esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Particle Swarm Optimization Variations to Solve the Transportation Problem Effectively
## 2 Transportation Problem (TP)  
Many researchers have developed various types of transportation models. The most prevalent was presented by [PERSON] in 1941 [PERSON] (1941). Similar studies were conducted later by [PERSON] in 1949 [PERSON] (1949) and in 1951 by [PERSON] (1951). It is well known that the problem has become quite widespread, so several extensions of transportation model and methods have been subsequently developed. However, how is the Transportation Problem defined?  
The TP can be described as a distribution problem, with m suppliers \(S_{i}\) (warehouses or factories) and \(n\) destinations \(D_{j}\) (customers or demand points). Each of the \(m\) suppliers can be allocated to any of the \(n\) destinations at a unit shopping cost \(c_{ij}\), which corresponds to the route from point \(i\) to point \(j\). The available quantities of each supplier \(S_{i}\), \(i=1\), \(2\),..., \(m\) are denoted as \(s_{i}\), and those of each destination \(D_{j}\), \(j=1\), \(2\),..., \(n\) are denoted as \(d_{j}\). The objective is to determine how to allocate the available amounts from the supply stations to the destination stations while simultaneously achieving the minimum transport cost and also satisfying demand and supply constraints [PERSON] (1941).  
The mathematical model of the TP can be formulated as follows:  
\[\text{min}Z=\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}\cdot x_{ij}; \tag{1}\]  
\[\sum_{i=1}^{m}x_{ij}\geq d_{j}\ for\ j=1,\ 2,\ \ldots,\ n; \tag{2}\]  
\[\sum_{j=1}^{n}x_{ij}\leq s_{i}\ for\ i=1,\ 2,\ \ldots,\ m; \tag{3}\]  
\[x_{ij}\geq 0\ for\ i=1,\ 2,\ \ldots,\ m,\ j=1,\ 2,\ \ldots,\ n. \tag{4}\]  
Equation (1) represents the objective function to be minimized. Equation (2) contains the supply constraints according to which the available number of origin points must be more than or equal to the quantity demanded from the destination points. Respectively, the sum of the amount to be transferred from source \(S_{i}\) to destination \(D_{j}\) must be less than or equal to the available quantity than we possess, as presented in Equation (3). A necessary condition is depicted in Equation (4), as units \(x_{ij}\) must take positive and integer values. Without loss of generality, we assume that in this paper, both the supplies and demands are equal following the balanced condition model.  
As already mentioned, there are several methods which can lead to finding a basic feasible solution. However, most of the currently used methods for solving transportation problems are considered complex and very expansive in terms of execution time. As a result, it is appealing to seek and discover a metaheuristic approach based on the PSO algorithm to solve the TP efficiently and effectively.
## 3 Results and Discussion
### Chemical Characterization of Biota and Partition Studies
#### 3.4.3 Ichthyoplankton  
Finally, the PAH and PCB occurrence was also determined for the fourth taxon, fish larvae (_ichthyoplankton_). Results revealed, regarding PAHs, only NaPh and AcPy were detected, with concentrations below quantitation limits, while PCB congeners were not detected.  
Comparing to previous taxa, _ichthyoplankton_ showed the lowest contamination; such behavior could be ascribed to fish larvae feeding habits. Indeed, _ichthyoplankton_ feeding is typically based on copepods larvae that are characterized by a short lifetime and, hence, a short exposure to pollution agents dissolved in water. The transfer of pollutants is, therefore, further hindered.  
Finally, it should be mentioned that PAH concentrations detected in the four target taxa did not exceed regulation limits (5 \(\upmu\)g/Kg of BaP).
Sensors_**2015**, _15_, 10074-10087; doi:10.3390/s150510074  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A New Indoor Positioning System Architecture Using GPS Signals**  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1,2}\)**  
\({}^{1}\) The Hong Kong Polytechnic University, Hong Kong, China; E-Mails: [EMAIL_ADDRESS] (W.C.); [EMAIL_ADDRESS] (Y.X.); [EMAIL_ADDRESS] (S.J.)  
