esa-sceva/satcom-chunk-collection huggingface dataset:
# Condenser Design for On-Board ORC Recovery System
###### Abstract  
In this study, an ORC plant is considered that recovers the heat source from 1800 cc Diesel Engine exhaust gases. The power recovered by the ORC system could be used, as an auxiliary system, to provide energy to the electronic parts of the car. The purpose of this study is to investigate the possibility of allocating this ORC plant into a vehicle and in the design of the condenser, with the purpose of reaching the best configurations that lead to low-pressure drop and compact dimensions. The Organic Rankine Cycle converts thermal energy from low-grade heat sources into electricity. The thermodynamic cycles to produce 5 kW were simulated using a custom software application. The basic cycle was chosen to guarantee the most compact configurations. The selected thermodynamic parameters are based on the need to cope with atmospheric conditions. The working fluid chosen is R245 fa, due to its thermodynamic characteristics. Since the electrical part would be partially satisfied by this system, the entire power of the thermal engine would be dedicated to mechanical use. It could also be assumed that, as a consequence of the settings of this plant, a reduction in fuel consumption could be expected, which, although at a percentage that cannot be theoretically evaluated at the moment, is nevertheless predictable. In this first stage of the evaluation, the condenser design is presented and analyzed.  
Organic Rankine Cycle; thermodynamic feasibility; condenser design Article
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Brake-Safe Control
#### 3.3.2 Control  
The idea of brake-safe control is to constrain the multi-vehicle system to remain in the so-called **brake-safe states**. Assuming vehicle \(j\) is in a state \(x_{j}(t_{0})\) at time \(t_{0}\), the authors note  
\[B_{\sigma(j)<\sigma(i)}(x_{j}(t_{0})):=\{x_{i}\in X_{i}|\forall t\geq t_{0}, \tag{3}\]  
\[\Big{(}y_{i}\big{(}t,\underline{u_{i}}x_{i}(t_{0})\big{)},y_{j}\big{(}t, \underline{u_{j}}x_{j}(t_{0})\big{)}\Big{)}\
otin R_{\sigma(j)<\sigma(i)}\},\]  
where \(\underline{u}\) represent braking control signal. Consequently, if \(x_{i}(t_{0})\in B_{\sigma(j)<\sigma(i)}(x_{j}(t_{0}))\), then vehicle \(i\) can safely brake until it stops without colliding with \(j\) and does not violate the priority (i.e., does not enter the completed obstacle region) even if \(j\) uses \(\underline{u_{j}}\) to brake from \(t_{0}\) and onward.  
The control law was described below.  
Let \(u_{i}^{impulse}\in U_{i}\) represent the impulse signal for vehicle \(i\) defined by  
\[u_{i}^{impulse}(t)=\left\{\begin{array}{ll}\bar{u_{i}}&if\ t=0\\ \underline{u_{i}}&if\ t\geq 1\end{array}\right.. \tag{4}\]  
The control law is then be introduced synthetically:  
\[g_{i}(x)=\left\{\begin{array}{ll}\underline{u_{i}}&if\ \exists\ \sigma(j)<\sigma(i),\exists t\geq 0\\ s.t.\Big{(}y_{j}(t,\underline{u_{j}},x_{j}),y_{i}(t,u_{i}^{impulse},x_{i}) \in R_{\sigma(j)<\sigma(i)}\Big{)}\\ \bar{u_{i}}&else\end{array}\right.. \tag{5}\]  
This equation means that if the paths among these vehicles at the next time slot under the worst-case scenario are collision-free, the simulated reached state is the brake-safe state.
