esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential Study of Solar Thermal Cooling in Sub-Mediterranean Climate
## 4 Economic Feasibility
### Economic Feasibility
Figure 10: Absorption cooling system cash flow diagram with a COP of the absorption chiller of 0.79.  
system under real measurements over four summer months (May 2015-August 2015) with an average COP of 0.32 shows that the system is not feasible, as shown in Figure 11. For the observed cooling capacity, the net present value was equal to \(-\)137,684 JD with a payback period of 44 years which exceeds the expected lifespan of the project. Table 3 summarizes the results in the analysis in terms of the financial metrics chosen for the evaluation of the feasibility of the system. The simple (solid lines) and discounted (dashed lines) payback periods for the claimed COP = 0.79 (blue lines) and the observed COP = 0.32 (red lines) are shown graphically in Figure 12.  
One of the main drawbacks of the applying the payback period method is that it does not take into account several key factors including the time value of money, thus the discounted payback period is considered in this study. As shown in Figure 12, the discount payback period increased from 7 to 23 years and from 9 to 44 years for the simple and discounted payback techniques, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Values for a** & **Values for a COP = 0.32** \\  & **COP = 0.79** & **(Observed)** \\ \hline Capital cost, JD & & 243,000 \\ Maintenance cost, JD & 1\% of CC, each 5 years with an increment of 0.5\% \\ Annual operating cost (Auxiliary components), JD & 5737 & 5737 \\ Annual save (electricity), JD & 41,772 & 16,524 \\ Annual saving (CO\({}_{2}\) reduction), JD & 742 & 300 \\ Net present value, JD & 185,070 & \(-\)137,684 \\ Payback period, years & 10 & 44 \\ Rate of return & 14.31\% & 0.82\% \\ Levelized Cost of Electricity (LCOE), JD/kWh & 0.65 & 1.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the economic analysis of the solar absorption for the claimed and the observed results. Figure 12: Simple (solid lines) and discounted (dashed lines) payback periods for the claimed COP = 0.79 (blue lines) and the observed COP = 0.32. Moreover, the absorption cooling system is considered as infeasible economically with a payback period exceeding the system span life for the real observed cooling capacity. In addition, a part of the cost analysis of the absorption chiller system was performed according to the levelized cost of electricity (LCOE). The LCOE is calculated by dividing the net present value of the total cost of absorption chiller system and operating the power generating asset by the total electricity saved over its lifetime. The LCOE values of the system are equal to 0.65 JD/kWh and 1.65 JD/kWh when the system operates at claimed COP of 0.79 and observed COP of 0.32, respectively. When taking into account the electricity tariff of 0.27 JD/kWh, it is obvious that the absorption chiller system is not feasible economically.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 2 Preliminaries
Through discretization modeling techniques, the continuous road network is transformed into a structured digital map compatible with path planning algorithms such as the A* algorithm and DQN algorithm. Ultimately, this methodology facilitates the development of an intelligent planning system incorporating real-time traffic situation awareness and dynamic route optimization. The path planning module consists of the following key steps:  
1. Input of Target Information The specific target information entered by the user via the frontend interface--such as speed, fuel consumption, and other relevant details--will be extracted for use in algorithmic calculations. 2. Data Processing Data processing operations involve the preloading of offline map data, data format conversion, and the integration of real-time traffic information. 3. Geographic Grid Construction Based on the processed data from the previous step, a geographic grid model is constructed, incorporating information such as avoidance points and restricted areas, to suit different algorithms. 4. Mode Selection For terrestrial path prediction, the user is required to select between online and offline modes. 5. Algorithm Calculation Based on the entered information and fundamental geographic data, an appropriate algorithm is selected to calculate the shortest path. 6. Solution Display The path planning results not only output the set of path points but also include information such as estimated travel time, estimated distance, the number of traffic lights, and congestion levels.
## 3 Study of the Pivotal Factors
### IEEE 802.15.4 Hopping Design Method  
#### 3.2.1 Tx Terminal  
In the creation of a radio frequency communication system, it is imperative to encompass a transmitter, the transmitted signal, the wireless channel, and a receiver. The flow of the transmitter signal is depicted in Figure 10.  