\({}^{2}\) China University of Petroleum (Huadong), Qingdao 266000, China  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +852-6480-9857.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 5 March 2015 / Accepted: 23 April 2015 / Published: 29 April 2015_  
**Abstract:** The pseudolite system is a good alternative for indoor positioning systems due to its large coverage area and accurate positioning solution. However, for common Global Positioning System (GPS) receivers, the pseudolite system requires some modifications of the user terminals. To solve the problem, this paper proposes a new pseudolite-based indoor positioning system architecture. The main idea is to receive real-world GPS signals, repeat each satellite signal and transmit those using indoor transmitting antennas. The transmitted GPS-like signal can be processed (signal acquisition and tracking, navigation data decoding) by the general receiver and thus no hardware-level modification on the receiver is required. In addition, all Tx can be synchronized with each other since one single clock is used in Rx/Tx. The proposed system is simulated using a software GPS receiver. The simulation results show the indoor positioning system is able to provide high accurate horizontal positioning in both static and dynamic situations.  
**Keywords:** GNSS; indoor positioning; pseudolite
## 3 Results
### High-Frequency Physicochemical Characteristics of Inflow and Outflow
#### 3.3.3 Dissolved Oxygen  
The median overall concentration of DO at the inlet (9.1 mg L\({}^{-1}\)) and outlet (9.0 mg L\({}^{-1}\)) of the dry detention basin were alike (Table 1). During each of the storm events when the autonomous loggers were deployed, the median outflow concentration of DO was elevated relative to the inflow (Table 1). The median DO concentration at the outlet of the wetland (1.1 mg L\({}^{-1}\)) was significantly (\(p<0.001\)  
\begin{table}
\begin{tabular}{c c c c} \hline
**Location** & **pH** & **DO (mg L\({}^{-1}\))** & **ORP (mV)** \\ \hline Dry Basin Inlet & 6.6 (5.3, 7.0) & 9.1 (2.7, 15.7) & 99.6 (67.0, 261.7) \\ Dry Basin Outlet & 6.5 (5.2, 7.1) & 9.0 (0.1, 18.4) & 218.6 (145.0, 374.7) \\ Difference & 0.1 & 0.1 & 119 \\ Freq I\(>\) O & 60 & 0 & 0 \\ \hline Wetland Inlet & 6.6 (6.3, 7.2) & 5.2 (0.1, 10.8) & 613.3 (\(-\)81.4, 659.2) \\ Wetland Outlet & 5.7 (5.4, 6.9) & 1.1 (0.1, 11.5) & \(-\)80.0 (\(-\)978.3, 322.5) \\ Difference & 0.9 & 4.1 & 141.3 \\ Freq I\(>\) O & 100 & 85 & 37.5 \\ \hline Dry Basin Outlet & 6.5 & 9 & 218.6 \\ Wetland Outlet & 5.7 & 1.1 & \(-\)80 \\ Difference (B-W) & 0.8 & 7.9 & 298.6 \\ \hline \end{tabular}
\end{table}
Table 1: Median and range of physicochemical readings for the inlet and outlet of the dry detention basin and wetland. Frequencies that the inlet (I) values exceeded the outlet values are reported. Differences in outflow concentrations for the basin (B) in relation to the wetland (W) were also reported.  
Figure 10: Outlet stage response to a 0.6 cm rain event in December of 2018 for the dry detention basin (**a**,**b**). Outlet stage response to a 0.6 cm rain event for the wetland in December of 2019 (**c**,**d**). The slope of the stage decline for the basin (**b**) was 1 order of magnitude steeper than the wetland (**d**). Grey-shaded boxes denote range of inset graphs (**b**,**d**).  
lower relative to the inlet (5.2 mg L\({}^{-1}\)). During 85% of storms monitored, the inflow DO concentration exceeded the outflow (Table 1). Differences in the median concentration of DO between the inlet and the outlet of the dry detention basin were less than 0.2 mg L\({}^{-1}\), while there was a 4.1 mg L\({}^{-1}\) difference when comparing inlet and outlet for the wetland (Table 1). The outflow concentration of DO for the wetland was significantly lower (\(p<0.001\)) in comparison to outflow from the dry detention basin.
