esa-sceva/satcom-chunk-collection huggingface dataset:
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 3 Theoretical Model
### Phase Error Model : Multipath, Phase Standard Deviation and Radiation Pattern
#### 3.3.1 Multipath Propagation Model  
Multipath interference is a major challenge in RFID-localization and several solutions have been proposed to estimate, reduce or mitigate its effect on measurements [53; 54]. To start investigating the multipath, we use a simple two-ray model, assuming that the measured signal is a superposition of the line-of-sight (\(p=1\)) signal and a signal reflected on the ground (\(p=2\)), as shown in Figure 3. The two signals propagate over different path lengths \(r_{p}\) and orientations, which translate in different received power values due to Friis' law:  
\[P_{p}(r)=\big{(}\frac{\lambda}{4\pi r_{p}}\big{)}^{2}\times P_{t}\cdot G_{T}(i _{p})\cdot G_{R}(i_{p})\;\;for\;\;p=\{1,2\}\]  
where \(P_{t}\) is the power transmitted by the antenna, \(P_{p}\) is the received power along path \(p\), \(G_{r}\) and \(G_{t}\) are the receiver and transmitter gain which depend on the signal orientation angle \(i_{p}\) and the antenna radiation pattern, \(\lambda\) is the carrier wavelength and \(r_{p}\) is the path propagation distance. We can then define the amplitude gain \(A_{p}(i_{p},r_{p})\) for the line-of-sight (1) and reflected (2) signals :  
\[A_{1}(i_{1},r_{1})=\frac{1}{r_{1}}\sqrt{G_{t}(i_{1})\cdot G_{r}(i_{1})}\]  
\[A_{2}(i_{2},r_{2})=\frac{1}{r_{2}}R(i_{2})\sqrt{G_{t}(i_{2})\cdot G_{r}(i_{2})}\]  
where \(R(i_{2})\) is the reflection coefficient impacting the reflected ray (which depends on ground relative permittivity). The received signal voltage \(s_{p}\) after normalization by the initial emitted voltage can be expressed by the following phasor:  
\[s_{p}(i_{p},r_{p})=A_{p}(i_{p},r_{p})\cdot\frac{\lambda}{4\pi}\cdot e^{-jkr_{ p}}\;\;for\;\;p=\{1,2\} \tag{11}\]  
where \(k\) is the wave number. The resulting signal \(s_{tot}\) arriving on the tag is the sum of the two phasors:  
\[s_{tot}=s_{1}(i_{1},r_{1})+s_{2}(i_{2},r_{2})\]  
After accounting for tag modulation efficiency \(L_{t}\)[55], and due to the reciprocity of all gain values during the back-scattered propagation, the full signal phasor received by the station antenna is finally expressed as follows:  
\[s_{full}=s_{tot}^{2}\cdot L_{t}\]  
As a reminder, the squared \(s_{tot}\) corresponds to the back-and-forth path of the signal.
## 3 Results
### Embedded Toxicity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Chemical Name**}} & \multicolumn{4}{c}{**Enzaldehyde**} & \multicolumn{1}{c}{**Benzene**} & \multicolumn{1}{c}{**Hexane**} & \multicolumn{1}{c}{**Naphthalene**} & \multicolumn{1}{c}{**Pyridine**} \\ \hline CAS No & & & 100-52-7 & 71-43-2 & 110-54-3 & 91-20-3 & 110-86-1 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} ILCD \({}^{\text{A}}\)/USEtox \({}^{\text{B}}\) \\ (CTUh per kg material) \\ \end{tabular} } & \multirow{3}{*}{LPD} & C & \(2.21\times 10^{-9\,\text{A}}\) & \(1.20\times 10^{-7\,\text{A}}\) & \(1.93\times 10^{-10\,\text{A}}\) & \(6.35\times 10^{-7\,\text{A}}\) & \(5.40\times 10^{-7\,\text{B}}\) \\ \cline{2-7}  & \multirow{3}{*}{HPD} & NC & \(2.87\times 10^{-9\,\text{A}}\) & \(3.04\times 10^{-8\,\text{A}}\) & \(2.62\times 10^{-8\,\text{A}}\) & \(8.74\times 10^{-7\,\text{A}}\) & \(1.64\times 10^{-6\,\text{B}}\) \\ \cline{2-7}  & \multirow{3}{*}{HPD} & C & \(3.39\times 10^{-8\,\text{A}}\) & \(4.74\times 10^{-7\,\text{A}}\) & \(1.79\times 10^{-9\,\text{A}}\) & \(1.22\times 10^{-6\,\text{A}}\) & \(2.84\times 10^{-6\,\text{B}}\) \\ \cline{2-7}  & \multirow{3}{*}{HPD} & NC & \(4.40\times 10^{-8\,\text{A}}\) & \(1.20\times 10^{-7\,\text{A}}\) & \(2.44\times 10^{-7\,\text{A}}\) & \(1.68\times 10^{-6\,\text{A}}\) & \(8.62\times 10^{-6\,\text{B}}\) \\ \cline{2-7}  & \multirow{3}{*}{LPD} & C & \(2.54\times 10^{-8\,\text{A}}\) & \(1.38\times 10^{-6\,\text{A}}\) & \(2.22\times 10^{-9\,\text{A}}\) & \(7.30\times 10^{-6\,\text{A}}\) & \(6.21\times 10^{-6\,\text{B}}\) \\ \cline{2-7}  & \multirow{3}{*}{LPD} & NC & \(7.75\times 10^{-9\,\text{A}}\) & \(8.21\times 10^{-8\,\text{A}}\) & \(7.07\times 10^{-8\,\text{A}}\) & \(2.36\times 10^{-6\,\text{A}}\) & \(4.43\times 10^{-6\,\text{B}}\) \\ \cline{2-7}  & \multirow{3}{*}{HPD} & C & \(3.90\times 10^{-7\,\text{A}}\) & \(5.45\times 10^{-6\,\text{A}}\) & \(2.06\times 10^{-8\,\text{A}}\) & \(1.40\times 10^{-5\,\text{A}}\) & \(3.26\times 10^{-5\,\text{B}}\) \\ \cline{2-7}  & & NC & \(1.19\times 10^{-7\,\text{A}}\) & \(3.24\times^{-7\,\text{A}}\) & \(6.59\times 10^{-7\,\text{A}}\) & \(4.55\times 10^{-6\,\text{A}}\) & \(2.33\times 10\text{-5\,\text{B}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characterization factors for benzaldehyde, benzene, hexane, naphthalene, and pyridine were given in CTUh and converted to DALY using weighting factors 11.5 and 2.7 for carcinogenic and non-carcinogenic effects, respectively. LPD = Low population density, HPD = High population density, C = Carcinogenic effects and NC = Non-carcinogenic effects. The reference database for the characterization factors is marked with A = ILCD database [28] and B = USEtox database [30] only.
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 3 Results and Discussion
### Increase in the NoN  
The primary role of the HRAN routing protocol is to handle networks with hundreds of nodes, hence this is the focus of the first performance test. The second evaluation looks at the forwarding protocol's message throughput as the network NoN grows. Figure 9a shows that the DSR and the HRAN protocol use less control packets than the other protocols for networks with 150 nodes. For the smaller network of 150 nodes, the DSR has a little lower value than the HRAN and the ZRP protocol, respectively.  
Figure 8: Protocols performance with increasing source nodes. (**a**) Number of control messages. (**b**) Ratio of delivered messages to control messages.  
As shown in Figure (b)b, proactive approaches (OLSR and fisheye) never experience delays when requesting new routes since they always have existing routes in place. For large networks, the HRAN has lower values compared to the other routing protocols since it makes use of heat structures to direct searches for new routes.
## 3 Result and Discussion  
### NP Concentrations of the Source and Treated Water Samples  
As shown in Figure 1, water samples were collected at various locations covering the whole area of Taiwan, including Ban-shin (A), Bao-shan (B), Bei-dou (C), Carp Lake (D), Chan-shin (E), Chen-chin Lake (F), Dong-shin (G), Feng-shan (H), Feng-yuan (I), Kao-tan (J), and Kin-men (K), Nan-hua (L) and Shin-shan (M). All of them are important source waters of Taiwan, so each of them is withdrawn to a nearby WTP. Raw water and produced water samples were collected 2 to 19 times in 2009-2015.  
The NP concentration of each source water is provided in Supporting Information (Table S2). Different unit processes, namely, rapid filtration, pre-chlorination, coagulation/sedimentation, pre-ozonation, post-chlorination, granular activated carbon, ultrafiltration, and low-pressure reverse osmosis, are employed in combination at each of the WTPs (Table S2).  