To delineate the transitions from bit to symbol and symbol to chip, as well as the shifts from serial to parallel, we employed a meticulous analysis and juxtaposition. These two comparative methodologies served as the foundation for assembling the schematic illustrating the conversion from bit to chip elegantly depicted in Figure 11.  
Figure 8: Modulation and spread spectrum signal process.  
Figure 10: Transmitter signal flowchart.  
Figure 9: Schematic of hopping with diversity reception function.  
Upon inputting a binary signal consisting of two bits for testing and subjecting it to a serial-to-parallel transformation, the acquired four bits are temporarily held in storage, yielding a corresponding 32-bit output. The outcomes are elegantly illustrated in Figure 12.  
In the segment dedicated to OQPSK modulation, the existing 32-bit signal is partitioned into two distinct components: the I-phase and Q-phase. These components are elegantly depicted in Figure 13. The \"b-up\" represents the I-phase, composed of 16 bits of data, while the \"b-down\" represents the Q-phase, likewise consisting of 16 bits of data.  
Figure 11: Bit to chip diagram.  
Figure 12: Bit-to-chip timing diagram.  
Following the application of half-chord shaping, the even-numbered terms within both the I-phase and Q-phase are subjected to multiplication through a phase hysteresis of 180\({}^{\circ}\), achieved through the application of a negative half-sine wave (0, \(-1\), 0).  
The signal undergoes alterations due to environmental factors during the transmission process, thereby exerting an influence on the signal's reception at the recipient's end. Consequently, the bit error rate of the signal ascends significantly. As a remedy, a technical measure known as the Cyclic Redundancy Check (CRC) is introduced. This process hinges on polynomial division, chiefly aimed at yielding a value following the execution of CRC. It is computed both before and after transmission, with a subsequent comparison to discern the presence of any erroneous bits. The pertinent polynomial employed here is G(x) = x16 + x12 + x5 + 1. The stepwise procedure is elegantly elucidated in the diagram depicted in Figure 14[29; 30].  
A CRC-16 is created by differentiating between the I-phase and Q-phase. CRC-up and CRC-down shall each comprise 32 code words. To enhance the error correction capabilities, an error correction code is incorporated into the data volume. In reference to the CPLD code, Max Plus II is utilized for the computations, as elucidated by the outcomes presented in Figure 15.  
Figure 14: Polynomial division processing diagram.  
Figure 13: Distinguishing between I-phase and Q-phase.
## 3 UAV Flying Base Station Properties
### Providing Communication and Connectivity to Users  
The generic UAV drones have many applications and purposes, including sensing and collecting information and the delivery/moving of physical objects. The generic UAV drones typically use communications to facilitate and optimize their goals (communications for UAV); they are the beneficiary of communication service provider infrastructure, assuming the mobile user's role in cellular architecture. In contrast, the UAV flying base station's goal and purpose is to provide connectivity and networking to the users (UAV for communications) and is a part and enabler of the communication service provider infrastructure.  
**Security implications:** Due to the networking application's reliance on the communications and networking provided by the cellular infrastructure (including the UAV flying base station), the flying base station has significant security risks, i.e., the integrity and availability threats on its operations have significant impacts.
## 4 Roundness Error Evaluation
### Application of Particle Swarm Optimization in Error Evaluation  
PSO is widely used because of its easy implementation, high precision and fast convergence. The application process of particle swarm optimization in roundness error evaluation is as follows:  
Step 1 Select particles  
In the application of PSO to analyze the roundness error, the real number encoding method is adopted, and the particle form can be determined as follows:  
\[X_{i}=(x_{i},y_{i})\]  
Step 2 Initiate the particle swarm  
According to the experimental results, set a reasonable particle swarm size \(n\), set maximum velocity \(V_{\text{max}}\) and minimum velocity \(V_{\text{min}}\), population boundary \(pop_{\text{max}}\) and \(pop_{\text{min}}\), evolution times \(T\), initial particle swarm \(P(0)\) and initial velocity \(V(0)\).  
