esa-sceva/satcom-chunk-collection huggingface dataset:
# Watermarking Protocols: A Short Guide for Beginners
## 2 Requirements
### Usability Requirements
#### 2.1.3 Multiple Negotiation Problem  
Web users usually purchase goods online without having to disclose their identities. All that matters is that buyers can pay what they purchase. On the contrary, copyright protection systems need to unambiguously link buyers to the contents they purchase, since, as reported in Section 1, they have to track down the infringers once illegal copies of contents have been found in the market. However, web users wish to remain anonymous during purchases. They fear that content providers may gather sensitive data about buyers and may benefit from reselling those data to other parties which can misuse it. As a consequence, to reach a good compromise between anonymity and tracing, most watermarking protocols implement identification systems based on anonymous digital certificates issued by certification authorities (CAs) [8]. However, such systems are usually considered acceptable to web users residing within geographical areas such as Western Europe, the USA and Japan, but they cannot be adopted outside these areas, where the spread of digital certificates is still rather limited.  
The considerations reported above suggest that watermarking protocols have to support multiple negotiation mechanisms so as not to force buyers to adhere to a unique and rigid identification method when they want to buy digital contents [15]. Such mechanisms can also identify the buyers by exploiting the information normally used to purchase digitalcontents, such as credit card numbers, since a credit card is always associated with a unique identity. The ultimate goal is to make the purchase of copyrighted contents as similar as possible to the purchase of any other good online.
# Outpatient Appointment Optimization: A Case Study of a Chemotherapy Service
## 2 Problem Description
### Problem Categorization  
We denote the problem using the Graham notation \(\alpha\,|\,\beta\,|\,\gamma\)[4].  
For each session, the patient should go through four stages, as mentioned earlier (Figures 2 and 3), and then, the problem belongs to the family of **Flow Shop problems**.  
Figure 4: Example of resources’ variation in a day.  
Figure 3: Gantt diagram of a particular case: no consultation, and drug mixing is allowed to process in advance.  
In addition, according to the capacity of different types of resources, the last stage (_monitoring_) can be executed for multiple patients in parallel. The problem is classified to be a **Hybrid Flow Shop** scheduling problem. Since each care session has four operations, we denote \(\alpha=FH4\).  
The resource constraints are denoted as \(res\,\lambda\sigma\rho\) according to [PERSON] et al. [5], with new modifications introduced by [PERSON] [6]. There are four types of resources required for the execution of jobs (\(\lambda=4\)). No bound on the resource capacities is known a priori (\(\sigma=\cdot\)). The requirement of resources is bounded correspondingly to the stage (\(\rho=1\)): one doctor for _consultation_, one pharmacy unit for _drug mixing_, one nurse and one chair for _installation_, and \(1/\Psi\) nurse (in terms of capacity \(\Psi\)) and one chair for _monitoring_. The resource constraints notation is then \(res\,4\,\cdot\,1\).  
The final job constraint is the waiting time between two treatment sessions of a patient (Figure 1). An outpatient chemotherapy cycle requires the patient in question to rest a certain number of days before entering the next session. In the literature, this constraint is described as the time-lag constraints, which are denoted as \(l_{jj^{\prime}}\)[7,8]. The time-lag is a generalization of the precedence constraint where one job has to wait an amount of time before processing, depending on another job. Due to the technical aspects of the chemotherapy care, we are only interested in the number of days between two consecutive jobs.  
The objective of our studied problem is to maximize the quality of service. In this case, we tend to reduce the mean waiting time of the patients, which is equivalent to minimizing the total completion time [9].  
In summary, the problem is noted as:  
\[FH4\,|\,l_{jj^{\prime}},res\,4\,\cdot\,1\,|\,\sum C_{j}\]
# An Efficient BDS-3 Long-Range Undifferenced Network RTK Positioning Algorithm
## 4 Discussion  
By making full use of the BDS-3 multi-frequency data, this paper studied the multi-frequency long-range URTK method of the BDS-3 in detail, using the most mature global navigation satellite system GPS as a comparison object. The proposed method is simple and easy to implement and can achieve centimeter-level positioning without introducing other high-precision products. Also, it is suitable for the BDS-3, GPS, and Galileo systems, which use code division multiple access technology for signal modulation. In addition, it lays a foundation for the next multi-system integration.  