## 3 Results and Discussion
### Model Calibration and Verification
#### 3.3.3 Extreme Condition Test  
The extreme condition test in system dynamics is a method used to evaluate the behavior and robustness of a model when subjected to extreme or highly challenging scenarios. It involves pushing the model to its limits by introducing inputs or conditions that go beyond typical ranges or exceed normal operating conditions. This test is particularly valuable for assessing the model's performance, identifying potential vulnerabilities, and understanding how the system responds under extreme circumstances. By subjecting the model to extreme conditions, it becomes possible to observe how the system behaves and whether it exhibits any unexpected or undesirable outcomes. This test helps uncover potential risks, weaknesses, or limitations in the model's structure, assumptions, or feedback mechanisms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (Year)** & **Historical Data** & **Simulation** & **Error** \\  & **(m3/Year)** & **(m3/Year)** & **(\%)** \\ \hline
2010 & 986,659 & 986,659 & 0.00\% \\
2011 & 942,819 & \(1.12\times 10^{6}\) & 18.79\% \\
2012 & \(1.98\times 10^{6}  
It also provides insights into how the system may respond to unprecedented events or disruptions, allowing for a better understanding of its resilience.  
In this test, the input value of the model takes its limit value, and its effect on the variables of the model is investigated. As an example, with an 80% reduction in the impact of dumping sites and almost making the variable ineffective, water pollution will decrease significantly to 2.22 \(\upmu\)g/L (approximately 43% decrease) (see Figure 10). By observing the results of this change or other changes to the input variables, practically no unreasonable behavior has been created.  
In addition to the above example, Figure 11 shows that with an 80% decrease in Santee Cooper Winyah Generating Station effluent, water pollution will decrease significantly to 1.97 \(\upmu\)g/L (approximately 50% decrease) (see Figure 11).  
Figure 11: The relationship between the impact of the reduction of Santee Cooper Winyah Generating Station effluent on water pollution.  
Figure 10: The relationship between the impact of the reduction of dumping sites on water pollution.
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### Embedding Domain
#### 2.1.9 Discrete Shearlet Transform  
In this approach, various band transforms have been evolving till now based on several significant steps that are featured by information processing, which relies on an increased efficiency characteristic, and on the conservation of different conventional approaches, which are based on the multi-resolution analysis. Some examples that are related to this approach include the Contourlet, Shearlet and Curvelet. The Discrete Shearlet Transform (DST) approach represents a well-localised waveforms pyramid, while varied orientations are enclosed by this pyramid involving various locations, scales and shapes when classical wavelets are produced differently from them. Therefore, classical wavelets are applied with the state-of-the-art algorithms within the domains of object and signal processing domains. Furthermore, their processes are efficient in resolving relevant problems, which rely on different multi-scale systems such as the fragile direction that are based on such systems. Shearlet transform [49] is defined as structuring the affine system for detecting different geometrical structures, which are based on various signals of dimensions [50].  
In addition, there are other algorithms that are used to embed watermarks in objects such as Patchwork and Intermediate significant bit [20]. Patching is a random statistical method that is invisibly used in an original image by a Gaussian distribution using a repeated mark-up pattern while the intermediate significant bit was developed to improve LSB by replacing the original pixels of the images within the blank/filled area with watermark pixels [51]. Moreover, a histogram shifting can be used to include the secret data into the compressed images [52]. This technique examines the pixel histograms of the cover image and looks for pairs of peak and zero values. During the data embedding process, the pixels between the peak and zero points are changed. The 1-bit hidden data are embedded in each pixel in the peak points. The others have been altered, and no hidden informationhas been embedded. The maximum hiding ability for the hidden data to be embedded in this scheme is the number of pixels in the peak points.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### RSSI Based Solutions
#### 4.3.2 Game-Theoretic Optimization of RPL (GTM-RPL)  
The authors [38] proposed a game theoretic based mobile RPL (GTM-RPL) using a transmission rate as a variable to achieve a higher packet delivery ratio (PDR). In a GTM game formulation, a network is assumed to consist of a static sink node, static nodes for providing better coverage, and mobile sensor nodes. While playing mobile management games, players representing sensor nodes in a network within the same collision space compete to send data packets to sink nodes in a selfish manner. The utility function of other players is affected by the action of a player in which actions include changes in data rate, Trickle timer settings, transmission power, and parent node. To send data at high transmission rates, a lower cost for energy consumption is assumed in nodes having higher priorities. The mobile intensity in an application is reflected by nodes sharing applications with specific mobility metrics. In addition, to compute the link quality, nodes measure the RSSI of each message at the MAC layer.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Working Principle** & **Issues Addressed** & **Advantages** & **Disadvantages** \\ \hline Modified & Performs immediate ETX & Delay & Shows a high PDR of 90\%, high throughput of 19 & It causes longer delays as compared to RPL, solutions to overcome these longer delays are not discussed. \\ Multiple & Performs immediate ETX & Delay & & \\ Modified & probing and avoids loops & & & \\ RPL [45] by introducing parent ID & & & & \\ in the DIO messages. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.  