Since the least-squares method has a certain precision, the correlation parameters in the region near the center of the least-squares circle are set to the initial particle swarm.  
\[P(0)=\left\{\left(x_{i}^{0},y_{i}^{0}\right)\middle|i=1\cdots n\right\}\]  
When particle swarm optimization is applied, the maximum velocity can be set to the desired precision according to its direction moving principle, and the expansion or divergence of particle swarm can be limited. The reasonable boundary of the population limits the control search scope and avoids the waste of computing resources. The inertia weight is reduced nonlinearly according to Equation (11).  
Step 3 Calculate particle fitness and compare  
In particle swarm optimization algorithm, the high fitness is regarded as a better case. The objective function of finding the minimum radius difference in the minimum region method is taken as a negative number, and the fitness function is set as:  
\[fit(x,y)=r_{min}-r_{max}\]  
The fitness values of all particles in the current population are calculated, the fitness values of all particles in the current population are compared with the historical fitness values of individual particles. Update the current global fitness extreme value \(p_{g}=\left(p_{g1},p_{g2},p_{g3}\cdots p_{gd}\right)\) and update the individual particle fitness extreme value \(p_{i}=\left(p_{i1},p_{i2},p_{i3}\cdots p_{id}\right)\).  
Step 4 Update speed and location  
The velocity \(v_{i}\) and position \(x_{i}\) of each particle in the current particle swarm are updated according to Equations (9) and (10) to form a new particle swarm \(P(t)\).  
Step 5 Set stop conditions  
When the iteration number \(t\) does not reach the maximum number \(T\) or the precision does not reach the set precision, it returns to calculate the fitness value of individual particles and updates the particles until the termination condition is reached and the results are output.
# High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector
## 3 Direct Detection Method  
The traditional direct detection method, as described previously in References [4; 6; 7], is shown in Figure 2.  
As we can see in Figure 2, the four output electrical signals are sent to low pass filters (LPF) first. Then they pass through amplifiers to increase the amplitudes. After analog to digital conversion, the data are put into a mean filter. Finally, the mass center estimation described as the following expression (4) can calculate the deviation of the light spot.  
When the center position of the beacon spot is \([x,y]\), we can express it as follows directly using the expression of mass center [12]:  
\[x=\frac{(I_{C}+I_{D})-(I_{A}+I_{B})}{I_{A}+I_{B}+I_{C}+I_{D}},\;\;y=\frac{(I_ {A}+I_{D})-(I_{B}+I_{C})}{I_{A}+I_{B}+I_{C}+I_{D}} \tag{4}\]  
Figure 1: Quadrant Detector.  
Figure 2: Classical direct detection method.  
Here: \(I_{A}\), \(I_{B}\), \(I_{C}\), and \(I_{D}\) are respectively the photocurrent outputs from four quadrants of QD. However, when the electric signals pass through the amplifiers, DC offsets are brought in.
# Stable Matching of Users in a Ridesharing Model
## 4 Experimental Simulations
### Experimental Results  
Figure 5 shows the average SW values obtained by the proposed stable matching algorithm when using the Gale-Shapley version to reach stability versus the Munkres version intended to achieve maximum SW values. These two versions rely on different implementations of Step 4 in Algorithm 1. While Figure 5a illustrates the SW behavior in the increment of the number of passengers and drivers (\(n_{p}\) and \(n_{d}\), respectively), Figure 5b shows the boxplot of the SW values by considering all their combinations. In both of these images, it can be observed that the solutions obtained by the implementation using Munkres have higher average SW values than those obtained for the Gale-Shapley version. These differences seem to be homogeneous according to the number of available seats and drivers. However, the higher the number of available seats provided by drivers, the higher the differences in SW values between the Gale-Shapley and Munkres versions (see Figure 5a). Statistical tests show that the differences are significant between the solutions for both implementations. The maximum average SW value achieved in the experiments is 3091 by the Munkres version, while the Gale-Shapley version is 2787 (see Figure 5b); nevertheless, the Gale-Shapley version algorithm always reached a stable matching for all test cases.  
Figure 6 shows the approximated PoS values of the solutions of the proposed algorithm. Figure 6a presents these values regarding the increment in the number of passengers and drivers. Although a decrement in the approximated PoS is shown as \(n_{p}\) and \(n_{d}\) increases, there is no a clear association with respect to the configuration of the benchmark graphs. On the other hand, Figure 6b shows that the approximated PoS values ranges from 0.8712 to 0.9237, with an average value of 7.63% of degradation of the solution quality for the outcome being stable by considering all the test cases. Therefore, in general, guaranteeing the matching stability between passengers and drivers does not cause severe effects on their individual utilities.  