In this study, before analyzing the experimental results, a simple analysis of the selected two sets of long-range reference station observation data was performed. Since the data used in this study were collected in China, the satellite visibility of the GPS was slightly lower than that of the BDS-3, which could be the main reason for the poor performance of the GPS. Then, the results of the most critical parts of the three modules were analyzed, and the analysis showed high consistency in the results. With the increase in the frequency of the BDS-3 observation data, the success rates of the AR, convergence time, stability, and positioning accuracy were significantly improved, which fully reflects the advantages of the BDS-3 multi-frequency data. Further, the distance between the reference stations of reference station network 1 was smaller than that of reference station network 2, so the result of reference station network 1 was better than that of reference station network 2 in the analysis process.  
Currently, the GPS has been in the stage of modernization, and the Galileo system has been constantly improving. In addition, multi-frequency observation data have become a mainstream trend. However, the issue of how to use the massive data rationally has been a problem requiring further research and analysis. The data selected in this study were collected in China, so they cannot fully represent the global positioning performance of the BDS-3, and global verification needs to be conducted in future work. At the same time, the observation region selected in this study was relatively simple, and more complex environments need to be studied in the future, which requires the effective use of multi-system data.  
Therefore, in future work, the following three aspects could be addressed:  
Figure 17: The residual values corresponding to different frequencies of satellites for rover station \(U_{2}\).  
Realization of a multi-system multi-frequency long-range undifferenced network RTK method;  
Verification of the global performance of the BDS-3, using the observation data of different regions in the world;  
Studying positioning performance in a complex environment.
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 6 Security Analysis  
### Forward-Backward Privacy  
First, forward security means that an update does not reveal any information about the updated keywords. Since the hash function is one-way, the server cannot decrypt the stored identifier unless the client can generate a previous search token. At the same time, every time the data owner updates, the updated keyword search token is updated, so even if the previous search token is leaked, it will not affect future security. Therefore, the scheme in this paper realizes forward privacy.  
Backward security ensures that search queries do not show indexes that were previously added but later removed. In this scenario, the file and its file state \(op\) are encrypted. Because the search results are still in ciphertext, even if it is stored in a curious server, an attacker cannot learn useful information about the index without knowing exactly what the keyword is. Thus, we support backward privacy.
## 7 Discussion  
Our analysis of the results revealed the following: the model with the lowest fidelity was the C00 model; the results of PLCC and SROCC indicated a weak positive correlation, where the OR was zero and the RMSE was 0.4546. The model with the highest fidelity was the B00 model; PLCC and SROCC indicated a strong positive correlation, and the OR was 0.066%, which is very low.  
The results for each model were mapped to a MOS rank using Table 2. We show only Figure 4 (B00 model) as this is the model with the highest level of movement and the one with the highest PLCC. In this model, the delay was set to a constant value of 400 ms (according to rec. 1541 [51]), and the other two parameters were randomly set. Figure 4 illustrates that packet loss influences the model; for packet losses higher than 0.1%, the MOS began to reduce to three. For this type of video, packet loss exerts a large influence on QoE owing to the high level of movement. Therefore, only the very low packet loss values (below 0.05%) yielded MOS measurements higher than four.  
A joint analysis of Figure 4 and the results in Table 5 demonstrate that the B00 model interpreted the conducted subjective tests by 85.3%; this is coherent with the results obtained from the model through simulation, wherein the model yielded an MOS close to 3.0 for high values of packet loss. For this type of model, a MOS less than 4.0 indicated a low-quality video because packet loss significantly affected the video quality, producing pixilation and jerky movements.  