## 4 Experimental Procedure and Results
### Continuous Hydrogen Production Testing
This was due to the external wall heating method used in the reactor, which caused localized high temperatures near the wall, leading to a certain degree of water-gas shift reaction inside the reactor. Subsequent steps could involve employing pressure swing adsorption (PSA) to remove impurities from hydrogen. Alternatively, in future industrial applications, adopting a shell-and-tube heat exchanger reactor design, where high-temperature heat sources serve as the heat transfer medium, may prevent localized overheating and mitigate the water-gas reaction. The comparison between the research results of this article and those of previous studies is shown in Table 9 and Figure 15. From the chart in Figure 15, it can be seen that the hydrogen production rate obtained in this study is the highest globally, which has important guiding significance for the industrial application of thermochemical iodine-sulfur cycle water-splitting hydrogen production technology. In contrast to mainstream electrolytic water-splitting hydrogen production technologies, which suffer from high electrical energy consumption and low overall thermo-electric-hydrogen conversion efficiency (~30%), the I-S thermochemical hydrogen production technology enables direct heat-to-hydrogen conversion. This study achieved an overall thermal efficiency of 53%. Furthermore, the technology is compatible with nuclear energy, solar energy, and industrial waste heat, making it suitable for large-scale industrial green hydrogen production. However, challenges such as low HI decomposition rates and the selection of high-temperature corrosion-resistant materials currently limit its readiness for industrial adoption, keeping the technology in the pre-industrial stage. \begin{table}
\begin{tabular}{c c c c} \hline
**Institutions** & **Year** & **Running Time (h)** & **Hydrogen Production Volume (L/h)** \\ \hline GA [8] & 1981 & / & 1.2 \\ JAEA [12] & 1997 & 48 & 1 \\ JAEA [13] & 2004 & 172 & 32 \\ INERI [9] & 2005 & 18 & 10–75 \\ INET [21] & 2007 & 7 & 10 \\ INET [22] & 2013 & 80 & 60 \\ ENEA [11] & 2014 & 48 & 10 \\ JAEA [18] & 2020 & 150 & 30 \\ CEU [23] & 2021 & 4 & 80 \\ CEU (this study) & 2025 & 24 & 300–1536 \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison of I-S hydrogen production in various research institutions.
## 4 Experimental Results  
To evaluate the proposed instance segmentation scheme, YOLACT++ [33] was implemented in a PC with CPU i7-9700, 32 GB RAM, and NVIDIA Tesla T4. The backbone was ResNet-101, which had 100 convolutional layers and 1 fully connected layer. The output feature maps of the backbone were combined to yield multilevel visual feature maps in YOLACT++. The parameters of YOLACT++ are listed in Table 1. Since the input size of the proposed scheme was 550 \(\times\) 550 pixels, each input image was resized. Moreover, in line with our experiences, the weights in Equation (5) were predefined as follows: \(\beta_{1}\) = 1, \(\beta_{2}\) = 1.5, and \(\beta_{3}\) = 6.125. Here, the optimizer for network model learning was the stochastic gradient descent algorithm where the values of the learning rate and momentum were 0.0001 and 0.9, respectively. The parameter, IOU_THRESHOLD, was a threshold exploited in NMS.