The result shows that NP concentration was in the range from below detection limit to several hundred ng/L for most of the treatment plants (Figure 1). The maximum NP concentration of 986 ng/L was found at the source water of the Site-M, which consists of conventional processes such as pre-chlorination, coagulation/sedimentation, rapid filtration, and post-chlorination. While the range of NP concentrations of source water was found from 46.1 to 986 ng/L, that of produced water (after conventional treatments) was from below detection limit to 117 ng/L, respectively (as shown in Table S2). It was observed that the NP concentration was not varying significantly at the two sites, i.e., Sites-F and -E. On the other hand, the water samples collected from the Sites-I and -L did not  
Figure 1: (**a**) Sampling Locations and (**b**) NP concentration of source waters. The highest, second highest, and the third highest NP concentrations were detected at (M) Shin-shan, (F) Chen-chin Lake, and (C) Bei-dou, respectively.  
show NP; the concentration was below the detection limit. The high NP contents of the raw water samples collected from Sites-M, -B, -E and -H were attributed to discharge from municipal WWTPs and industrial parks [42].
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 4 Security and Privacy in D2D Communications
The authors also identify the countermeasures in the form of cryptography methods, intrusion detection  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref. ** & **Problem** & **Algorithm** & **Attack Addressed** & **Findings** & **Limitations** \\ \hline \multirow{4}{*}{[51]} & Lack of trusted devices to execute secure aggregation without a base station. & Security protection mechanism of private data based on homomorphic encryption. & Improves the security and optimizes the resources allocation in D2D network. & Further studies on election factors and the impact of the resources allocations in D2D networks. & Improves the security, efficiency, and effectiveness of 5G D2D communication. & For the network and the fast-vertical handover, the fairness of the resources allocation in D2D networks. methods, and human factors. The authors investigate six different types of privacy-preserving and authentication schemes and proposed six open directions on how to secure communication in 4G and 5G cellular networks in the future, which focus on the privacy-preserving and authentication schemes. [PERSON] et al. in [PERSON] et al. (2019) considered battery-powered wireless devices to designed a secured lightweight D2D system using multiple sensors in the wireless devices. For securing the D2D communications by leveraging the sensors in the devices, the authors proposed a lightweight and efficient key distribution scheme. The experiments show that the communications with low energy consumption and computing resources can be achieved using the proposed scheme. [PERSON] et al. [PERSON] et al. (2019) study the problem of group information exposure when the devices in proximity identify other devices in the same group by their group identity. The exposure will lead the eavesdroppers to analyze the usage of the applications. From the study, the authors proposed the network-covered and the network-absent protocol using the authenticated key exchange for D2D communications. The aim is to guarantee end-to-end security to network operators, accountable group anonymity, revocability, and traceability for management and accounting requirements. The performance analysis shows that the cost of both communication protocols is low even after providing the new security properties. [PERSON] et al., in [PERSON] et al. (2019), proposed a new data sharing framework using the blockchain technology named the D2D blockchain. The proposed framework works by deploying Access Points (APs) that will verify the transactions of end users. The authors proceed with designing a two-stage contract theory-based joint optimization scheme for the transaction relaying phase and the block verification phase. The testing result shows that the proposed framework can improve privacy and security protection in D2D data sharing. The result also indicated that the proposed scheme could improve the local AP utility while reducing the delay under information asymmetry.
###### Abstract  
A Finite-State Machine (FSM) model is frequently employed to represent the behavior of sequential circuits. In the optimal design of these circuits, it is crucial to enhance FSM characteristics such as area (implementation cost), performance (operating frequency), and power consumption. This paper proposes sequential state encoding methods that aim to reduce the area and enhance the performance of FSMs. The methods involve sequentially selecting FSM states for encoding and determining the most appropriate code for each selected state. Several state and code selection modes are introduced, allowing for consideration of the relationships between states, the number of incoming and outgoing transitions, and the number of input variables initiating transitions to each state. The code selection process takes into account the architectural features of the electronic device in which the FSM is implemented, while some code selection modes are introduced to optimize both the area and performance of the FSM. The experimental results demonstrate that the proposed approach yields, on average, a reduction in the FSM area by 19.7% (in some instances, up to twofold reduction), along with an average performance increase of 21.2% (in certain cases, up to 69.3%), compared to the Sequential mode of the Quartus system.  
finite-state machine (FSM); state encoding; sequential encoding method; field-programmable gate array (FPGA); area; performance
## 5 Analysis of the Influence of the Control Parameter and Improvement Methods  
### Analysis of the Influence of the Control Parameter  
Figure 13 illustrates the influence of the PLL bandwidth on Re[Yinv(j\(\omega\))], the abscissa shows the frequency, the ordinate shows the PLL bandwidth and different colors represent Re[Yinv(j\(\omega\))]. The parameters in Table 1 are used to generate Figure 13.  
