esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### TLS Basic Principles  
TLS is a non-destructive testing (NDT) technique based on the emission of a laser beam of known direction, used to quickly measure the position of points on the surface of a surveyed object, in a reference system with the origin in the center of the instrument. Two principles are adopted to measure distances: time-of-flight (ToF) and phase shift. In our case, a TLS ToF is used. Figure 1 illustrates the operating principle of the ToF. A short pulse is emitted by the TLS through a photodiode. The distance is obtained through the ToF of the emitted impulse, which is the time interval \(\Delta\)t taken by the impulse to reach a target and go back. Then, the distance d from TLS to target is given by Equation (1):  
\[d=\frac{c\Delta t}{2} \tag{1}\]  
where c is the speed of light.  
As for the direction of the laser beam, this is obtained by the scanning system, usually consisting of a multi-facet mirror that rotates around a horizontal axis for vertical scanning, while the entire instrument rotates around a vertical axis through a motor, like the alidade of a motorized TS. In the case of a line scanner configuration, rotation around the vertical axis is disabled. The maximum range can reach 6000 m, while the attainable precision for single points varies from 1 to 20 mm. Better values are obtained for shorter distances [23].  
For our application, we made use of a TLS Riegl VZ-1000(r), RIEGL Laser Measurement Systems GmbH, Horn, Austria, whose characteristics are summarized in Table 1.  
The Riegl VZ 1000(r) TLS is a versatile instrument. Along with the possibility to operate in line scan mode, it makes it possible to perform long-range surveys and, therefore, to obtain 3D models of objects and of the surrounding environment.  
When line scan mode is selected, the laser beam describes a line repeatedly, being addressed only by the multi-faceted mirror (see Figure 1). Using the maximum scanning speed of 120 lines per second,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Accuracy of Single Point** & \(\pm\)**8 mm** \\ \hline Precision of single point & \(\pm\) 5 mm \\ Range & from 1 m to 1400 m. \\ Sampling frequency & until 122.000 points/sec. \\ Field of view & 100\({}^{\circ}\) (Vertical +60\({}^{\circ}\) -40\({}^{\circ}\))/360\({}^{\circ}\) (Horizontal). \\ Scan Speed & 3 lines/sec to 120 lines/sec \\ Angular Stepwidth & 0.0024\({}^{\circ}\) to 0.288\({}^{\circ}\) between consecutive shoots \\ Angle Measurement Resolution & 0.0005\({}^{\circ}\) (1.8 arcsec) \\ Timesamp & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of TLS Riegl VZ-1000®.  
it would be theoretically possible to measure vibration frequencies up to 60 Hz. Taking into account the precision of measurement, it is necessary to consider a strong noise in the time series of samples, which would make it very difficult to measure oscillations with a peak-to-peak difference less than 1 cm. This limit can be overcome if information on the geometry of the surveyed object is used.
## 6 Prediction of Field Strength by Construction Log Data and Future Perspectives
### Construction Log Data  
Figure 14 shows an example of deep mixing log data of a production column in a certain project. The mixing tool is equipped with two injection nozzles at the elevation of the bottom and top mixing blades for penetration injection and withdrawal injection, respectively. In Figure 14, the positions of the bottom and top mixing blades and the rotation speed of the mixing blade are plotted along the time of day. The ground at the construction site consists of four layers: the clay-1 and clay-2 layers are soft layers with natural water contents of approximately 65-80%, while the alluvial clay and alluvial sand layers are relatively hard layers with low natural water contents. Laboratory mix tests were carried out on the soils, and the relationships between the _W/C_ ratio by changing the cement dosage and the laboratory strength, \(q_{ul}\), were examined to obtain parameters \(a\) = 37.8 MPa and \(b\) = \(-\)1.62 for the clay and \(a\) = 16.7 MPa and \(b\) = \(-\)0.98 for the sand in Equation (5).  
Figure 13: Relationship between the _W/C_ ratio and unconfined compressive strength [23].  
