esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 4 Results and Discussions
### Extensive 4G LTE Mapping and Signal Performance Measurement  
The network mapping and performance at the three locations are presented in Figures 2-7. In an attempt to compare signal strengths across the different metrics used, the performance at each location for the metrics has been set side by side. Figure 2 compares the received signal received power (RSRP) performance for UNILAG, Ikorodu, and Oniru VI. A similar comparison approach is adopted in Figures 3-7. Figure 3 shows the reference signal received quality (RSRQ) monitoring and comparison for the three locations. Figure 4 depicts the received signal strength indicator (RSSI) coverage in three locations and shows how the three locations vary in signal strength. Figure 5 shows the ratio of the packet component carrier (PCC) to the signal-to-noise ratio (SINR) for each location. This is a good indicator of the stability of the signals and the download speeds obtainable in the locations. Figures 6 and 7 show the speeds as well as the efficiency of the network for physical and logical links.  
Figure 3: Signal monitoring of the reference signal received quality (RSRQ).  
Figure 2: Signal monitoring of the reference signal received power (RSRP).  
Figure 4: Signal monitoring of the received signal strength indicator (RSSI).  
Figure 5: Signal monitoring of the ratio of the packet component carrier (PCC) to the signal-to-noise ratio (SINR).  
Figure 6: Signal monitoring of the packet component carrier (PCC) physical (PHY) downlink (DL) throughput.  
Figure 7: Signal monitoring of the packet data convergence protocol (PDCP) downlink (DL) throughput.
## 10 Open Challenges and Future Directions
### Energy Consumption
* **The optimization of the UAV flight tour inspired by combinatorial optimization and the integer programming problem**, in which, starting from the launch position of the UAV, we try to find an optimal set of paths that the swarm must traverse in order to return to its initial position. This is a problem of NP-Hard complexity [12; 49; 59] because we cannot pass twice through the same waypoint. Moreover, this problem becomes even more complex when we add the constraints of energy consumption and emissions. Typical examples were presented by [PERSON] et al. [39] and [PERSON] et al. [12] for drone delivery services. * **A novel energy harvester for powering UAVs** that will allow the possible recharge of an ultra-fast rechargeable battery onboard a UAV and an improvement in the lifespan. A research work conducted in this direction has been proposed in [35] and the analytical results show possible flight time improvements in the range of 3 to 16 min,depending on the flight conditions. In terms of energy usage, it appears that decreasing the UAV size and payload weight may be more important than communication methods for enhanced energy efficiency.
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.3 Sprint  
In [26], the authors proposed self-organized proactive routing protocols for non-uniformly deployed underwater networks (SPRINT), a proactive routing protocol that reduces the routing delay. Here, the nodes calculate received signal strength (RSS) and the number of hops and consider the number of neighboring nodes to optimize the routing path. At first, the gateway (GW) broadcasts a route request (RR) packet, and when a node in the network receives the RR, it measures the RSS value and records it in a table. The node then compares the RSS and the number of hops to reach the GW. It will choose the node with higher RSS as it uses RSS to measure the distance. The shortest distance requires less energy to transmit the signal, but this would increase the throughput of the signal. Hence, it uses the number of hops required and neighbor numbers to select the final candidate to forward the data. It regularly updates the routing path, thus making it more efficient. It can adapt to a dense, partially dense, and sparse network system. It needs no external device to extract its location.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Implementation Details  
#### 4.3.1 Evaluation Metrics  
Following existing video grounding works, we evaluate the performance on two main metrics:  
**mIoU**: This metric measures the mean Intersection over Union (IoU) between the model's predicted intervals and the reference intervals. By averaging the IoU values over the entire test set, it reflects the overall localization accuracy of the model and effectively evaluates the precision of temporal segment predictions.  
**Recall**: We adopt \"\(Rank@n\), IoU \(=m\)\" as the evaluation metrics, following [1]. The \"\(Rank@n\), IoU \(=m\)\" represents the percentage of language queries having at least one result whose IoU between \(top-n\) predictions with ground-truth is larger than m. In our experiments, we reported the results of \(n=1\) and \(m\in\{0.3,0.5,0.7\}\).
