esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and System Performance  
In this section, a comparison in the performance of the proposed scheme with References [18; 31; 32] is discussed. The initialization phase will only be implemented one time. Therefore, the required comparison will be in the authentication phase of performance. Where, the standard costs regarding to authentication which are computational and communication cost as shown in Tables 2 and 3, are considered. Such these costs are well known and common in most authentication researches.  
The experiments are conducted on a 2.5 GHz Intel i3-M380 processor, with a Windows 7 operating system of 64-bits, and 4 GB RAM (Lenovo PC HK limited, Hong Kong, China). Where, MATLAB R2014a is used to implement our experiments. While, Java class is used to implement the cryptosystem.  
In Table 2, a comparison is made among References [18; 31; 32] with the proposed scheme in terms of their computational costs. The total computational cost of our proposed scheme for the authentication phase is 14.1 milliseconds, which is deemed the lowest. The other mentioned papers have respective computational costs of 23.13, 34.65 and 34.65 milliseconds. In addition, another comparison of the communication costs, as shown in Table 3, is the total communication cost, which is  
Figure 6: Key Session from the SB to the CC.  
510 bits for the proposed scheme and 800, 1040 and 1040 bits for the other schemes respectively. In our proposed scheme, three considerations are involved with the key requirements of authentication phase (_Hsbj; Msbwj; IDsbj_). The full length of the communication requirement is 3 \(\times\) 128 = 354 bits. In the upcoming requirement, two considerations are elated, which are 2 \(\times\) 128 = 256 bits. Thus, we proved that the suggested scheme is the best and overcomes the others, that is References [18; 31; 32] in terms of computational cost and communication costs.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 2 Improved Retinex Model
### The Improved Retinex Algorithm  
The dependence of the SSR's enhancement effect on the Gaussian scale factor, \(\sigma\), can be illustrated as follows. When the value of \(\sigma\) is large, the color fidelity of the processed image is good. However, details in the image become difficult to identify. Conversely, when this value is reduced such that the details are highlighted after enhancement, the color fidelity of the processed image is poor. In particular, in areas where the local color changes significantly, there is a \"halo\" effect after SSR enhancement (Figure 2b).  
To address the defects of a traditional Retinex algorithm, we proposed the use of Gaussian pyramid transformation and an improved bilateral filtering function that increases edge retention and improves noise removal to replace the Gaussian filter function. The mathematical model for this improved bilateral filter function is deduced by improving the spatial kernel function and using a rotating window function to determine the pixel difference scale parameters.  
The operation of our technique proceeds as follows. The color space of the image is transformed from RGB to HSI in order to separate brightness from color information. Improved bilateral filtering of Retinex enhancement is subsequently performed on the intensity component of the HSI image in order to obtain a reflection image whose edges are maintained and are unaffected by changes in brightness. This enhanced image is then returned to the RGB color space, following which a Gaussian-Laplacian pyramid is generated by successively applying a Gaussian filter to an input image and scaling down the resolution of the resulting image. Finally, sub-images in the pyramid are enhanced using the improved Retinex algorithm for multi-scale processing before Laplacian reconstruction is applied to complete image enhancement. A flowchart detailing the operation of this improved Retinex algorithm is shown in Figure 3.  
Figure 2: Illustration of the limitations of the single-scale Retinex (SSR) algorithm. (**a**) Input image. (**b**) Result of SSR enhancement. The “halo” effect observed in areas with significant changes in local color is marked in red.
## 3 Results
### Verification of Proposed Algorithms with Virtual Data  
Using the simulation algorithm, the initial step involves specifying the floor plan and the receiver's location within the graphical user interface (GUI). Subsequently, sound sources are added sequentially. The algorithm executes the ray tracing program for each sound source, generating a corresponding ray diagram. Upon adding the first sound source, the resultant Sound Pressure Level (SPL) measured was 36.0 dB, which aligns with the SPL of the sound source, given that it is the only source present.  
The second sound source is then incorporated, prompting the ray tracing engine to run again and produce the ray diagram for this additional source, which has an SPL of 26 dB. Combining both sound sources at the receiver's location, the resultant SPL is calculated to be 36.41 dB. This is the same with the manually calculated SPL. Figure 10 illustrates the three-dimensional ray diagram generated during the simulation verification process.  
Figure 9: Resultant SPLs vs. surface materials.  