The cost of a node to send data to the sink node in a mobile environment at a specific rate is defined as the Payoff function. Strategies are defined as possible actions performed by each node. Measurement of cost in mobility presence is defined by mobility function. Similarly, the optimal strategy is chosen according to the game designed to maximize the Payoff function to find an optimal solution of a game. The outcome is the optimum transmission rate, which is the Nash Equilibrium for a node; this means that a player reaches an optimal strategy with no incentive to changes in its strategy while other players maintaining their current strategy. Optimum transmission rate changes with the changes in node movement, which means that the RSSI is affected or when the transmission rate of any other node changes affecting Link Quality Indicator (LQI). Changes in the value of RSSI are used as a mobility detection parameter. Simulation results show that GTM-RPL achieves a PDR of 88% for mobile nodes as compared to RPL with 44% PDR and 78% for mobile RPL (mRPL) [46]. Similarly, an improvement of 16% energy consumption is shown by GTM-RPL as compared to mRPL, furthermore, GTM-RPL takes a 15% lower average delay compared to mRPL.
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 5 Proposed Method
### Proposed Method for Land-Based Station
#### 5.1.3 Computational Complexity of Land-Based Localization  
Table 3 gives the number of multiplications required for some elements.
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 5 Results and Discussion
### Latency  
The latency of the two modules for the different DOPs is shown in Figure 9a. For the training module, we report the latency of the backward pass only, as the latency of the forward pass is the same as for the inference module. As expected, the latency decreases with higher DOP up to a minimum of only 81 ns for the inference module and 267 ns for the backward pass. It is to note that the latency does not decrease linearly with the DOP, because, even though the computations of the ANN layers themselves are accelerated, there exists a static component resulting from communication overhead and data transfer between the layers. Additionally, the higher parallelism increases the number of operations of the critical path; thus, Viviado HLS inserts additional registers which increase the latency.  
In summary, the latency of our implementation satisfies the constraints of most communication scenarios. For instance, in ultra reliable low-latency communication (uRLLC), end-to-end latencies as low as 5 ms are required. As there are many other, more complex processing blocks in a baseband processing system, the latency overhead of our demapper is negligible, and thus, it could be integrated in such a system without violating the timing constraints.  
Figure 8: Resource utilization on Ultra96-V2.
## 3 Experiments
### Self-Collecting Signals

Table 1: Comparison of the effects of the four feature extraction methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subject Number** & **KNN/\%** & **RF/\%** & **NB/\%** & **DA/\%** & **BPNN/\%** \\ \hline
1 & 93.3 & 96.7 & 98.3 & 98.3 & 100.0 \\
2 & 100.0 & 95.6 & 98.3 & 96.7 & 96.7 \\
3 & 98.3 & 94.4 & 98.3 & 96.7 & 98.3 \\
4 & 98.3 & 97.8 & 100.0 & 96.7 & 100.0 \\
5 & 95.0 & 96.1 & 95.0 & 98.3 & 98.3 \\
6 & 100.0 & 97.8 & 96.7 & 98.3 & 100.0 \\
7 & 96.7 & 95.0 & 95.0 & 96.7 & 93.3 \\
8 & 100.0 & 94.4 & 98.3 & 98.3 & 96.7 \\
9 & 98.3 & 98.3 & 100.0 & 100.0 & 100.0 \\
10 & 93.3 & 96.7 & 98.3 & 100.0 & 96.7 \\
11 & 96.7 & 97.2 & 98.3 & 98.3 & 93.3 \\
12 & 100.0 &It can be seen from Tables 2 and 3 that the DA classification algorithm has the highest recognition rate, and the average recognition rate is 98.2%. The standard deviation of recognition rates between different subjects and between different gestures is 1.3% and 0.5%, respectively, and robustness is the best. The individual differences between subjects amount to less than 2% in using RF, NB, and DA classifications. In the KNN and BPNN classification, the impact of individual differences is only 2.3%. And the effect of different gestures on the optimal size is less than 2.7%. All results show that the optimal size number has no difference in subjects and gesture actions, and the effect of DA is best.
