esa-sceva/satcom-chunk-collection huggingface dataset:
# Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems
## 3 System Model
### Vehicle Speed State Model  
We only considered the one-lane scenario of the highway, where the vehicle is in a free-flow traffic date and its arrival obeys the Poisson distribution with parameter \(\lambda\). Therefore, we can conclude that the arrival time interval \(\Delta t\) of any two cars obeys a negative exponential distribution. Its probability density function is as follows:  
\[f(t)=\lambda e^{-\lambda t}(t>0) \tag{1}\]  
The probability distribution function is derived as  
\[F(t)=P(\Delta t\leq t)=1-e^{-\lambda t}(t>0) \tag{2}\]  
Therefore, the probability of at least one car arriving at this section in the \(\tau\) time slot is  
\[P_{p}=P(\Delta t\leq\tau)=1-e^{-\lambda\tau}(\tau>0) \tag{3}\]  
Figure 1: RSU packet scheduling for CVIS.  
Figure 2: Packet scheduling model.  
Then, the probability of no vehicles arriving in the \(\tau\) time slot is \(1-P_{v}\).  
In this paper, we considered \(v(t)\) as the speed of the vehicle when it arrives in the t-slot and assumed that the vehicle's speed in the road section does not change. In the free flow traffic state, the speed \(v(t)\) obeys the normal distribution with an average value of \(\overline{V}\) and a standard deviation of \(\sigma\). Its probability density function is  
\[f_{v}(v)=\frac{1}{\sigma\sqrt{2\pi}}e^{[-(\frac{v-\overline{V}}{\sigma\sqrt{2 }})^{2}]} \tag{4}\]  
If the speed limit interval of the highway is expressed as \([V_{\min},V_{\max}]\), then the truncation probability density function of the speed distribution is  
\[{f_{v}}^{*}(v)=\frac{2f_{v}(v)}{erf\left(\frac{V_{\max}-\overline{V}}{\sigma \sqrt{2}}\right)-erf\left(\frac{V_{\min}-\overline{V}}{\sigma\sqrt{2}}\right)} \tag{5}\]  
The continuous speed is divided into \(X+1\) speed states [18]. The state vector is expressed as \(V=[V_{1},\ldots,V_{X+1}]\), in which \(V_{1}=V_{\min}\) and \(V_{X}=V_{\max}\) represent the minimum and the maximum speed limits of the highway, respectively, and satisfy \(V_{x}>V_{x+1}(\mathrm{x}=1,2,\ldots,X)\). \(y_{n}(t)\) indicates the speed the vehicle reaches \(RSU_{n}\) in the t-slot. \(y_{n}(t)=x(1\leq x\leq M)\) indicates \(v(t)\in[V_{x+1},V_{x})\) and \(y_{n}(t)=X+1\) indicates that no vehicle will arrive at \(RSU_{n}\) in the t-slot.  
Let \(\delta_{x}\) represents the probability that the vehicle is in the speed state \(x\), and its probability density function expression is  
\[\delta_{x}=P\{y_{n}(t)=x\}=\begin{cases}P_{v}\int_{V_{x+1}}^{V_{x}}{f_{v}}^{* }(v)dv\,,&1\leq x\leq X\\ 1-P,&x=X+1\end{cases} \tag{6}\]
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN  
CR-LPWAN systems will be beneficial to many IoT applications and we discuss a number of these benefits in two categories. First, we discuss general technical benefits of CR to LPWAN, and then we discuss a few application areas that may benefit from CR-LPWAN systems. These are highlighted as follows:
## 3 Results and Discussion
### Changes in Microbial Population  
Results of the vertical change in microbial population after oxidation and biodegradation at various depths are presented (Figure 6, Supplementary Table S3). In general, the soil microbial population (CFU/g) decreased by one order of magnitude with soil pH decreasing after chemical oxidation (Table S4). This deleterious effect was minimal in the case of persulfate oxidation as the concentration of sodium persulfate (50 mN) used was low. With the reclamation of pH after applying bioaugmentation foam, an exponential increase in CFU by 4 to 5 orders was observed. Moreover, the variation in the number of microorganisms as a function of depth in the column without holes was more significant compared to the column with holes.  
High CFU counts after 4 weeks indicated that the supplemented microorganisms had multiplied and reduced the total petroleum hydrocarbons through biodegradation. The results demonstrate that the TPH degradation is often related to the total microbial population which, in turn, is correlated to the pH of the soil. At day 30, the microbial growth reached \(10^{7}\) (maximum at 5 cm depth) CFU/g soil, while the TPH was significantly reduced. The CFU counts in deeper soils were slightly lower than shallower soils because of the lower pH, insufficient nutrients, and lower oxygen levels at lower depths. The overall results of this study also suggest that the coupling of foam spray and vertical holes accelerated distribution of added microorganisms throughout diesel-contaminated soil.
## 2 Materials and Methods
### Experiment Design  
We compared our proposed MTLNFM model with several baselines, including the following:  
* Linear regression (LR): A basic model that calculates the first-order linear relationship between the input features and the target variable.
* XGBoost-raw: XGBoost [38], or Extreme Gradient Boosting, is a powerful ensemble learning algorithm that builds multiple decision trees sequentially. XGBoost-raw is XGBoost directly trained on the raw input features.
* XGBoost-embed: This version of XGBoost is trained on the embedded features, where the input features are transformed into a low-dimensional, dense representation.
* FM [34]: Factorization Machine extends linear models by creating second-order interactions between features. This approach adds the ability to capture interactions between pairs of features.  