With the distance and SPL established, a manual calculation using the appropriate formula yields a resultant SPL of 36.41 dB. The simulations with and without reflections were 36.41 and 38.1, respectively. The collected data are systematically tabulated in Table 2, and a comparison of the results is presented in Table 3.  
The analysis indicates a difference of 1.69 dB between the simulation with and without reflection, highlighting the importance of a 3D simulation compared to a 2D approach.
## 4 Cryptanalysis of Son et al.'s Son et al. (2019) Protocol
### Vehicle Impersonation Attacks  
Vehicle impersonation attacks involve attackers disguising themselves as legitimate vehicles registered with the TA. These attacks pose serious risks, such as obtaining paid services under false pretenses or engaging in malicious activities to avoid detection and punishment, thereby compromising the security of VANETs [26].  
Our scrutiny of [PERSON] et al.'s initial V2I authentication stage indicated the following major flaw: if an attacker acquires a vehicle's pseudo-identity RID, they can convincingly impersonate that vehicle and pass the authentication checks of all RSUs. Worryingly, this could even apply to unregistered vehicles, highlighting the severity of this security breach. The vehicle impersonation attack proceeds as follows:  
1. \(\mathcal{A}\) selects a random number \(b_{i}^{*}\) and a timestamp \(T_{1}^{*}\).
2. Upon choosing the random number and timestamp, \(\mathcal{A}\) computes the following values from the acquired pseudo-identity \(RID_{i}\) as the vehicle enters the legitimate \(RSU_{t}\): \[P_{il}^{*} =b_{i}^{*}\cdot P_{t}\] (1) \[P_{i}^{*} =b_{i}^{*}\cdot P\] (2) \[M_{1}^{*} =RID_{i}\oplus h(ID_{t}||P_{il}^{*}||T_{1}^{*})\] (3) \[M_{2}^{*} =h(RID_{i}||P_{il}^{*}||T_{1}^{*})\] (4)
3. \(\mathcal{A}\) then creates a valid authentication request message \((M_{1}^{*},M_{2}^{*},P_{i}^{*},T_{1}^{*})\).
4. \(RSU_{t}\) authenticates the message \((M_{1}^{*},M_{2}^{*},P_{i}^{*},T_{1}^{*})\), calculates \(SK_{il}^{*}=TID_{i}^{*}\oplus h(n_{j}||ID_{t}||k)\), and forwards the message \((M_{3}^{*},M_{4}^{*},Q_{t}^{*},T_{2}^{*})\) to \(\mathcal{A}\).
5. \(\mathcal{A}\) receives the message and computes \(Q_{il}^{*}=b_{i}^{*}\cdot Q_{t}^{*}\), \(TID_{i}^{*}=M_{3}^{*}\oplus h(Q_{il}^{*}||RID_{i})\), and \(SK_{il}^{*}=h(Q_{il}^{*}||TID_{i}^{*}||RID_{i})\).
6. \(\mathcal{C}\) Consequently, both \(\mathcal{A}\) and \(RSU_{t}\) generate the session key \(SK_{il}^{*}\) and maintain it.  
Through this attack, \(\mathcal{A}\) can convincingly impersonate a legitimate vehicle and gain access to \(RSU_{t}\).
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
## 3 Temperature Dependence in Data from the Saint Petersburg Observatory
### Temperature Dependence in December 2021 with an Abnormally Large Seasonal Temperature Change  
Throughout almost the entire November 2021, the outside temperature at the SPG observatory was fairly flat and positive between 3 and 5 \({}^{\circ}\)C. However, at the end of the month, it dropped first to \(-3\)\({}^{\circ}\)C, and in early December even lower, reaching \(-14\) and \(-17\)\({}^{\circ}\)C on 5 and 6 December, respectively. Then, by 12-13 December the outside temperature returned to 0 \({}^{\circ}\)C and remained at this level until about 19 December, when it dropped again, and by the end of the month it was back to 0 \({}^{\circ}\)C again.  
All this also affected the inside temperature measured at the variometer sensor, which is shown in Figure 5. In turn, this led to significant deviations in the G plot under reduced temperatures on 7 December 2021 and its high correlation with the temperature plot, despite making absolute observations on that day (first vertical line in Figure 5).  