(2024) provided insights into the complex nexus between climate change, food security, and public health in Tanzania, revealing how climate stress increases susceptibility to health issues like diarrhea, thereby compromising food production and access [15]. Further contributing to this discourse, [PERSON] et al. (2021) analyzed the effects of climate change on crop production and household food security in Ethiopia, emphasizing the importance of local adaptation strategies and climate change awareness [16]. [PERSON] et al. (2022) examined the relationship between climate variables and the dimensions of food security in sub-Saharan Africa, finding that rainfall positively impacts food security, while temperature and CO\({}_{2}\) emissions present mixed effects [12]. These studies emphasize the need for the development of strategies that address the immediate and long-term effects of climate change on agriculture and food systems. These strategies must consider the impact of climate variability on agricultural output and food accessibility, as well as the sustainability of food systems under changing climatic conditions. The studies suggest that a multifaceted approach that combines sustainable agricultural practices, public health interventions, and targeted policy frameworks is necessary to build resilient communities and food systems that can withstand the challenges posed by climate change. Although previous research has provided a foundation for this study, there is a notable gap in comprehensive, localized analyses that use historical climate data with socioeconomic implications for the study area. To address this gap, this study employs advanced modeling techniques and leverages cloud computing to analyze and predict the impacts of temperature and precipitation changes on the region's agricultural productivity and water resources. The significance of this work lies in its potential to contribute actionable insights and evidence-based strategies to mitigate climate change's adverse effects on one of India's most climate-vulnerable regions. The aim of this research is to quantify the effects of climate-induced changes in temperature and precipitation on crop yields and water availability, to identify vulnerable areas and communities for targeted interventions, and to recommend adaptation strategies that are tailored to the specific needs and conditions of the region. The ultimate goal is to provide stakeholders with the necessary knowledge and tools to build a sustainable and resilient future for the region, preserving its agricultural heritage and ensuring socio-economic stability in the face of climate change. This study was carried out using the Google Earth Engine platform, and the related JavaScript code is presented in Figure 1.
# Analytic Combinatorics for Computing Seeding Probabilities
## 4 Results
### Substitutions Only
Proposition 2: _The probability that a read of size \(k\) has no seed under the uniform substitutions model is asymptotically equivalent to_  
\[\frac{C}{z_{1}^{k+1}},\]  
_where \(z_{1}\) is the root with smallest modulus of \(1-pz(1+qz+\ldots+(qz)^{\gamma-1})\), and where_  
\[C=\frac{(1-qz_{1})^{2}}{p^{2}z_{1}\big{(}1-(\gamma+1-\gamma qz_{1})(qz_{1})^{ \gamma}\big{)}}. \tag{11}\]  
_If \(z_{1}=1/q\), expression (11) is undefined and the constant \(C\) should be computed as \(-P(z_{1})/Q^{\prime}(z_{1})\), where \(P(z)\) and \(Q(z)\) are the respective numerator and denominator of \(S_{\gamma}(z)\) in expression (10)._  
Example 3: _Approximate the probability that a read of size \(k=100\) has no seed for \(\gamma=17\) and for a substitution rate \(p=0.1\). To find the dominant singularity of \(S_{17}\), we solve \(1-0.1z\times(1+0.9z+\ldots+(0.9z)^{16})=0\). We rewrite the equation as \(1-0.1z\times(1-(0.9z)^{17})/(1-0.9z)=0\) and use numerical bisection to obtain \(z_{1}\approx 1.0268856\). Substituting this value in equation (11) yields \(C\approx 1.396145\), so the probability that a read contains no seed is approximately \(1.396145/1.0268856^{101}\approx 0.095763\). For comparison, a 99% confidence interval obtained by performing 10 billion random simulations is \(0.09575-0.09577\). The computational cost of the analytic combinatorics approach is infinitesimal compared to the random simulations, and the precision is much higher for \(k=100\)._  