## 4 Reliability Assessment Considering ME
### Deformation Reliability Incorporating ME  
Recall Equation (13), which gives the deformation reliability of retaining structures without considering the potential impact of ME. Taking into account the systematic error in the measurement of lateral displacement, which dominates in ME [51], we let \(\Delta_{i}\) denote the error associated with the \(i\)th (from the bottom to the top) measurement interval, which is a random variable representing the uncertainty of the measurement. Since the monitored data at different depths are obtained with the same inclinometer, and the ME accumulates with the number of measurement intervals, it is reasonable to assume that each \(\Delta_{i}\) is fully correlated and follows a normal distribution. With this, it follows that  
\[\Delta_{i}=k_{i}\epsilon,\quad i=1,2,3,\ldots n, \tag{19}\]  
where \(\epsilon\) is a random variable that reflects the accuracy of the inclinometer, and \(k_{i}\) is a scaler (\(i=1,2,\ldots n\)) that reflects the accumulation of the ME with the number of measurement intervals. With Equation (19),  
\[\sigma_{\Delta_{i}}=k_{i}\sigma_{\epsilon}, \tag{20}\]  
where \(\sigma_{\bullet}\) denotes the standard deviation of \(\bullet=\Delta_{i}\) and \(\epsilon\). Let \(A\) denote the accuracy of the inclinometer. With a confidence level of 95%, it follows that  
\[2\sigma_{\Delta_{i}}=Ad_{i}\Rightarrow\sigma_{\Delta_{i}}=\frac{Ad_{i}}{2}. \tag{21}\]  
Notice that the coefficient \"2\" in Equation (21) is explained by the 95% confidence level; that is, \(\Phi(2)-\Phi(-2)\approx 95\%\). Similarly, if the confidence level is 99%, then the coefficient \"2\" should be replaced by \(\Phi^{-1}(1-99\%)\approx 2.58\).  
Combing Equations (20) and (21) gives  
\[\frac{Ad_{i}}{2}=k_{i}\sigma_{\epsilon}\Rightarrow k_{i}=\frac{Ad_{i}}{2 \sigma_{\epsilon}}. \tag{22}\]  
Furthermore, in the presence of ME, the application of \"point control\" (c.f. Section 2 and Equation (3)) gives  
\[\begin{split}\mathbb{L}_{\text{PC}}(X)&=\mathbb{P} \left(y_{1}+\Delta_{1}\leq y_{\text{lim}}\cap y_{2}+\Delta_{2}\leq y_{\text{ lim}}\cap\ldots y_{n}+\Delta_{n}\leq y_{\text{lim}}\right)\\ &=\mathbb{P}\left(y_{1}+k_{1}\epsilon\leq y_{\text{lim}}\cap y_{ 2}+k_{2}\epsilon\leq y_{\text{lim}}\cap\ldots y_{n}+k_{n}\epsilon\leq y_{ \text{lim}}\right)\\ &=\mathbb{P}\left(\epsilon\leq\frac{y_{\text{lim}}-y_{1}}{k_{1}} \cap\epsilon\leq\frac{y_{\text{lim}}-y_{2}}{k_{2}}\cap\ldots\epsilon\leq \frac{y_{\text{lim}}-y_{n}}{k_{n}}\right)\\ &=\Phi\left(\frac{\min_{i=1}^{n}\frac{y_{\text{lim}}-y_{i}}{k_{i} }}{\sigma_{\epsilon}}\right)\xrightarrow[\text{Equation~{}\eqref{Notice that the proposed method (Equation (13) or (24)) only needs the computational costs of calculating a one-fold integral, which enables the deformation reliability assessment to be performed efficiently compared with the traditional finite element-based approaches. Thus, the proposed method is promising to be adopted for use in practical engineering.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
###### Abstract  
Recently, the development of the Internet of Things (IoT) applications has become more active with the emergence of low-power wide-area network (LPWAN), which has the advantages of low-power and long communication distance. Among the various LPWAN technologies, long-range wide-area network (LoRaWAN, or LoRa) is considered as the most mature technology. However, since LoRa performs uplink-oriented communication to increase energy efficiency, there is a restriction on the downlink function from the network server to the end devices. In this paper, we propose cooperative downlink listening to solve the fundamental problem of LoRa. In particular, the proposed scheme can be extended to various communication models such as groupcasting and geocasting by combining with the data-centric model. Experiments also show that the proposed technology not only significantly reduces network traffic compared to the LoRa standard, but also guarantees maximum energy efficiency of the LoRa.  