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 2 Wind Turbine Model Description
### Horizontal Conductor Model  
GPR is the phenomenon of a huge amount of electricity entering the earth and producing large voltages that cause serious damage to wind power plant equipment. Therefore, a horizontal conductor is one of the important materials used in a wind turbine grounding foundation to mitigate the impact of GPR and the grounding system impedance. Although increasing the length of the horizontal conductor does not continuously lead to a noticeable reduction, finding an effective length is always desirable, and this paper is no exception, as it examines this in Section 4.  
The behavior of the horizontal conductor [39] in connecting the tower grounding to the local transformer is very similar to that of the transmission line, so to estimate the proper length, it is essential to calculate the conductor parameters in the same way as the line impedance; these are obtained from [39; 40] as follows:  
\[L_{c}=\frac{\mu_{0}}{2\pi}(\ln\frac{2l_{c}}{\sqrt{2 rD}}-1), \tag{3}\]  
\[C_{c}=\frac{2\pi\epsilon_{0}\epsilon_{r}}{\ln\frac{2l_{c}}{\sqrt{2 rD}}-1}, \tag{4}\]  
\[G_{c}=\frac{\pi}{\rho(\ln\frac{2l_{c}}{\sqrt{2 rD}}-1)}, \tag{5}\]  
where \(L_{c}\), \(C_{c}\) and \(G_{c}\) are the line inductance, capacitance and conductance in H/m, F/m and S/m, respectively, \(r\) is the conductor radius, \(l_{C}\) is the conductor length, \(D\) is the burying depth, \(\mu_{0}\) is the vacuum permeability and \(\epsilon_{0}\) and \(\epsilon_{r}\) are vacuum and relative permittivity, respectively. In this paper, the mutual impedance between the horizontal grounding conductors and vertical rods is ignored.
# Smart Warehouses: Rationale, Challenges and Solution Directions
## 4 Results
### RQ-3: What Are the Current Distinctive Characteristics of Smart Warehouses?
The computer of things (CoT), as an evolution on IoT and cloud computing, combines the physical resources with the management through cloud services, allowing it to be accessed independently from the location with simple and common methods [21]. In addition to an IoT infrastructure, a multi-agent system (MAS) is also recommended to support modern warehouse management processes [29]. Furthermore, a data capturing and processing module is crucial [32]. This module collects data from different management systems such as the sales system, WMS, and transportation management system (TMS) and provides filtered, sorted, and uniform outputs for decision support models to base decisions on. The decision system automatically performs risk analysis and formulates the ideal workflow [17]. Finally, all warehouse operations have to protected from cybercrime. In order to cope with this, a multi-level strategy is proposed by [PERSON] et al. [31]. On a top-level, consultation for the security strategy is advised, followed by risk management to prevent attacks and training from identifying and controlling them. Self-learning from software through monitoring and detecting could provide a solution.
# An Energy-Efficient Integration of a Digital Modulator and a Class-D Amplifier
## 4 Block Diagram of the Integrated System  
### Preferred Modulation  
Angular modulation schemes such as FSK and PSK are more robust than linear techniques such as amplitude shift keying (ASK) or on-off keying (OOK) [10] while maintaining simplicity. Quadrature amplitude modulation (QAM) modulation has an increased spectral efficiency, but transceivers are more complicated and more power needs to be transmitted to achieve a similar bit-error rate (BER) [11]. Among its resources, the software-defined radio (SDR) includes direct digital synthesis (DDS), which provides sinusoidal signals with arbitrary frequency or phase [12; 13; 14]. This makes PSK a good choice for wireless links where power is a limited resource.  
A switching-circuit phase modulator is shown in Figure 2[10]. Actually, the signal at the output of the comparator block of Figure 2 is the rising edge PWM of \(v_{in}\), with a carrier frequency of \(2f_{c}\). The Flip-Flop circuit divides the number of pulses between the two.