In the production, as shown in Figure 14, the mixing tool penetrated clay-1, clay-2, and alluvial clay at a constant speed of 1 m/min, while the rotation speed of the mixing blade was kept almost constant at 18 rpm. When the bottom end of the mixing blade reached the alluvial sand layer at \(-\)23.2 m, the penetration speed was reduced to 0.13 m/min due to the hard alluvial sand layer where the required torque of the mixing blade was increased. The mixing shaft continued to penetrate into the alluvial sand layer to a depth of \(-\)27.5 m with a low penetration speed, while the mixing blade was rotated at a constant speed with several intentional stops. During penetration, water was injected approximately 1.1 m\({}^{3}\) at depths of \(-\)23.2 m to \(-\)23.8 m and approximately 5.09 m\({}^{3}\) at depths of \(-\)24.7 to \(-\)27.5 m from the bottom nozzle for the purpose of softening the alluvial sand layer for ease of penetration. After reaching the design depth of \(-\)27.5 m, the mixing tool was moved up and down for the bottom treatment, while cement slurry of 80% _w/c_ was injected from the bottom nozzle (penetration injection).
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 4 The Proposed Clustering Protocol
### Settling Phase
#### 4.4.1 Random CH Selection  
In this phase, every node elects itself as a local CH on the basis of certain probability. In SEED [11], every node in the network selects itself as CH on the basis of the desired percentage of CHs for the whole network. Each node chooses a random number \(R_{and}\) from zero to one, then it calculates the threshold \(T_{h}\). The node compares the self-generated random number \(R_{and}\) with the calculated \(T_{h}\). If the selected random number \(R_{and}\) is less than or equal to threshold \(T_{h}\), then this specific node becomes a CH for the current round. Firstly, in our proposed protocol the CHs are selected by following the random procedure. The detail of the random CH selection process of our model is shown in Figure 4.  
The threshold \(T_{h}\) calculating formula for MOCHs is defined as follows:  
\[T_{h}=\begin{cases}\frac{p}{1-P*(r\ mod\frac{p}{p})}&\quad If\ N_{i}\in\ G\\ 0&\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\  
**Case 3**: \((R_{and}=T_{h})\): In this scenario, the node becomes a \(W_{CH}\) and waits for the decision of the BS for finalizing the optimal number of \(O_{CHs}\).  
After the random CH selection process, the nodes which are selected as \(R_{CHs}\) advertise their status message as \(Adv\)-\(R_{CHs}\)-\(Msg\) on their intra-cluster communication range. The nodes which receive this advertisement message, send the _Joint-Req-Msg_ to these \(R_{CHs}\). When the \(R_{CHs}\) receives this joint request, it checks the number of \(C_{MNs}\). If the number of \(C_{MNs}\) are less than the pre-defined number, then \(R_{CHs}\) add this node as a \(C_{MN}\) for this round. The complete random CH selection and random cluster formation procedures are defined in Figure 5.  
Figure 4: The random election, random Cluster Head (CH) selection, and random cluster formation in the network.
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 4 Results: User Tests
### Methodology
#### 4.1.4 Procedure  
* Step 1 (\(\sim\)5 min). Participants are welcomed, and introduced to the test.
* Step 2 (\(\sim\)3 min). Participants fill in a consent form.
* Step 3 (\(\sim\)3 min). Participants fill in a demographic and background information questionnaire.
* Step 4 (\(\sim\)3 min). Participants fill in the Simulation Sickness Questionnaire (SSQ) [28].
* Step 5 (\(\sim\)5 min). Part 1--First Test Condition.
* Step 6 (\(\sim\)5 min). Participants fill in the Igroup Presence Questionnaire (IPQ) [29] and SSQ questionnaires.