LPWAN; LoRaWAN; downlink; data-centric; energy efficiency Article
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 3 Sensing Connectivity and K-Node (Edge) Sensing Connectivity
### K-Node (Edge) Sensing Connectivity
#### 3.3.1 Flexible Sensor Deployment  
Sensor deployment must first ensure that each collector has \(k\) independent light paths to access sensing data. A lower number of repetitions per sensing data is better. We constructed a set of candidate sensors by greedily choosing the sensor with the minimum cost from available sensors. The cost of sensors can be evaluated via different strategies. When all users have at least \(k\) independent end-to-sensing light paths to candidate sensor sets, this candidate sensor set is the final result. The followings are three optional sensor deployment strategies:  
**Spectrum-Aware Sensor Deployment**: The sensor whose adjacent links have a large number of available spectrum resources is the preferred choice as a candidate sensor for sensor deployment.  
**Capacity-Aware Sensor Deployment**: The deployment area with a large remaining capacity is the preferred choice as a candidate sensor for deployment. In this situation, the capacity of the deployment area plays a more important role than the spectrum resource. The network operator should adopt this strategy when the current optical sensor network has enough spectrum resources compared with capacity resources in the deployment area.  
**Disaster-Aware Sensor Deployment**: In order to avoid a disaster event destroying all sensors where the sensing data is obtained, the sensor must be placed in a more reliable area. The sensor that is far from the area where the natural disasters occur frequently is the preferred choice as a candidate sensor. While researchers in climatology, geology, and environmental science have been studying how to predict disasters and assess disaster risks for certain regions, networking research can exploit this information to develop novel methods to prepare networks to handle disasters with the knowledge of risky regions, to better prepare them for a predicted disaster.
## 3 Results and Discussion
### Deviations of Positioning and Their Error Indicators from the Period \"FIX\"
The results from a previous study [17] and the static measurement results of other authors [48; 49] showed \(RMS_{err}\) values of approximately 0.01 m. Another source [24] reported dynamic \(RMS_{err}\) values between 3.4 mm and 3.8 mm during their dynamic measurements. However, the latter calculated the reported accuracy based on an unreliable trajectory from the reference system obtained through odometry by a field robot. It was assumed that the results of the error indicators measured by in this study influenced the dynamic scenarios serving as the basis of the study. After comparing the \(RMS_{err}\) values specified by the manufacturer for the \"FIX\" system status, it was impossible to demonstrate that the RTK receivers met the stated values. The dynamic measurements were believed to be the cause.