In Figure 13, the abscissa represents the frequency of Re[Yinv(j\(\omega\))], the ordinate represents the PLL bandwidth and the color in the figure represents the amplitude of the Re[Yinv(j\(\omega\))], with warm colors representing larger amplitudes and cool colors representing smaller amplitudes. Dark blue represents the negative amplitude of the Re[Yinv(j\(\omega\))]. The blue vertical dotted line in Figure 13a and red vertical dotted line in Figure 13b represent the 1700 Hz line.  
As shown in Figure 13a, the increase of the PLL bandwidth has no effect on the Re[Yinv(j\(\omega\))] of the dq-based control method. With the increasing PLL bandwidth, the negative area of the Re[Yinv(j\(\omega\))] of the PR-based control method expands at a low frequency (Figure 13b). The positive area shrinks even though the amplitude increases. In general, the stability frequency area of the system becomes smaller.  
In order to specifically analyze the influence of the control parameters on a certain frequency, 1700 Hz is selected as the target frequency for analysis in this article. The increased PLL bandwidth (>2000 Hz) will cause the Re[Yinv(j\(\omega\))] of the PR-based control method to enter a stable area at 1730 Hz (Figure 13b). Thus, when the resonant frequency of the grid impedance is around 1700 Hz, the inverter-grid system can still be stable.  
In Figure 14, the abscissa represents the frequency of Re[Yinv(j\(\omega\))], the ordinate represents the PI bandwidth for the current control loop of the dq-based control method (Figure 14a), and the proportion coefficient KP-PR of the PR-based control method (Figure 14b). The red vertical dotted line in Figure 14a and the blue vertical dotted line in Figure 14b represents the 1700 Hz line.  
Figure 13: The influence from the PLL bandwidth to Re[Yinv(j\(\omega\))], full band. (a) dq-based control method; (b) PR-based control method.
# SD-BROV: An Enhanced BGP Hijacking Protection with Route Validation in Software-Defined eXchange
## 2 Background and Related Work
### Limitation and Motivation to Improve Current BGP Route Validation  
Despite the importance of securing Internet and its extensive efforts by all the parties, RPKI deployment is still in a slow progress. To implement RPKI, AS has to issue ROAs covering their address blocks first. At the same time, AS should also configure BGP routers to do Route Origin Validation (ROV), discarding BGP advertisements with invalid ROAs. For enforcing such progress, every AS needs to enable ROV on its local routers. While some legacy routers may meet performance issues, and that is the reason that currently only a small part of ISPs with new equipment is able to enforce the ROV. On the other hand, this situation may cause compatible problem-the inconsistency of ROA makes non-registered IP prefixes to be marked as \"invalid\" and turns to be filtered.  
As described above, to enable ROV for better security, it is important to simplify the validation process for speeding up the deployment. Hence, this research proposes an idea for improving ROV mechanism by leveraging SDN to IXP (i.e., SDX) to make further engagement. The used techniques and expected contributions are listed below:  
Figure 1: An example of ROA record.  
Implementing BGP ROV by validating each advertised route from participating routers in IXP through SDN controller in SDX.
* Deploying overlay SDX fabric that crosses multiple geographical locations to verify the proposed idea over large-scale and long-distance environment.
* Evaluating the development to make sure it is capable of detecting and preventing BGP hijacking event for peering members in the SDX.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 3 Materials and Methods
### Deep Learning Algorithm  
Deep learning approaches attempt to model high-level abstractions in data by using a deep graph with multiple processing layers [90]. It can be used for applications pattern analysis (unsupervised) and classification (supervised) with hierarchical feature extraction.  
Common deep learning models [91] include convolutional deep neural networks (DNN), deep belief networks (DBN), Auto encoder (AE), convolutional neural network (CNN), etc. They are have been applied to computer vision, automatic speech recognition, natural language processing, and bioinformatics with a rather good results. DBN proposed by [PERSON] in 2006 is a directed acyclic graph with multiple layers which is composed of binary or real-valued latent variables.  
DBN is a Bayes probabilistic, generative model made up of multiple layers of hidden units. The advantages of DBN are that it is able to learn non-labeled data, train effectively, and overcome the over-fitting.  
Figure 4: BP-ANNs model.  
The basic component in DBN is restricted Boltzmann machines (RBM) which is an undirected, generative energy-based model with a layer of visible units as input layer and a hidden layer. Contrastive divergence (CD) proposed by [PERSON] is used to train RBMs. In training a single RBM, weight updates are performed with gradient ascent.  
DBN can be used for time series predictions--e.g., stock market prediction, speech recognition--and to extract features of electrophysiological data [91]. For this reason, this method is a candidate to deal with APs.  