## 4 Results and Discussion
### Results of All Models Using 1D and 2D Data  
The comprehensive overview of experimental results is presented in Table 9 and Figure 7. In Table 9, \"/\" denotes that the image transformation method was not used. We assessed the results of various ML models, including traditional ML methods and DL models. The DL models include single models such as 1D CNN, MobileNet, EfficientNetB4, 2D CNN, and 2D FCN, as well as an integrated architecture, the 2D EnCF, that merges the advantages of the 2D CNN and 2D FCN. The best-performing traditional machine learning method is Random Forest (RF), which achieves an accuracy of 0.9242. However, the 2D EnCF ensemble method outperforms all traditional machine learning methods, achieving an accuracy of 0.9528 on the RP feature transformation. The single deep learning models achieve varying levels of performance, with the best-performing single model being the 2D CNN on the RP feature transformation with an accuracy of 0.9340. However, the 2D EnCF ensemble method  
The results demonstrate that the 2D EnCF ensemble method with RP is superior to both conventional ML methods and single DL models with respect to performance, and the combination of various models can leverage their strengths and mitigate their weaknesses, yielding superior overall results. Additionally, the choice of feature transformation significantly determines the performance of DL models.
## 4 DDPG-Based Offloading Design
```
1:Randomly initialize the actor network \(\mu_{\theta^{\mu}}\) and the critic network \(Q_{\theta^{Q}}\) with weights \(\theta^{\mu}\) and \(\theta^{Q}\);
2:Initialize target network \(\mu\) and \(Q\) with weights \(\theta^{\mu^{\prime}}\leftarrow\theta^{\mu}\), \(\theta^{Q^{\prime}}\leftarrow\theta^{Q}\);
3:Initialize the experience replay buffer \(B\);
4:for each episode \(n=1,2,\cdots,N\)do
5: Reset simulation parameters for the environment;
6: Randomly generate an initial state \(s_{1}\);
7:for each time slot \(t=1,2,\ldots,T\)do
8: Select an action \(a_{t}=\mu(s_{t}|\theta^{\mu})+\
abla\mu\) to determine the power for transmission and computation;
9: Execute action \(a_{t}\) and receive reward \(r_{t}\) and observe the next state \(s_{t+1}\);
10: Store the tuple \((s_{t},a_{t},r_{t},s_{t+1})\) into \(B\);
11: Sample a random mini-batch of \(N\) transitions \((s_{t},a_{t},r_{t},s_{t+1})\) from \(B\);
12: Update the critic network by minimizing the loss \(L:\) ;
13: Update the actor network by using the sampled policy gradient: \(\
abla_{\theta^{\mu}}I\approx\frac{1}{N}\sum_{t=1}^{N}\
abla_{A}Q(s_{t},a| \theta^{Q})|_{a=a_{t}}\
abla_{\theta^{\mu}}\mu(s_{t}|\theta^{\mu})\);
14: Update the target networks by: \(\theta^{\mu^{\prime}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}}\); \(\theta^{Q^{\prime}}\leftarrow\tau\theta^{Q}+(1-\tau)\theta^{Q^{\prime}}\);
15:endfor
16:endfor
```  
**Algorithm 2** The DDPG-based Offloading Algorithm
## 3 Data Description and Methods  
### Data Description  
The data used in this study include the VIX and the closing prices of the S&P 500, which were utilized to analyze market volatility and movements. The data cover the period from January 2020 to December 2024 and were collected from the Yahoo Finance platform. This study specifically employed data starting from 2020 to minimize discrepancies with pre-COVID-19 data and to effectively reflect structural changes in the market.  
Figure 1 visualizes the movements of the S&P 500 and VIX over the selected period. The blue solid line represents the S&P 500 index, while the orange dashed line shows the VIX index. The graph highlights that S&P 500 and VIX generally exhibit an inverse relationship. For example, during the early pandemic phase in 2020, VIX experienced sharp spikes while the S&P 500 declined. As the market stabilized, VIX decreased and the S&P 500 resumed an upward trend. This inverse pattern is also observed during correction periods in 2022 and 2023.  
To better understand the characteristics of the data, statistical analyses were conducted. The results are summarized in Table 1.  
The statistical analysis results indicate that VIX data exhibit high skewness (2.5211) and kurtosis (11.1301), reflecting frequent extreme market events. In contrast, S&P 500 data follow a near-normal distribution with slight positive skewness (0.2480) and a kurtosis close to zero (\(-\)0.0865). The Jarque-Bera test confirms that both datasets significantly deviate from a normal distribution. Moreover, the Augmented Dickey-Fuller (ADF) test results show that VIX data are stationary, whereas S&P 500 data are nonstationary. These findings highlight the necessity of proper preprocessing techniques, such as VMD, to address the nonstationary characteristics of the S&P 500 data and account for extreme events in the VIX data.