Simulations with the I00 and C00 models allowed us to verify that the QoS parameters in Table 4 had the greatest influence. The results obtained reveal the possibility to use the proposed models in environments where the online calculation of QoE is desirable, allowing a service provider to adjust the network parameters in order to prevent user complaints.  
Our models were designed based on the distortion of transmitted videos in an emulated environment, with simultaneous variation of the three QoS parameters. As shown in the related work section, our approach has a series of advantages over earlier models, given that, in certain proposals, the videos in the video files were distorted without transmission, or, in other cases, the authors did not explain how to introduce distortion in the videos used in their proposals [52].
# Reducing Soil Permeability Using Bacteria-Produced Biopolymer
## 3 Results and Discussion
### Effect of the Hydraulic Gradient  
With some exceptions (for instance, the mixture of the soil with the LabXLG for a curing time of 14 days), most of the results show a slight decrease in the permeability coefficient with the increase in the hydraulic gradient, mainly for the natural soil and the treated soil with 0.16% of the commercial XG. Although, this tendency is not in line with most of the results obtained by [PERSON] et al. [25] for a range of hydraulic gradient from 5 to 100; in some cases, the increase in the hydraulic gradient from 5 and 10 (similar to the range considered in this work) also shows a decrease in the soil's permeability [25].  
The differences previously observed may be due to the methodology used in the tests to evaluate the permeability coefficient. As shown in Figure 3 in the present work, the  
Figure 4: Permeability coefficient of the natural soil and stabilised soil for different hydraulic gradients; (**a**) curing time of 3 days; (**b**) curing time of 7 days; (**c**) curing time of 14 days; (**d**) curing time of 28 days.  
permeability coefficient was measured with a descendent water flow. Thus, the increase in the hydraulic gradient tends to increase the vertical effective stress with the consequent decreases in the void ratio, which tends to decrease the soil's permeability, as observed in most of the results in this work. In the case of the specimens being treated with the Lab and the commercial XG, the results also seem to indicate that there may be a movement of some hydrated hydrogels towards the voids in the soil, which tends to restrict the water flow, thereby inducing the decreases in the permeability coefficient.  
The results also show that the specimens with a high level of stabilisation (i.e., with a low permeability coefficient) show a lower impact on the variation of the hydraulic gradient. Indeed, the stronger solid skeleton promoted by the stabilisation with a high content of the commercial XG (0.33%) and cement (0.33% and 1%), restricts the eventual movement of the soil particles and hydrogels, which leads to a lower influence of the hydraulic gradient.
# Static and Moving Target Imaging Using Harmonic Radar
## 3 Imaging of Moving Targets  
Military systems face a daunting task when confronted with a complex scene containing multiple moving targets as well as stationary and moving clutter [53]. They often attempt to separate moving targets from stationary objects based on (i) measurements of the range to target; and (ii) measurements of the Doppler frequency induced by target motion. Hence, target declarations are often made based on analysis of data transformed into the range-Doppler plane. Unfortunately, range-Doppler artifacts created by moving clutter objects (such as swaying trees) can often mask slowly-moving targets of interest [54; 55]. In addition, the presence of a large number of moving targets may overload downstream processing of moving target indication (MTI) detections. If, however, the targets of interest produce nonlinear responses to a radar probe signal, it should be possible to further separate them from both background clutter and linear targets that are not of interest.  
Until recently, the exploitation of non-linear phenomena has been restricted primarily to the detection of stationary targets [21; 41; 56]. If such non-linear processing techniques could be incorporated into an MTI processing framework, a substantial enhancement in MTI system detection capability could be realized. MTI data can be processed many ways. If a target is moving between multiple range bins as data are collected, change detection can be implemented to find the moving target [57]. If the target is moving slowly with respect to the data collection rate, the target remains fixed within a single range bin and Doppler processing is needed. If the data are phase-coherent, then a coherent processing scheme can be performed to create a range-Doppler plot to track the slow moving target.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Linear frequency range & 800–1000 MHz \\ Harmonic frequency range & 1.6–2 GHz \\ Transmit power & +40 dBm \\ Transmit antenna gain at fundamental & 10 dBi \\ Receive antenna gain at fundamental & 10 dBi \\ Receive antenna gain at second harmonic & 8 dBi \\ Range resolution at fundamental & 75 cm (2.46 feet) \\ Range resolution at second harmonic & 37.5 cm (1.23 feet) \\ System noise floor & \(-\)125 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the radar system.  