## 6 Security Solutions
### End Device Security
#### 6.1.3 Secure Storage  
IoT devices store cryptographic keys, sensor readings, and system images in their memories. The secure storage ensures protected access to this security-sensitive data. The on-chip memory can be designed using one-time programmable technology, such as read-only memory and poly-silicon fuses [114; 115; 116], to store codes and credentials securely. As a result, reprogramming attacks cannot modify or replace information stored in the secure storage.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Property** & \multicolumn{1}{c}{**Highlights**} \\ \hline Communication Overhead (CO) & The total number of messages (\(m_{t}\)) and bytes (\(b_{t}\)) exchanged until a session key is negotiated. In addition, the amount of energy (\(e_{co}\)) consumed by a radio transceiver for exchanging messages. \(CO=\sum(m_{t},b_{t},e_{co})\) \\ \hline Computation Complexity (CC) & The number of arithmetic operations (\(o_{t}\)), such as addition, subtraction, multiplication, division, and modular exponentiation, required to compute a session key. In addition, the amount of energy (\(e_{cc}\)) consumed by the CPU for performing cryptographic computations. \(CC=\sum(o_{t},e_{cc})\) \\ \hline Resilience & The ability to provide services under Denial-of-Service attacks \\ \hline Scalability & The ability to accommodate a large number of IoT devices \\ \hline Interoperability & The ability to negotiate a cipher suite to establish a secure association with heterogeneous devices \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._
## Abbreviations  
The following abbreviations are used in this manuscript:  
2 DOF Two degrees of freedom  
NN Neural networks  
EKF Extended Kalman filter  
SMC Sliding-mode controller  
RHONN Recurrent high-order neural network  
XSG Xilinx system generator  
HIL Hardware-in-loop  
KF Kalman filtering  
FPGA Field programmable gate array  
PCB Printed circuit board  
v7 Virtex 7 FPGA  
FF Flip-flop  
LUTS Look-up table  
IOBs Input/output block  
Mults/DSP48S Floating-point multiplier circuit  
TBUFs 3-state buffer  
BRAMs Block RAM  
Slices Basic building block components in the FPGA fabric
# Coverage, Rate, and Last Hop Selection in Multi-Hop Communications in Highway Scenarios
## 1 Introduction
### Contributions  
In this paper, to fill the above-mentioned gap, we first propose a mixed analytical-simulation framework to characterize the trade-off between multi-hop bridge length and achieved rate in highway deployment of mmWave/sub-THz BS. To this aim, we utilize empirical measurements of propagation at 300 GHz, realistic IEEE 802.15.3d modulation and coding schemes (MCS), and then apply the tools of stochastic geometry to form the bridge based on multiple propagation paths between communicating vehicles. We further proceed to propose the last hop selection strategy that allows for improving the capacity of the multi-hop bridges. We comprehensively compare its performance to alternative strategies.  
The main contributions of our study are:  
* In terms of optimal antenna locations: (i) bumper location is better in terms of bridge length; (ii) the bumper and engine locations coincide in terms of the data rate, and (iii) engine level location is characterized by the least number of hops thus requiring the least signaling overhead;
* Since the LoS propagation is dominant for all the considered last hop selection strategies contributing more than 90% of all the paths, for reasonable road traffic conditions, the lower the traffic density, the higher the bridge availability and linear increase in the number of hops leads to a linear increase in the bridge availability;
* The proposed last hop selection strategy leads to extreme performance gains in terms of data rate as compared to the \"first opportunity\" strategy reaching 100% for large inter-site distance (ISD) and 400-500% for small ISDs.  
The rest of the paper is organized as follows: In Section 2, we introduce our system model. In Section 3, we developed our numerical framework. Numerical results are elaborated in Section 4. Conclusions are drawn in the last section.
## 3 Results
### Application of TLC Fingerprint for Quality Control Extraction
#### 3.3.2 Effect of Wetting Time on Simplisia Fingerprint  
According to Figure 6, the multivariate analysis revealed a 29.01% similarity between the phytochemical profiles of the samples and the standardized extract. Extracts prepared by wetting time for 30 min, 3 h, and a standardized extract with an 82.16% similarity value were included in the first group. Extracts prepared for 24 h of wetting time with a 61.85% similarity value with a standardized extract were included in the second group.  
The extract prepared with wetting times of 30 min and 3 h belongs to the same group as the standardized extract, according to the PCA score plot (Figure 7a). The PCA loading plot (Figure 7b) and PLSA loading plot (Figure 7d) show that P10 and P13 contribute to this grouping. A wetting time of 3 h is optimal, yielding the highest similarity value (96.05%) and placing the standardized extract in a single cluster.  
Figure 5: Multivariate analysis of _Ipomoa bhattas_ L. leaf extract due to ethanol concentration variations: score plot PCA (**a**), loading plot PCA (**b**), score plot PLSDA (**c**), and loading plot PLSDA (**d**).