCo\({}_{4-x-y}\)Fe\({}_{x}\)Zn\({}_{y}\)O\({}_{7}\)[30]) will be studied in a future article. It is an interesting case because Fe atoms preferentially substitute the Co atoms at the triangular layer whereas Zn atoms occupy the kagome layer.  
Conceptualization, J.M.W.; Methodology, I.N.A. and A.T.A.; Software, I.N.A.; Formal analysis, A.T.A.; Investigation, I.N.A., A.T.A. and J.M.W.; Writing--original draft, J.M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Effect of HP Treatment on Microbial Growth of Sliced Cooked Turkey  
Similar results were obtained for vacuum packed sliced cooked turkey, HP treated in pack at 600 MPa. LAB were the pre-dominant spoilage microorganisms at all storage temperatures. HP cold pasteurization resulted in a more than 3-log reduction of initial  
Figure 4: Growth of _Pseudomonas_ spp. sliced cooked ham during storage at 0, 5, 10, and 15 \({}^{\circ}\)C, for control and HP treated samples at 600 MPa and 25 \({}^{\circ}\)C for 5 min.  
Figure 3: Growth of lactic acid bacteria of sliced cooked ham during storage at 0, 5, 10, and 15 \({}^{\circ}\)C, for control (**a**) and HP treated samples at 600 MPa and 25 \({}^{\circ}\)C for 5 min (**b**).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{6}{c}{**Growth Rate Constants (days\({}^{-1}\)) for Lactic Acid Bacteria (LAB)**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Storage** \\ **Temperature** \\ \end{tabular} } & \multicolumn{2}{c}{**Sliced Cooked Ham**} & \multicolumn{2}{c}{**Sliced Cooked Turkey**} & \multicolumn{2}{c}{**Cooked Ham Cubes**} \\ \cline{2-7}  & \multirow{2}{*}{**Control**} & **HP** & **HP** & **Control** & **HP** & **Control** & **HP** \\  & & **500 MPa** & **600 MPa** & & **600 MPa** & & **600 MPa** \\ \hline \(0\)\({}^{\circ}\)C & 0.109 \(\pm\) 0.007 & 0.096 \(\pm\) 0.011 & 0.024 \(\pm\) 0.006 & 0.188 \(\pm\) 0.031 & 0.025 \(\pm\) 0.004 & 0.260 \(\pm\) 0.012 & 0.083 \(\pm\) 0.009 \\ \hline \(5\)\({}^{\circ}\)C & 0.249 \(\pm\) 0.024 & 0.115 \(\pm\) 0.030 & 0.037 \(\pm\) 0.004 & 0.300 \(\pm\) 0.062 & 0.061 \(\pm\) 0.010 & 0.418 \(\pm\) 0.106 & 0.094 \(\pm\) 0.018 \\ \hline \(10\)\({}^{\circ}\)C & 0.509 \(\pm\) 0.107 & 0.347 \(\pm\) 0.027 & 0.154 \(\pm\) 0.020 & 0.390 \(\pm\) 0.071 & 0.150 \(\pm\) 0.026 & 0.536 \(\pm\) 0.097 & 0.158 \(\pm\) 0.031 \\ \hline \(15\)\({}^{\circ}\)C & 0.652 \(\pm\) 0.122 & 0.532 \(\pm\) 0.083 & 0.227 \(\pm\) 0.039 & 0.535 \(\pm\) 0.088 & 0.207 \(\pm\) 0.041 & 1.143 \(\pm\) 0.240 & 0.245 \(\pm\) 0.057 \\ \hline \multicolumn{6}{c}{\(\mathrm{E_{a}}\) (kJ/mol)} & 79.8 & 81.5 & 106 & 44.5 & 94.2 & 61.1 & 49.2 \\ \hline \end{tabular}
\end{table}
Table 3: Growth rate constants (days\({}^{-1}\)) for the lactic acid bacteria of meat products, stored at temperatures ranging from 0 to 15 \({}^{\circ}\)C, for each type of product. The activation energy values are also presented.  
LAB population (from 3.1 logCFU/gr of control samples to not detectable values for HP treated samples) and a significant lag phase (up to 25 days lag-phase with no microbial growth for sliced cooked ham, 10 days for sliced cooked turkey, and 15 days for diced ham cubes) at the lower storage temperatures. An up to eight-fold increase of control samples growth rate was estimated for the lower temperatures (0.188 days\({}^{-1}\) at 0 \({}^{\circ}\)C), compared to the corresponding values of HP treated samples (0.025 days\({}^{-1}\) at 0 \({}^{\circ}\)C) (Table 3).