Figure 4: Graphs of G (blue) and variometer sensor temperature (orange) based on 1-min data over 1-28 November 2021. Vertical lines indicate moments of absolute measurements.  
Figure 5: Graphs of G (blue) and variometer sensor temperature (orange) based on 1-min data in December 2021. Vertical lines indicate moments of absolute measurements.  
The temperature-dependent deviations of G from their usual permissible values during the first 12 days of December turned out to be too large, and the G plot itself was no longer a criterion of the quality of the obtained data, but a scaled and shifted temperature plot. As in the case of the data for February of the same year, adjusting the G plot by discarding the observed baseline values was unsuccessful due to the impossibility of accounting for intraday temperature variations. While the February 2021 G plot shown in Figure 2 was somehow acceptable as a criterion for the quality of the quasi-definitive data [21], the December 2021 G plot shown in Figure 5 cannot be evidence of the reliability of the obtained data.
## 3 Formulation of the Wind Power Curtailment Scheduling Problem
### Constraints for the Expected Wind Power and Load  
#### 3.1.1 Active Power Balance Equations  
\[\sum_{i=1}^{N_{G}}p_{G,i,t}+\sum_{i=1}^{N_{W}}\int_{0}^{l_{W,i,t}}\big{(}1-F_{ AW}(p_{AW,i,t})\big{)}dp_{AW}=\sum_{i=1}^{N_{B}}p_{D,i,t}^{f},\qquad\qquad \forall i,t, \tag{14}\]  
where the superscript \(f\) denotes the expected value of the random variables, and \(p_{D,i,t}\) is the load on bus \(i\) at time \(t\), which is the sum of the EV load and the non-EV load.  
1. Operating limits of the conventional units: \[p_{G,i,t}+r_{up,i,t}\leq P_{i}^{\max}\cdot u_{i,t},\qquad\qquad\forall i,t,\] (15a) \[p_{G,i,t}-r_{dn,i,t}\geq P_{i}^{\min}\cdot u_{i,t},\qquad\qquad\forall i,t,\] (15b) where \(P_{i}^{\max}\) and \(P_{i}^{\min}\) are the maximum and minimum generation limits of generator \(i\), respectively.
2. Minimum up and down time limits: \[\big{(}X_{i,t}^{on}-T_{i}^{on}\big{)}\cdot\big{(}u_{i,t-1}-u_{i,t}\big{)}\geq 0,\qquad\qquad\forall i,t,\] (16a) \[\big{(}X_{i,t-1}^{off}-T_{i}^{off}\big{)}\cdot\big{(}u_{i,t}-u_{i,t-1}\big{)} \geq 0,\qquad\qquad\forall i,t,\] (16b) where \(X_{i,t}^{on}\) and \(X_{i,t}^{off}\) are respectively the turn-on and turn-off periods before time \(t\), and \(T_{i}^{on}\) and \(T_{i}^{off}\) are the minimum up and down times of unit \(i\).
3. Hourly ramp up/down limits of the conventional unit: \[p_{G,i,t}-p_{G,i,t-1}\leq UR_{i}^{\max}\cdot 60,\qquad\qquad\qquad\forall i,t,\] (17a) \[p_{G,i,t-1}-p_{G,i,t}\leq DR_{i}^{\max}\cdot 60,\qquad\qquad\qquad\forall i,t,\] (17b) where \(UR_{i}^{\max}\) and \(DR_{i}^{\max}\) are respectively the upward and downward maximum ramping rates of generator \(i\).
## 4 Results
### Optimal Placement Strategy for Air Quality Monitors
#### 4.4.1 Data Definition and Preprocessing  
To accurately assess the need for air quality monitoring, we defined and preprocessed three key datasets.  
* D1: Density of Vulnerable Populations This dataset represents the density of populations vulnerable to PM across different regions. The density values range from 0 to 1, with values closer to 1 indicating higher concentrations of vulnerable groups, such as older adults and children under 14 years old.
* D2: Accessibility to PM-Related Facilities This dataset measures the accessibility of relevant facilities such as hospitals and clinics within each region. The accessibility score ranges from 0 to 1, with values closer to 1 indicating poorer accessibility, reflecting areas where vulnerable populations have limited access to essential services.