# Towards an Optimized Blockchain-Based Secure Medical Prescription-Management System
## 2 Blockchain Technology
### Types and Features of Blockchain Technology  
There are mainly four kinds of blockchain network: public, private, consortium, and hybrid [27]:  
* Public blockchain, in which all the participated users can join the network and can perform read and write transactions, without verifying the transacting parties' identities, such as the Bitcoin blockchain.
* Private blockchain: Private blockchains are permissioned networks where prior authorization is required to join. Only trusted and pre-authorized participants can join the network and perform write transactions, such as the Quorum and Hyperledger blockchain.
* Hybrid combines elements of both public and private blockchains. It enables a network to be decentralized and transparent, while also providing secure and private transactions for certain participants.
* Consortium blockchain: Also known as Federated blockchain, is a subtype of permissioned blockchains, in which a group of entities or organizations jointly manage the network.  
Among the key features of blockchain technology that make it a revolutionary technology in the digital world, we can mention the following:  
* Decentralization: One of the most prominent features of blockchain technology is its decentralized nature, which eliminates the need for intermediaries or third-party entities to validate transactions. Transactions are validated and recorded on a distributed network of computers or nodes, allowing for greater security and transparency.
* Immutability and data integrity: Once a new transaction has been added to the blockchain database, it cannot be modified directly, and it can only be deleted after consensus has been reached. The blockchain's participants can utilize this feature to enhance regulatory compliance and minimize fraudulent activities.
* Transparency: All transactions on the blockchain network are visible to all network participants, providing a transparent and auditable record of every transaction. This enables greater accountability and trust among network participants.
* Efficiency: Blockchain technology enables fast and efficient transactions, eliminating the need for intermediaries and reducing transaction costs. However, it is important to note that scalability issues can pose challenges to the speed and efficiency of certain blockchains. Despite this, blockchain technology remains a popular choice for applications that prioritize fast and secure transactions, such as financial services and supply chain management.
* Security: Blockchain technology employs advanced cryptographic algorithms and protocols to secure transactions and protect the network from malicious attacks. Public blockchains, with their decentralized structure, typically exhibit greater resilience to cyberattacks. However, private and permissioned blockchains, due to their limited number of nodes, may face vulnerabilities. Additional security measures are crucial for these networks. Overall, while blockchain offers inherent security features, the implementation strategies must adapt to address specific vulnerabilities.
# Hg\({}^{2+}\) Optical Fiber Sensor Based on LSPR with PDDA-Templated AuNPs and CS/PAA Bilayers
## 3 Results
### Hg\({}^{2+}\) Detection  
Before Hg\({}^{2+}\) detection, the LSPR spectrum of the blank sample was obtained by immersing the prepared sensor probe in the ultra-pure water for 10 min and then drying it in a vacuum drying oven at 60 \({}^{\circ}\)C. In the Hg\({}^{2+}\) measurement, Hg\({}^{2+}\) can be absorbed by the amino (-NH2) and hydroxyl (-OH) groups in the CS to form stable chelates, and also be chelated with a large number of carboxyl (-COOH) groups in the PAA. This series of reactions will increase the degree of cross-linking between the two-layer sensing materials, and accordingly, increase the refractive index of the sensing materials. According to refractive index sensing in Figure 5b, a redshift of the LSPR peak is expected when increasing the concentrations of Hg\({}^{2+}\) solution. The LSPR spectrum with different Hg\({}^{2+}\) concentration was monitored when the sensor probe was immersed into the Hg\({}^{2+}\) solution for 10 min and then removed from the Hg\({}^{2+}\) solution and afterward dried completely in the oven. When the sensor probe was removed from the Hg\({}^{2+}\) solution, some Hg\({}^{2+}\) ions were immobilized by the use of the CS/PAA film, and hence the refractive index changed due to the Hg\({}^{2+}\) contact. The spectral measurement in the air instead of the solution has been proven to be effective [14]. Moreover, this method can maximally exclude the influence of solution salinity and PH because the sensor probe has been dried completely before measuring the LSPR spectrum. After each measurement, the sensor probe soaked in HCl solution at PH = 3 for 10 min until the resonant wavelength recovered to the original wavelength. In the acid solution, the -NH2 and -COOH groups in the sensor layer material were protonated, resulting in the desorption of the adsorbed Hg\({}^{2+}\), thus ensuring the sensor can be reused.  