# Design of Indoor Visible Light Communication PAM4 System
###### Abstract  
Four-level pulse amplitude modulation (PAM4) can transmit more information in the same symbol interval, effectively improving the information transmission rate and frequency band utilization of visible light communication (VLC). This paper proposes a method for encoding and decoding the PAM4 signal based on FPGA. The PAM4 signal is studied and analyzed under different channel noise models. The research results show that the bit error rate (BER) performance of the PAM4 signal is the best under the additive noise model. The influence of the multiplicative noise on the BER of the PAM4 signal is much greater than that of the additive noise. The BER performance of the PAM4 signal depends on the variance of the noise data. The greater the variance, the worse the BER performance. At the same time, an indoor VLC system based on PAM4 is designed. The simulation results verify that the system scheme is feasible and provides a way to further realize high-speed signal transmission.  
PAM4; visible light communication; FPGA 3
## 5 Experimental Evaluation
### Evaluation and Benchmarking of AQM Algorithms
#### 5.1.2 Pie  
In experiments (8)-(11), the internal parameters of PIE were configured with target = 15 ms, tupdate = 15 ms, alpha = 0.125, beta = 1.25, max-burst = 150 ms, and max-centh = 0.1.  
**Scenario 1 (Bandwidth = 10 Mbps, Delay = 20 ms)**: We evaluated the performance of PIE in two cases: (i) Case 1 is with enabled ECN; the results are shown in Figure 8, and (ii) Case 2: ECN is disabled, and the results are in Figure 9.  
The PIE algorithm drops packets instead of merely marking them when the drop-probability exceeds the configurable parameter'max_ccnth'. This behavior ensures that the  
Figure 5: Case 2: CoDel (ECN disabled)—Scenario 1 with Bandwidth = 10 Mbps, Delay = 20 ms. (a) Throughput; (b) Congestion Window; (c) Smoothed TCP RTT.  
Figure 6: Case 1: CoDel (ECN enabled)—Scenario 2 with Bandwidth = 1 Mbps, Delay = 20 ms. (a) Throughput; (b) Congestion Window; (c) Smoothed TCP RTT.  
Figure 7: Case 2: CoDel (ECN disabled)—Scenario 2 with Bandwidth = 1 Mbps, Delay = 20 ms. (a) Throughput; (b) Congestion Window; (c) Smoothed TCP RTT.  
congestion control optimally sets _cvond_ sizes leveraging its high variability characteristics in both cases whether ECN is enabled or disabled, as illustrated in Figures 8b and 9b.  
This behavior ensures consistent throughput and RTT values across multiple TCP connections, regardless of enabling ECN or not. It results in low RTTs and stable bandwidth sharing, as depicted in Figures 8c, 9c and 8a, 9a, respectively.  
**Scenario 2 (Bandwidth = 1 Mbps, Delay = 20 ms)**: We evaluated the performance of PIE in two cases: (i) Case 1 is with enabled ECN; the results are shown in Figure 10, and (ii) Case 2: ECN is disabled, and the results are in Figure 11. The PIE algorithm exhibits consistent behavior in low-bandwidth environments, in high-bandwidth environments characterized by low RTTs, high variations in _cvond_ in its search for optimal _cvond_ values, and optimal throughput, albeit only when ECN is disabled. Notably, with ECN enabled, significant unfairness across TCP connections is observed across all metrics, alongside high RTTs.  
Figure 8: Case 1: PIE (ECN enabled)—Scenario 1 with Bandwidth = 10 Mbps, Delay = 20 ms. (a) Throughput; (b) Congestion Window; (c) Smoothed TCP RTT.  
Figure 10: Case 1: PIE (ECN enabled)—Scenario 2 with Bandwidth = 1 Mbps, Delay = 20 ms. (a) Throughput; (b) Congestion Window; (c) Smoothed TCP RTT.  
Figure 9: Case 2: PIE (ECN disabled)—Scenario 1 with Bandwidth = 10 Mbps, Delay = 20 ms. (a) Throughput; (b) Congestion Window; (c) Smoothed TCP RTT.