* D3: Environmental Indicator (PM Concentration) This dataset provides the average PM concentration for each region, with values normalized between 0 and 1. Higher values correspond to poorer air quality, indicating a greater need for air quality monitoring in these areas.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 4 Experiments and Results
### Analysis of Results  
In this section, we show how the double optimization problem (minimizing the number of drones and maximizing the average leftover energy) is addressed. We plot the relation between various drone parameters, as in Figure 18.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Length & 4 km \\ Width & 4 km \\ Normalized \(H_{min}\) & 0.185 km \\ Height threshold & 0.05 km \\ Air density & 1.293 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Various environmental parameters for the Pettimudi region used in the experiments.  
Figure 17: Coverage of drones for the Pettimudi region as obtained by the region-filling algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Coverage angle & 30\({}^{\circ}\) \\ Weight & 5 kg \\ Diameter (distance between two opposite & 0.4 m \\ rotors) & 20,000 mAh \\ Battery voltage & 22.2 V \\ Battery energy & 444 Wh \\ Battery efficiency & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Various parameters of the drones used for the experiments.  
Figure 18: Analysis of various drone parameters: (**a**) number of drones required vs. \(W\); (**b**) \(W\) vs. average leftover energy; (**c**) area vs. number of drones required; (**d**) coverage angle vs. number of drones required; (**e**) \(H_{max}\) vs. number of drones required; (**f**) \(H_{max}\) vs. average leftover energy; (**g**) number of base stations vs. number of drones required; (**h**) number of base stations vs. average leftover energy.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
###### Abstract  
In this paper, we propose an RFID-based location-positioning platform for managing cart operations at vast and fast-moving distribution facilities. Our work was motivated by a real-world problem in a large airmail center. Our system requires that each cart is affixed with an active RFID tag, and RFID readers are installed at multiple locations of the facility. The locations of the tagged carts are determined by the estimated angles of arrival communication signals between the readers and the tags and the signal strengths, and the information about their positions are stored in the platform continuously. This platform enables the users to locate tagged objects in real time, thereby saving staff members a significant amount of time from searching for the right carts in the highly dynamic environment. Consequently, the mail facility can benefit from the increased efficiency. Furthermore, the system is also able to reduce the chance of having a delay in delivering mail bags to unit load device (ULD) area and misplacements of mail bags to ULDs. We further analyze the the huge amount of historical data collected from the RFID infrastructure of the airmail center for the cart movements within the facility and observed previously unrecognized operational issues. We also discuss some of the challenges and problems faced in this project. We believe that this application can bring huge benefits to businesses and organizations along supply chains by effectively anticipating the increasing demand for logistics services in the age of electronic retailing.  
RFID; real-time locating; traceability; postal services; big data; analytics +
Footnote †: journal: Applied Sciences
# An Integrated Platform for the Internet of Things Based on an Open Source Ecosystem
## 4 Evaluation  
The IoT integrated application development platform proposed here was evaluated by an industrial IoT scenario. In an industrial production environment, functions such as environment monitoring, data acquisition processing, and equipment management can be implemented by IoT technology to improve production efficiency and ensure production safety. At present, a typical industrial IoT application is where a factory uses sensors to monitor various data in the production environment [81]. The smart factory has typical IoT application characteristics, such as a diversity of devices and transmission protocols, stringent real-time business requirements, and complexity of data processing. The sensing data collected about the smart factory also need to be transmitted to a remote cloud computing platform for intelligent processing, which can detect abnormal events. The factory production environment is complex and often located far from enterprise management personnel. Therefore, it requires a long-distance wireless data transfer mechanism to achieve remote data collection and management. These protocols also can be easily integrated with enterprise applications.  
Figure 5 shows an industrial IoT scenario that implements the acquisition, monitoring, and management control functions of a factory. The simulation environment is shown in Table 5. The Modbus protocol does not have open API and requires professional knowledge for development. For easy integration with various application systems, application-oriented protocols, such as CoAP/HTTP/MQTT/WebSocket, were applied. At present, the CoAP and HTTP protocol are used for remote data transmission. In the future, more application layer protocols will be added, analyzed and compared.  