We employed the deep learning tool DeepLearnToolbox ([[https://github.com/rasmusbergpalm/DeepLearnToolbox](https://github.com/rasmusbergpalm/DeepLearnToolbox)]([https://github.com/rasmusbergpalm/DeepLearnToolbox](https://github.com/rasmusbergpalm/DeepLearnToolbox))) to train the dataset. For setting the parameters of a DBN, mini-batch is set to be equal to the number of categories so that it is easy for parallelism, and each batch consists of a number of samples in a class. In the present work, we classify the plant electrical signal induced by non-damage electrical stimulation into two types: AP and non-AP, mini-batch = 1.There are three hidden layers of our DBN, the number of nodes in the input layer is equal to sampling points of the electrical signal [92].
# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
## List of Acronyms  
\begin{tabular}{l l}
**Acronym** & **Definition** \\ RIS & Reconfigurable Intelligent Surface \\ UE & User Equipment \\ EIR-CM & Edge-Integrated RIS Control Mechanism \\ VSR-CM & Visually Steered RIS Control Mechanism \\ mmWave & Millimeter-Wave \\ LoS/NLoS & Line-of-Sight/Non-Line-of-Sight \\ LAN & Local Area Network \\ V2X & Vehicle-to-Everything \\ UAV & Unmanned Aerial Vehicle \\ TCP/IP & Transmission Control Protocol/Internet Protocol \\ FPGA & Field-Programmable Gate Array \\ CSI & Channel State Information \\ YOLOv7 & You Only Look Once version 7 (real-time object detector) \\ UART & Universal Asynchronous Receiver/Transmitter \\ \end{tabular}
## 3 Long-Range, Low-Power IoT Communication Protocols and Their Security
### NB-IoT
at the EPC, which has more resources than the UE, will then encrypt the data end-to-end to the cloud using a robust encryption algorithm such as IPSec or DTLS. **Applications. ** NB-IoT works well in indoor and outdoor applications [48]. Smart metering in Automated Metering Infrastructure (AMI) networks is an example of an IoT system [72] where smart meters send real-time reports to an electrical utility. With NB-IoT, smart meters can send data directly to a server at the utility company. Since NB-IoT supports data payloads up to 128 bytes, it can be used for smart meter reports, which may require on average 300 bytes on each smart meter uplink message [5]. Furthermore, 3 GPP provides a table of different applications in the context of a smart city, which includes infrastructure services such as water, gas, electricity, transportation, and waste management. Table 5, adapted from [67], provides examples of sensors, the frequency of the messages that they send, and how large the messages are when using NB-IoT.
# Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks
## 6 Experimental Results
### Evaluation on a Star Network  
The proposed EM and the evaluated MAC protocols were implemented on a testbed made of six PowWow nodes including one sink, in a star topology. The nodes were deployed in a room with no windows and were exclusively powered by indoor fluorescent light, which achieved a luminance of 500 k, allowing reproducibility of the experiments. Each node, except the sink, was equipped with a solar panel Sanyo AM-5913 CAR-SCE, of size 60.1 mm by 55.1 mm. The sink was battery powered. Moreover, the nodes were deployed under different lighting conditions, as shown in Figure 12. Nodes 1, 2 and 5 were located on desks, directly under the ceiling lights, while Node 3 was deployed in a more shadowed area, and Node 4 was located on a bookcase, close to the ceiling, thus receiving less light than the others. Each experiment lasted for 3 h and was performed during daytime, and the PowWow nodes have been equipped with a ULP WuRx only when the SNW-MAC protocol was evaluated. Figure 13 shows the obtained results, where Figure 13a presents the throughput, in packets per minute, achieved with the different MAC protocols. SNW-MAC significantly outperforms the two other protocols, allowing up to two-times higher throughput than PW-MAC for Node 2 due to the lower energy cost of packet transmissions. The performance of SNW-MAC is confirmed by Figure 13b, which shows that the EUC is much higher for SNW-MAC, revealing a better use of the energy budget. It is not surprising to notice that the results obtained for each node are strongly linked to the average energy budget allocated by the EBC shown in Figure 13c. As the amount of harvested  
Figure 11: Energy consumption per received packet for the evaluated protocols.  
energy varies for different nodes, the average allocated energy budget also differs. Indeed, Nodes 3 and 4 were placed in shadowed areas and therefore received less environmental energy than the others, leading to lower average energy budgets. Node 2 was on a shelf, closer to the light source, and therefore harvested more energy. Finally, Figure 13d shows the Packet Delivery Ratio (PDR) achieved by the three protocols. A PDR of 100% is achieved for all nodes that use SNW-MAC, while failed transmissions occur for the two other protocols. These results show that the use of ULP WuRx enables the design of highly-reliable protocols.