* Step 7 (\(\sim\)5 min). Part 1--Second Test Condition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Participant ID** & **Clip 1A** & **Clip 1B** & **Clip 2A** \\ \hline
1-7-13-19 & Mode A & Mode B & Mode B \\
2-8-14-20 & Mode C & Mode A & Mode B \\
3-9-15-21 & Mode B & Mode C & Mode A \\
4-10-16-22 & Mode A & Mode C & Mode B \\
5-11-17-23 & Mode B & Mode A & Mode C \\
6-12-18-24 & Mode C & Mode B & Mode A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Counterbalancing for Test Condition 1 (subtitling presentation modes) to avoid order effects.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Participant ID** & **Clip 2A** & **Clip 2B** \\ \hline Odd ID (1, 3, 5 \(\dots\) ) & Mode B & Mode C \\ Even ID (2, 4, 6 \(\dots\) ) & Mode C & Mode B \\ \hline \hline \end{tabular}
\end{table}
Table 2: Counterbalancing for Test Condition 2 (use of guiding methods) to avoid order effects.  
Step 8 (-5 min). Participants fill in the IPQ and SSQ questionnaires
* Step 9 (-5 min). Part 1--Third Test Condition
* Step 10 (-5 min). Participants fill in the IPQ and SSQ questionnaires
* Step 12 (-5 min). Participants fill in the ad-hoc questionnaire on subtitling presentation modes.
* Step 13 (-5 min). Part 2--First Test Condition.
* Step 14 (-5 min). Part 2--Second Test Condition.
* Step 15 (-5 min). Participants fill in the ad-hoc questionnaire on indicators.
* Step 16 (-5 min). Participants are thanked and said goodbye  
Before each test condition, the experiment facilitator helped the participants with the setting and location of the VR equipment, and launched the experience.  
Overall, the duration of the test session for each user was between 60 and 80 min.
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 1 Introduction
### Contribution  
The primary contributions of this paper are as follows:  
1. A trajectory planning method is proposed for a single AUV operating in collaboration with a USV to support data collection in UASNs and enable IoUT. The method ensures that essential data with important information from all sensor nodes is delivered to the surface before it loses its value to enable timely intervention.
2. This paper utilizes the VoI concept to quantify the time-sensitive importance of data based on its abnormality and urgency. Important data experiences rapid value decay, whereas normal data diminishes more slowly [1]. A realistic VoI formulation, as proposed in [1], is adopted to accurately capture this behaviour.
3. An optimization problem is formulated to maximize the total residual VoI collected from sensor nodes by the time it is delivered to the USV. A forward DP algorithm is used to solve this problem, providing the AUV's optimal waypoints and their best visiting order and the USV's starting and ending positions. This collectively maximizes the total residual VoI at mission completion.
4. To minimize the AUV's energy consumption and avoid long travel distances, a path design, as in [24], is proposed to reduce unnecessary movement. The AUV first determines the optimal turning angle to align with the next waypoint. If alignment is achieved, it proceeds directly to the next position, following the shortest feasible trajectory.
5. The communication range of sensor nodes is adjusted to facilitate data transmission without requiring the AUV to reach the exact node location or hover for extended periods. The AUV only needs to navigate within the communication region and remain there just long enough to complete the transmission. This ensures successful data delivery while minimizing travel time, hence maintaining residual VoI.
6. The proposed method is assessed using MATLAB R2022b simulations and benchmarked against alternative approaches to validate its performance.
# FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication
## 1 Introduction
Analogous to the hardware-based PUFs, communication medium impairments along with the physical characteristics' variation of transceivers have been exploited as a means for identifying transmitters [14; 15]. Such a concept is often referred to as RF fingerprinting (RFF), where the transient features of a transmitter in both the time and frequency domains are used to identify the packet sender [16]. These transient features are random and almost constant, and hence can be captured and used to effectively differentiate among the various network nodes with high accuracy [17; 18; 19]. However, environmental conditions introduce noise that can significantly impact the RF fingerprint accuracy, which can lead to misclassification, especially when the computational complexity of the classifier is a concern, as is the case in IoT [20]. Such a limitation can degrade the authentication process and thus compromise the security of the system [21]. Given the inherent characteristics of IoT devices and their management structure, the authentication process should be decentralized and avoid the involvement of trusted third parties. To ensure efficiency and expediency, it should also not require complex computations or the exchange of numerous messages. This paper opts to fulfill such a need by developing an authentication protocol that leverages two effective techniques, namely PUFs and RF fingerprinting, that suit IoT applications while considering the noise effect. By exploiting the inherent manufacturing variations in IoT devices, the combination of RF fingerprinting and PUF enables the unique identification of each node in the network. Such a unique combination obviates the need for traditional identification methods that rely on key storage for authentication. Since both the PUF and the RF fingerprint are based on unintended variations caused by manufacturing, we aim to increase robustness and mitigate the potential effect of noise by applying the fuzzy extractor. We call the proposed protocol Fuzzy-extractor-based RF and Hardware fingerprinting two-Factor Authentication (FeRHA). FeRHA is validated using data collected from a 64-bit PUF implemented on an FPGA and is shown to achieve high robustness to cyberattacks. The rest of the paper is organized as follows. The next section sets FeRHA apart from competing schemes in the literature. Section 3 provides some background information on PUFs, RFF, and fuzzy extractors. Section 4 states the made assumptions and provides an overview of the FeRHA. The detailed protocol design can be found in Section 5. Section 6 describes the validation setup and reports performance results. Finally, the paper is concluded in Section 7.
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 4 Results and Analysis
### Safe Minimum Separation (DR5)  
This requirement ensures that drones maintain a safe minimum separation distance at all times. It is based on a single environmental assumption (A16), which states that the built-in tolerances for separation distance calculations are sufficiently large to accommodate uncertainties and nonlinear interactions.  
If this assumption is omitted or incorrect, the safety margins may be too narrow to handle real-world variability such as geolocation errors, wind-induced drift, or communication latency. In such cases, drones may unintentionally violate separation constraints, thereby increasing the risk of mid-air collisions.  
To mitigate these risks, developers may need to adapt separation tolerances dynamically based on real-time environmental conditions. A fixed safety margin, although easier to implement, may become insufficient as environmental uncertainties evolve. For example, under turbulent conditions or high latency, the original separation parameters may no longer ensure safety. If the system assumes static tolerances that cannot be adjusted, the requirement must be revised to support dynamic safety margin computations.  
**RTD:** If the assumption regarding fixed tolerances is not validated early or does not account for dynamic factors, the requirement becomes rigid and misaligned with operational needs. This leads to a form of technical debt that requires late-stage revisions to increase flexibility or re-engineer safety logic. This is classified as _Overconstrained Assumption Debt_, which refers to requirements that are over-constrained due to rigid assumptions, reducing adaptability to real-world variability.
# Extracting the Secrets of OpenSSL with RAMBleed
## 2 OpenSSL and Apache
### Ssl/Tls  
SSL/TLS is a protocol for secure communication based on public key infrastructure (PKI). Encryption and authentication of communications using symmetric-key cryptography and public-key cryptography prevent eavesdropping, tampering, and spoofing. RSA is a public-key cryptosystem used for authentication. In authentication using RSA, a server certificate that links the public key to the server domain enables the client to verify if the party with whom they are communicating possesses the private key for the intended domain. A server certificate is issued by a certification authority based on a certificate signing request (CSR) created by the server administrator using a private key. Thus, if the private key is compromised, the certificate can be duplicated by a third party. RSA may be used to exchange the shared key for encrypted communications in TLS 1.2 and earlier. The information that forms the basis for the shared key is encrypted using the public key that corresponds to the private key held by the server and exchanged. Consequently, a third party possessing a compromised private key can intercept this exchange and duplicate the shared key. Thus, any leakage of private keys is crucial and compromises the security of the SSL/TLS protocol.  
OpenSSL [14], developed as open-source software, is often used to build web servers that support SSL/TLS communication. OpenSSL implements the functionality of the latest version of the SSL/TLS protocol, TLS 1.3.