Below, we present the evaluation results, including the system functions and performance analysis.  
\begin{table}
\begin{tabular}{c l} \hline
**Technology** & **Product Name and Quantity** \\ \hline Gateway & Raspberry Pi 3 B+ running Eclipse Kura and CoAP Server \\ Link & LoRA protocol: Lora-gate-bridge, Lorsaverer, Lora-app-server, packet forwarder, or wireless and wired LAN established via Tp-Link \\ LoRAWAN Gateway & 1 \\ LoRA Terminal Module & 2 \\ Transfer protocol & CoAP/HTTP/MQTT/WebSocket \\ Security & DTLS Eclipse Scandium \\ Sensors & One temperature and humidity sensor DHT1 connected with GPIO, Two LED lights \\ MQTT Client & PC \\ Cloud Platform & Docker-based Eclipse Kapua running on ThinkPad T470 Laptop \\ CoAP Client & Copper1.0.1 + Firefox55 plugin \\ GUI & Node-Red +node-red-contrib-coap 0.3.0 \\ \hline \end{tabular}
\end{table}
Table 5: Devices list used for evaluation.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## 3 Results and Discussion
### Effect of PEG Block Length on the Hydrogels  
We have previously studied the effect of PEG block length in PLA-PEG-PLA to the kinetics of gelation process and hydrogel properties [29]. Compared to the control hydrogel prepared from PLLA-PEG-PLLA (MW: 800-2000-800) and PDLA-PEG-PDLA (MW: 800-2000-800) simple micelle solutions, mixing the different copolymers with longer PEG block yielded different sol-to-gel transition temperatures and mechanical strength. The inter-micelle exchange of PLA chains for the micelles with short PEG polymer were found to be more rapid than those with long PEG polymer. The micelles made of the mixed PEG size copolymers (as illustrated in Figure 7) exchange PLA blocks with different rate and the sol-to-gel transition temperature changed linearly by the mixing ratios [29]. Moreover, the hydrogels from the short/long mixed PEG micelles exhibited remarkably higher modulus [29]  
In this study we mixed long-PEG copolymer, PDLA-PEG-PDLA (MW: 800-3350-800) to prepare D-micelles (Figure 7) and used for 3 LM-hydrogel formation. Table 2 summarizes the observation of sol-to-gel transition temperatures and flow behavior of 3 LM-hydrogels by tilt method. In agreement with our previous work, the PEG length affected to the gel formation and properties of 3 LM-hydrogel system. The transition temperature is the highest when using D-micelle-30S (short-/long-PEG = 30:70 wt %), that also indicates the slower chain exchange of longer polymers. However, those gels with long/short mixed length micelles (50S and 30S) do not flow over 60 min and are apparently more robust than the hydrogel with D-micelle from PDLA-PEG-PDLA (800-2000-800) only.
# Sensitivity Analysis of P-Wave Polarization Direction and Velocity Gradient Inversion
## 5 Tests and Discussion
### The Applicability to Complex Media  
The relief model used in this section is shown in Figure 9, with dimensions of \(200\times 800\) m\({}^{2}\). The velocity range is 3000 m/s to 4000 m/s. We discretized the model with 16 grid points in the horizontal direction and 64 grid points in the vertical direction. 64 sources were placed at equal intervals between depths of 2 m to 792 m on the left side of the model, and 64 receivers were placed at equal intervals between depths of 4 m to 798 m on the right side of the model.  
We obtained the estimated projection data based on Equation (6). The actual projection data were obtained by numerically solving Equations (1) and (2) through simulation. After removing abnormal rays, a total of 3827 rays were generated from left to right, which were subsequently used to invert for the relative velocity gradient, as illustrated in Figure 10.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PLCC** & **MSE** \\ \hline ART method & 0.9105 & 1.0984 \(\times\) 10\({}^{-6}\) \\ Proposed method & 0.9900 & 2.1695 \(\times\) 10\({}^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation results of gradient maps.  
Figure 9: Relief velocity model.  
By employing the iterative scheme described in Equation (23), we have obtained the relative velocity gradient map illustrated in Figure 11. The results reveal that this method exhibits adaptability to complex media, effectively identifying geological boundaries. This capability underscores its potential to enhance subsurface imaging and interpretation in geophysical applications. Furthermore, the method could serve as a valuable tool for future investigations in intricate geological settings, facilitating improved understanding of subsurface structures and processes.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model  
The codewords are transmitted by spreading over a wide block of time-frequency OFDM slots. After demodulation and demultiplexing, the receiver obtains the codewords that are distorted by noise and jamming. The jamming portion is removed using the SSR technique [8]. The receiver decodes the original messages based on the jamming-free codewords.