Figure 6a illustrates the normalized LSPR spectra of the blank sample and the Hg\({}^{2+}\) samples with different concentrations. The change in position of LSPR peak is quite evident. Figure 6b illustrates the wavelength shift of the absorption peak relative to the blank reference. The absorption peak wavelength shifted by 17.6 nm when the Hg\({}^{2+}\) concentration increased from blank to 30 \(\upmu\)M. The linear fitting of the peak response indicates the proposed sensor has a sensitivity of around 0.51 nm/\(\upmu\)M. When the concentrate increased to 50 \(\upmu\)M, the sensitivity began to decrease, and the wavelength shift seems to  
Figure 5: Refractive index sensing based on the proposed LSPR sensor. (**a**) Absorption spectra of different concentrations of sucrose solution; (**b**) LSPR resonant wavelength as a function of the refractive index of the sucrose solutions.  
be saturated. A limit of detection (LOD) was calculated to be 0.52 uM (3S) based on three times the standard deviation (SD) of measured signals of blank samples.
# Comparison of Distributed Tamper-Proof Storage Methods for Public Key Infrastructures
## 3 Concepts and Evaluation Methods
### IPFS Private  
The IPFS [14] is a distributed Peer-To-Peer (P2P) network intended for storing and allowing access to data, websites, and applications. In contrast to centralized systems that manage the data access via location-based addressing, IPFS implements a content addressing scheme [15] for accessing data stored within it. Using this scheme, a distributed download of one single file via multiple peers in different locations is possible. Said peers are called _IPFS Peers_ in the following.  
The decentralized storage of files offers, beyond others, some noteworthy advantages:  
1. **Redundancy:** Centrally stored files are lost, if the central file server is out of operation, e.g., after a fire hazard. Using IPFS, data redundancy can be achieved since data stored on one given peer is also replicated to multiple peers within the network. Therefore, the data availability is enhanced.
2. **Performance:** The data query can be accelerated because, using a custom routing scheme within the IPFS network, files can be queried from geographically close IPFS peers. This routing scheme is facilitated by distributed hash tables (DHTs) and is based on a P2P basis [16]. Thus, a file that has been uploaded in Brazil can be queried within a few milliseconds by a client located in Germany (Note: After complete file replication within the network and if there are IPFS peers closely located).
3. **Tamper-proof design:** For third parties, manipulation or deletion of a file stored within the IPFS is made almost infeasible since the data redundancy and content addressing schemes cryptographically and physically deny any attempt to alter data.  
The content addressing works by cryptographically hashing the given files using the _SHA-256_ hashing algorithm. Thus, two identical files will always produce the same data hash (called _Content identifier (CID)_). Furthermore, each file is split up into chunks of the same size to allow for an even more extended spreading of data. This is made possible by using Directed Acyclic Graphs (DAGs) and Merkle-DAGs in particular [17].  
Fundamentally, the IPFS is a homogeneous network comprised of peers that are storing the desired files on their local hardware. Each peer is therefore responsible for storing and managing the data, as well as communicating with other network participants. In general, IPFS is a publicly available implementation that can be used either in a private deployment or using public Application Programming Interfaces (APIs) and frameworks, although the inner workings of the technology are common for every setup.  
When using a public IPFS setup, each peer needs a financial incentive to store the data on its local hard drive. Within a private setup, this requirement is not given since every peer is set up-demand and is well-intended by default.
## 3 Results and Discussion  
The offline training shown in Figure 5 has been carried out in the gateway server, and the online identification should be implemented on different species of trees through the WASN sensor and soil MC nodes. The main process is as follows. Firstly, the sample dataset collected by sensors is preprocessed and normalized to be dimensionless. Next, the most representative nine characteristic parameters are screened from the sample dataset by the mRMR algorithm. Then, the feature fusion procedure is executed by weighing these selected features, and the optimal MC diagnosis model is trained by the FA-ISSA-SVM mechanism. Finally, real-time AE signals, root-soil moisture, air temperature, and air humidity data are input into the model to diagnose the MC level in the heart of the standing trees.