Figure 5: Average SW values of the proposed stable matching algorithm when using the Gale–Shapley version versus the Munkres version. (**a**) SW behavior in the increment of the number of passengers and drivers. (**b**) boxplot considering all the combinations.
# Real-Time Path Planning for Strategic Missions
## 5 Results and Discussion
### Experiment 2: Impossible Route  
In this experiment, a case was implemented for which obstacles isolate the target and the robot, and a message of impossible route is defined. Figure 9 illustrates the experiment, as well the video available at [[https://youtu.be/tRVyPzqXLRo](https://youtu.be/tRVyPzqXLRo)]([https://youtu.be/tRVyPzqXLRo](https://youtu.be/tRVyPzqXLRo)).  
Analogously to the first experiment, the second begins with an obstacle-free environment, see Figure 9a. Notice the path is identical to the one previously. The path changes just after setting zone 5 and 13 as locked ones. Notice in Figure 9b that obstacle 5 demands a path redesign, but obstacle 13 does not. In Figure 9c, note that the obstruction occurs in an area where the robot has already passed. In that case, zone 2. As expected, the route does not suffer any change, and the robot keeps moving forward. However, when zone 9 becomes occupied, as shown in Figure 9d, LTA* looks for a new feasible route. Nevertheless, the robot is not on this route, and so, the shortcut strategy takes care of finding a path to connect them. Finally, Figure 9e illustrates the robot following the path, just before receiving the Route Impossible alert. This event happens after zones 17 and 21 are labeled as not-safe to navigate, as shown in Figure 9f. In other words, the impossible route alert indicates there is no path connecting the starting and the destination zones. Then, the robot must stop or return to its home-position. In this paper, we opted for the latter approach, as highlighted in the experiment video.  
Figure 8: Experiment 1: The goal achievement. Snapshots of the route traveled, from the initial state (**a**) to the final one (**f**), passing by some intermediate stages (**b**–**e**).  
Figure 9: Experiment 2: The impossible route. Snapshots of the path traveled, from the initial state (**a**) to the final one (**f**), passing by some intermediate stages (**b–e**).
# Study on the Mechanism of Stress Sensitivity Changes in Ultra-Deep Carbonate Reservoirs
## 3 Influence of Different Young's Modulus on Stress Sensitivity
### Fractured Carbonate Reservoirs  
For fractured carbonate reservoirs, fractures are present within the rock itself, compromising its integrity. When external stress is applied to the rock, the fractures may expand or close due to variations in stress. The sensitivity of fractured carbonate reservoirs to stress changes varies according to Young's modulus of the rock.  
Fractured carbonate reservoirs with a low Young's modulus possess higher elastic deformation capability, and fractures tend to close relatively easily without significant deformation. Thus, for fractured carbonate reservoirs under these circumstances, their  
Figure 4: Stress sensitivity curve of matrix-type carbonate rock under variable Young’s moduli.  
stress sensitivity may be low. However, in fractured carbonate reservoirs with a high Young's modulus, the rock stiffness is greater, and external stress changes tend to cause the widening of the already existing fracture(s). At this point, the stress sensitivity of fractured carbonate reservoirs may generate high sensitivity, expressing that the rock is highly sensitive to variations in stress. It should be noted that the stress sensitivity of fractured carbonate reservoirs is also influenced by other factors, such as the number, size, and distribution of fractures. In addition, the actual rock properties and technical conditions can also impact stress sensitivity. If the Young's modulus E of these rocks is changed to 10-30 GPa, the stress sensitivity curve would exhibit characteristics as shown in Figure 5.  
Overall, fractured carbonate reservoirs exhibit significantly higher stress sensitivity than matrix-type carbonate reservoirs. With increasing Young's modulus, the rate of permeability decrease due to external stress varies; its stress sensitivity uniformly decreases. This demonstrates that the stiffness of the fractured carbonate reservoirs increases vis-a-vis their Young's modulus; i.e., with increasing Young's modulus, the sensitivity to stress changes reduces. Fractured carbonate reservoirs with a high Young's modulus can increase the rock's stiffness, resulting in decreased permeability decline in response to changes in external stress.