Overall, the analytic combinatorics estimates are accurate. Figure 4 illustrates the precision of the estimates for different values of the error rate \(p\) and of the read size \(k\). One can also compute the probabilities by recurrence using Theorem 3, after replacing the term \(1+qz+\ldots+(qz)^{\gamma-1}\) by \((1-(qz)^{\gamma})/(1-qz)\) in expression (10). Denoting \([z^{k}]S_{\gamma}(z)\) as \(s_{k}\), one obtains for every positive integer \(\gamma\)  
\[s_{k}=\left\{ \begin{array}{ll}1,&\text{if $0\leq k<\gamma$,}\\ 1-q^{\gamma},&\text{if $k=\gamma$,}\\ s_{k-1}-pq^{\gamma}\cdot s_{k-\gamma-1},&\text{if $k>\gamma$.}\end{array} \right. \tag{12}\]
## 3 The Proposed Scheme
### Communication Phase  
The SCC keeps a connection with the IoT main controller in the CH. When the connection is lost, the SCC will automatically send a notification to SG to solve the communication problem.  
The scenario of the communication phase is shown in Figure 4. The situation of a loss of connection from the SCC to the client's house, or if the IoT devices do not work, is detailed in the following steps:  
1. The SCC keeps communicating for the connection of the IMC in the client's house every minute (depending on the contract). If the IoT devices or the controller of the client's house does not respond, it means that the security mechanism is not working. SCC will trigger a lost connection event and record it.
2. The SCC staff check if it is a false alarm or not and confirm it. After confirmation, the SCC will search for available SGs who are nearby the client's house location automatically. The event is authenticated by the SCC, and a notification will be sent to the specific SG who is near the house.
3. The SG confirms and accepts the task, and sends a confirmation message to the SCC.
4. The SCC sends the event confirmation to the BCC and chains the confirmation record in the BCC.
5. The SG will go to the incident scene and solve the communication problem.
6. After the problem is solved, the security guard will report the problem with details and send the content to the SCC.
7. The SCC sends the record to the BCC and chains the report in the BCC.
8. The client gets the latest status notification from the SCC.
## 4 Discussion
SI, signal intensity; ASTM, average of SI per TE for each molar concentration; TE, echo time; SSI\({}^{1}\), saline signal intensity of the phantom study, SSI\({}^{2}\), saline signal intensity in the clinical phantom; FSI\({}^{2}\), fat signal intensity of the second phantom, SBP, SI of brain parenchyma; SC, SI of the intraventricular cerebrospinal fluid; MPSI, max peak signal intensity; EPSS, the enhancement percentage of SI for each mol by SBP; AEPSS, average EPSS; EPSC, enhancement percentage of SI for each mol by SC; AEPSC, average EPSC. decreasing GBCA concentrations after delivery into the body, minimizing the TE provided an overall increase in tumor contrast enhancement compared to healthy tissue. Based on the results of both the phantom and clinical studies, we found that both a faster and higher signal enhancement in CE-T1 by using a shorter TE. Moreover, a short TE in T1 imaging is useful for increasing contrast with brain parenchyma structures in the T1 effect examinations. In summary, in CE-T1 imaging, using a short TE facilitates differentiation of basic structures, provides faster contrast enhancement, and increases the extent of contrast enhancement. However, our study had a limitation that needs to be acknowledged. Although there are two methods for T1 effect: spin echo (SE) and turbo spin echo (TSE), the present study examined only the effects of changes in the TE parameter on T1 imaging using conventional SE, which has been usually used for T1 effect in the clinical field [2; 7; 8]. Therefore, further research with TSE should be performed in order to set a lower TE than that achieved with SE. Despite this limitation, our study findings are clinically relevant as we re-evaluated whether the TE parameter is being set appropriately in current, routine CE-T1 imaging and further demonstrated that TE adjustment can provide better contrast enhancement for diagnostic purposes.