# Study on the Combustion Behavior and Kinetic Characteristics of Semi-Coke from Oil Shale
## 4 Conclusions  
This study systematically investigated the combustion characteristics and kinetics of oil shale semi-coke using thermogravimetric analysis combined with model-free and model-fitting approaches. The key findings are as follows:  
(1) The combustion process of oil shale semi-coke occurs in three distinct stages, with the main combustion phase (310-670 \({}^{\circ}\)C) responsible for the majority of mass loss and energy release.  
Figure 8: Infrared analysis of oil shale and combustion semi-coke.  
(2) The average activation energies obtained from model-free methods (OFW and KAS) are essentially similar and show high correlation (R\({}^{2}\) > 0.996), confirming their reliability in describing the combustion kinetics.  
(3) The F2-R3-F2 model is identified as the most appropriate mechanism through model-fitting analysis, effectively reflecting the multi-stage nature of the reaction and associated mineral transformations.  
(4) SEM, XRD, and FTIR analyses confirm significant morphological and compositional changes before and after combustion, supporting the proposed reaction mechanisms.  
These results provide a theoretical foundation for the clean and efficient utilization of oil shale semi-coke as an alternative fuel. Future work should focus on validating these findings under practical combustion conditions to better assess industrial applicability.  
Methodology, L.Z., T.W. and P.X.; Validation, S.L.; Investigation, M.W.; Data curation, J.Y.; Writing--original draft, F.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Heilongjiang Provincial Natural Science Foundation of Joint Guidance Project (Grant No. LH2022E022).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 6 Evaluation of RPL and RPAL in a Real World Environment  
To validate the results from the performed simulations, we performed a set of tests on a real-life testbed (IWoTCore plus Ext_MiLive extend board [71]). For these tests, the maximal number of retransmissions was increased to three since some links in the field test were less reliable and the link qualities are extremely dynamic due to obstacles (e.g., trees, buildings or barriers). For the wireless multi-hop communication in these tests, radio transmitter modules were tuned using channel 19 and a transmit power of 3 dBm (the highest setting level in the RF230 bb's driver).
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### On-Site Personnel: The Reflectance-Based Approach
#### 2.3.2 Field Test Sites  
The test site used by UArizona and the NASA GSFC during the brief tandem flight phase of Landsat 8 and Landsat 9 is Ivanph Playa, California, USA, which continues to be used by a variety of national and international research groups for post-launch radiometric calibration work. As with Railroad Valley, temporal in situ data collection over the past 35 years have allowed UArizona to create a temporal library of hyperspectral surface reflectance data for Ivanph Playa. It is located southwest of Las Vegas, Nevada, on the Nevada-California border, and it has a useable area of approximately 2 km \(\times\) 10 km at an altitude of 800 m (Figure 10a). Traditionally, the area east of the Interstate-15 highway has been used for data collection, but this work uses the area to the west of the highway, which was chosen primarily due to easier accessibility.  
South Dakota State University (SDSU) has historically used vegetated sites located near Brookings, South Dakota, USA (Figure 10b). The three sites used for this work are 150 m \(\times\) 250 m, 120 m \(\times\) 180 m, and 120 m \(\times\) 180 m in size, at an average altitude of 500 m. The grass at each site is routinely maintained throughout the field campaign season, which typically ranges from April to October.  
Figure 9: RadCaTS TOA spectral radiance (TOA reflectance) processing.  
The reflectance-based approach is the predecessor to the RadCaTS, and the in situ measurements required to obtain TOA quantities follow the same in principle as follows: measurements of the surface BRF and the atmospheric transmission during overpass are required as inputs to a radiative transfer code to determine the TOA quantities of interest, which are then compared to the airborne or spaceborne sensor under test. Thorough descriptions of the atmospheric and surface BRF measurements have been previously published [12; 86; 94; 95; 98], but a general description relevant to the Landsat 9 work presented here is included.