Prior work on the use of harmonic radar to detect and characterize moving targets has primarily focused on insect tracking [58; 59] and respiration monitoring [60]. In both these applications, appropriate RF tags are attached to the target's body and the harmonically scattered signal is used for tracking or Doppler estimation. This suppresses the large scattering signals from large clutter objects and interfaces and permits the extraction of the Doppler response.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion  
### Experimental Test Results  
#### 3.1.1 Overview of UCS Measurements  
The experimental program generated a comprehensive dataset of 185 unconfined compressive strength (UCS) measurements (171 from cement-treated samples and 14 from control samples without cement). Statistical analysis (Figure 4) of these measurements revealed considerable variation in strength values, with UCS ranging from 157.47 kPa to 5054.61 kPa across all combinations of cement content, curing period, and compaction rate. The mean UCS value was 2169.93 kPa with a standard deviation of 1289.17 kPa, indicating significant dispersion across the experimental conditions. This variability underscores the complex nature of strength development in cement-stabilized soils and highlights the need for sophisticated predictive approaches that can capture these intricate patterns.  
The quartile distribution further illustrates this dispersion, with the first quartile (Q1) at 989.44 kPa, the median at 2135.62 kPa, and the third quartile (Q3) at 3138.21 kPa. This relatively symmetrical distribution around the median suggests that the experimental design adequately captured the central tendency of UCS development. The interquartile range (IQR) of 2148.77 kPa demonstrates the substantial variability in strength outcomes, reflecting the complex interplay between cement content and curing period in strength development.  
The wide range of UCS values observed in the experimental data (4897.15 kPa from minimum to maximum) reflects the substantial influence of the investigated parameters on strength development. The lowest UCS values were associated with untreated soil samples (0% cement), while the highest values were recorded for specimens with 10% cement content cured for 28 days. This range spans typical strength requirements for various geotechnical applications, from temporary works requiring minimal strength enhancement to permanent structures demanding substantial load-bearing capacity.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 2 Background and Related Work
### PKI as an Architecture for Securing ITS Communications  
Public key infrastructure (PKI) plays a crucial role in ensuring the security of digital communications [24]. Particularly, PKI is a building block for C-ITS security, also referred to as C-ITS communications security architecture and security management in the ETSI specification [25]. The most relevant elements are the ITS-S, the root certificate authority (CA), which serves as a trust anchor and provides certificates to the enrolment authority (EA) and authorisation authority (AA). EA manages enrolment credentials (EC), which are long-term certificates used for authentication and access to ITS communications. AA issues authorisation tickets (AT), also known as pseudonym certificates, which are short-term certificates that allow ITS-S to access specific ITS services while masking their identity. The sequence of interactions that occurs from the moment a vehicle intends to enter the C-ITS network and send messages to another ITS-S is depicted in Figure 3.  
Figure 3: PKI architecture in C-ITS (adapted on [25]).  
PKI for C-ITS achieves a high level of security. Regarding handling privacy issues, C-ITS messages include several identifiers that can be used for privacy violations. As a countermeasure, users' privacy is protected by a pseudonym scheme, i.e., changing the AT frequently, which also changes all its identifiers. Message integrity, authenticity and non-repudiation allow properties to be ensured by creating a digital signature (using an AT) over the message. The ITS-S transmitter can use its AT to generate a digital signature. When a signed message is received, the receiver can verify the digital signature. Confidentiality can be ensured by encrypting the packets with a key shared with the ITS-S receiver [26].  