## 3 Key Component Design
### Tape Retracting Device  
After verification of the steel tape, the device starts to work, which mainly consists of a servo motor, a fixed base, and a work driving arm, as shown in Figure 6. The steel tape box is fixed on the fixture. When the tape is retracted, the work driving arm drives the steel tape handle and the steel tape is wound into the tape box until the photoelectric switch is triggered and the rotary table moves to the horizontal state (mentioned in Section 3.1). The servo motor stops working and the zero fixture is manually taken out from the rotary table. Then, the last 5 m length tape belt is manually rewound.  
Figure 5: Force measuring device: 1. Steel tape; 2. stepping motor \(A\) for pressing mechanism; 3. fixed part; 4. pressing block; 5. slide block; 6. stepping motor \(B\) for tensioning mechanism.
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 2 Background
### Jammers
\begin{table}
\begin{tabular}{c c} \hline
**Jammer Type** & **Description** \\ \hline Constant [8, 10, 11, 12, 22] & Continuously emits random noise over a wireless medium to interfere with legitimate communications. \\ \hline Deceptive [10, 12, 22] & Periodically inject valid packets in their transmissions to deceive receivers into believing that legitimate messages are being sent. \\ \hline Random [8, 10, 11, 12, 22] & Operates on jam and sleep periods. They will jam for a time duration of \(\mathfrak{t}\), and sleep for a duration \(\mathfrak{t}_{\text{S}}\) before jamming again. Both \(\mathfrak{t}\) and \(\mathfrak{t}_{\text{S}}\) can be either fixed or values random. \\ \hline Reactive [8, 10, 12, 22] & Continuously monitor a communications channel and only jams when it senses activity. \\ \hline Intelligent [11, 22] & Uses knowledge of the communications protocols they are seeking to jam and analyze the transmissions to target specific messages or message types. \\ \hline \end{tabular}
\end{table} 
Table 2: Common Jamming Techniques. all the frequencies in the bandwidth. In FHSS systems, data is sent through a narrowband signal, but the frequency and channel being used to transmit signal rapidly changes. The frequency hopping pattern is known by the sender and intended receiver, allowing for messages to successfully be sent between them. In addition to filtering and spread spectrum techniques, various unique anti-jamming methods have been proposed to protect against the attacks, including exploiting reactive jammer reaction times [27], jammed node isolation [28], and directional antennas [29]. While all these methods can be effective at countering jamming attacks, no solution can offer absolute protection in all scenarios. For this reason, creating robust autonomous convoy solutions that can maintain operation in the face of a jamming attack is important for widespread commercial and military adoption.
## 2 Methodology
### Structure and Working Principle of the Ultrasonic Methane Sensor  
The traditional PD measurement structure directly measures the phase difference in an ultrasonic signal at a transmitter and receiver using only one acoustic tube. However, this structure cannot effectively compensate for the impact of temperature. In this study, a dual-channel acoustic tube measurement structure was adopted, and a reference acoustic tube was added to the single acoustic tube (measurement acoustic tube). The two acoustic tubes were completely identical in structure and length, but the reference acoustic tube is sealed with pure air inside. When the external temperature changed, the sound velocity ofthe acoustic tubes changed accordingly, and the dual-channel acoustic tube structure played the role of temperature compensation to a certain extent. Figure 1 shows the structure of the dual-channel acoustic tube measurement unit.  
The ultrasonic transmitter was set to a continuous-wave working mode, and an ultrasonic receiver was placed at the other end of the acoustic tube to detect the transmitted ultrasonic signal. Due to the attenuation of acoustic energy during transmission, the amplitude of the received ultrasonic signal was relatively low after passing through the acoustic tube. Thus, the received signal was sent to a signal amplification circuit for amplitude amplification. Since the phase detector we use is designed using a D trigger, the input signal must be a square wave signal. Therefore, the amplified signal was then sent to the shaping circuit to convert the sine wave signal into a square wave signal. The phase difference between the signals of the dual-channel acoustic tubes was then obtained through a phase detector. The circuit composition of the ultrasonic sensor module is shown in Figure 2.