Despite the high-security level C-ITS PKI provides, it also has its limitations. The main drawbacks of PKI are due to processing and communication overhead latency [13]. The first one is due to the use of asymmetric cryptography to sign and verify each message, which is quite computationally demanding. The ability of each vehicle to check its certificate revocation list for a large number of certificates and verify the senders' signatures on the received messages in a timely manner forms an inevitable challenge to C-ITS efficiency requirements [27]. Lastly, C-ITS PKI experiences high communication overheads because the certificate sent (AT) for message verification is large, causing inefficiency.
## 5 Examples of Forward-Inverse Modeling
### Sunglint: A Nuisance or Can Can It Be Used to Our Advantage?
#### NASA CAR Instrument  
The measurements used by [PERSON] et al. [80] were obtained under clear sky conditions from the NASA Cloud Absorption Radiometer (CAR) deployed aboard the University of Washington Convair 580 (CV-580) research aircraft [102]. The CAR is an airborne multi-wavelength scanning radiometer that measures scattered light in 14 spectral bands between 0.34 and 2.30 \(\upmu\)m. To measure BRDFs, the airplane flew in a circle about 3 km in diameter, taking roughly 2-3 min to complete an orbit about 200 m above the surface. A servo control system is installed to allow the instrument to point at any angle from zenith to nadir, and to compensate for variations in airplane roll angle down to a fraction of a degree. Multiple circular orbits were acquired over a selected surface so that average BRDFs would be smooth. Radiometric calibration was performed at Goddard Space Flight Center prior to and just after the field experiment and a linear change between them is assumed. For more details about the BRDF measurements, see [102] and the official NASA link ([[http://car.gsfc.nasa.gov/](http://car.gsfc.nasa.gov/)]([http://car.gsfc.nasa.gov/](http://car.gsfc.nasa.gov/))).  
CAR measurements provide accurate BRDFs for all geometry angles including zenith viewing angles from the nadir (\(\theta=0^{\circ}\)) to the horizon direction (\(\theta=90^{\circ}\)) over all relative azimuth angles (0-360\({}^{\circ}\)). The resolution for both polar and azimuth angles is \(1^{\circ}\). The BRDF measurements are also accompanied by concurrent measurements of atmospheric aerosol optical thickness above the airplane and wind speeds from the NOAA Marine Environmental Buoy Database.
# Reflection Propagation Law of Electromagnetic Waves in U-Shaped Roadway
## 4 Reflected Field Strength Calculation
### Reflected Field Strength at Receiving Point
#### 5.2.4 Impact of Roadway Width  
Figure 17 shows the variation in the reflected field strength with increasing distance between the relay node and the receiving point for roadway widths of 4, 5, and 6 m. Let the distance from the relay node to the reflection plane \(m_{5}\) be \(w_{3}\) = 2.5 m and the distance from the receiving point to the reflection plane \(m_{6}\) be \(w_{2}\) = 2.5 m; the heights of both the relay node and the receiving point are 2 m, and the receiving point moves in the same manner as shown in Figure 14.  
From Figure 17, as the distance between the relay node and the receiving point increases, the attenuation of the reflected field strength at the receiving point becomes more significant. The presence of corners in the roadway accelerates the attenuation of the reflected field strength at the receiving point. When the height of the roadway remains constant, with the increase in the distance between the relay node and the receiving point, the smaller the width of the roadway, the greater the attenuation of the reflected field strength at the receiving point. This is because the smaller the width, the smaller the initial incident angle of the rays. As a result, the rays are more likely to be blocked by the obstacle plane, leading to a reduction in the number of effective reflected rays in each reflection, which is unfavorable to the propagation of electromagnetic waves [31].  
Figure 16: Reflected field strength at the receiving point as a function of the distance between the relay node and the receiving point for different dielectric constants.  
Figure 17: Reflected field strength of the receiving point as a function of the distance between the relay node and the receiving point for different roadway widths.