All models, with the exception of XGBoost-raw, were trained on embedded input data. In addition to baseline comparisons, we investigated the performance differences between STL and MTL models, as well as the impact of varying the number of tasks involved in the MTL setting. For the experimental setup, the dataset was randomly split into training, validation, and test sets in an 8:1:1 ratio. An early stopping strategy was employed to prevent overfitting and halting training if validation performance did not improve for more than three consecutive epochs. To ensure a fair comparison across all models, we maintained consistent hyperparameters: the regularization strength was set to 1e-5, the embedding dimension was fixed at 30, and the batch size was 16. All models were trained using the Adam optimizer and the binary cross-entropy loss function, except for XGBoost, which was implemented via scikit-learn [39] with default settings. The codes were implemented with PyTorch 2.4 [40], and models were optimized with learning rates within the range of [0.1, 0.01, 0.001]. We conducted five random experimental runs to reduce errors by averaging the results. Due to the limited sample size, we employed a single hidden layer with a dimensionality of 64 in our MTL architecture to mitigate risks such as gradient vanishing. Model performance was assessed using four evaluation metrics: accuracy, precision, F1 score and Area under the Receiver Operating Characteristic Curve (AUROC).
## 5 Results and Analysis
### Mechanism of the Nonlinear Effect of Built Environment Factors on Ridership
#### 5.4.2 Accessibility  
Figure 9a-c show the partial dependence of reachability on total passenger flow and inbound and outbound passenger flow in the morning peak on weekdays, which shows the nonlinear effect of reachability on them.  
For total passenger flow and outbound passenger flow: When the accessibility time is before 25 min, the accessibility time has a negative correlation on passenger flow. By 25 min, the total passenger flow and outbound passenger flow become zero (as shown in Figure 9a,c). This indicates that the psychological upper limit of commuting time for passengers in the morning peak is about 25 min. Above this threshold, metro trips are abandoned due to the inability to meet on-time demand, or they are employed nearby or switch to other modes of transport.  
For inbound traffic: the reachable time starts at 32 min with a sharp drop in traffic, with a negative correlation until 45 min when traffic becomes zero (as shown in Figure 9b). This may be due to the fact that inbound traffic is mostly from suburban or peripheral residential areas, where passengers are more tolerant of long commuting distances, but 32 min becomes the upper limit of what is acceptable. By 45 min, passengers will choose to give up travelling to work by rail.  
Since the upper limit of commuting time for passengers in the morning peak is about 25 min, for total and outbound passenger flows, priority should be given to the development of a rapid transit system and the optimisation of route design to ensure that commuting times in key areas do not exceed this threshold, and to promote policies, such as proximity to employment, that reduce the need for long-distance commuting. For inbound passengers, who begin to show a markedly negative reaction to commute times of 32 min or more and abandon rail altogether at 45 min, the construction of transport hubs in suburban and peripheral residential areas can be strengthened in order to shorten the actual commute time and increase the competitiveness of rail.  
Figure 8: The nonlinear influence of office types on total ridership and inbound and outbound ridership. (**a**) On total ridership; (**b**) on inbound ridership; (**c**) on outbound ridership.
# Improving a Process-Based Model to Simulate Forest Carbon Allocation under Varied Stand Density
## 1 Introduction
However, few studies have used a process-based model with a dynamic carbon allocation scheme to test the growth response of forest management practices such as density regulation. The objective of this study was to adjust the carbon allocation module of an existing model, TRIPLEX-Management [18], in a different way that depends on light, water availability, and nitrogen to make it more flexible for simulating stand growth and the dynamics of planted forests with different planting densities. The performance of the modified model was tested using the stand characteristics of poplar (_Populus_ spp.) plantations with different densities and climatic conditions in northern Jiangsu Province. We also experimentally simulated the dynamic carbon allocation and stand growth between poplar plantations with diverse suggested planting densities in the region. We hypothesized that with an increase in stand density, the carbon allocated to the stems would decrease while that allocated to the leaves and roots would increase, resulting in a lower diameter at breast height (DBH).
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 4 Floods
### System Requirements and Constraints for Flood EW Systems  
Important system requirements that were highlighted by the reviewed literature are: (i) coverage in remote areas and difficult terrains, which might require to discard certain IoT solutions as they might not be suitable for said environments; (ii) energy consumption, especially under certain environmental or weather conditions (such as \"dark modes\" of operation when using a photo-voltaic energy supply) or when certain measurement requirements are to be met; and (iii) fault tolerance and the ability of the sensor network to keep working even if some nodes fail, because of low battery or harsh weather conditions.  
While the loss of nodes is taken into account in some reviewed articles and mesh solutions are proposed to avoid single points of failure, high fault tolerant systems require a large number of sensors to properly work, and most use cases do not take into account the effect of heavy rainfalls and extreme weather conditions on the communication channel, for example not planning for redundancy or fail-safe systems, as also noted in [23; 58]. Link budgets evaluations are important to determine if an area can be served properly by a service (for example, with areas with heavy foliage or distant, rural areas) or ad hoc network. Services that operate in ISM bands such as LoRaWAN can provide good coverage, but they can also bring some limits due to the shared radio resource. Worst case scenarios should also be evaluated in terms of battery life, when the weather can limit solar energy harvesting or when a higher sampling frequency is required during a measurement.
# Quantum Calculi--From Theory to Language Design
## 4 Foundational Quantum Programming Languages
### QML and the QIO-Monad  
Another seminal contribution in the definition of quantum functional calculi is QML, the typed quantum language for finite quantum computations we briefly compared with \(\lambda_{sv}\) in Section 2.3. QML has been developed in References [4; 39; 47; 48].  
QML permits to encode quantum algorithms easily. More precisely, a single term can encode a quantum circuit, that is, captures an algorithm of type \(Q^{k}\to Q^{k}\), where \(Q\) is the type of the qubits for a given \(k\in\mathsf{Nat}\). The syntax allows building expressions such as \(\alpha t\), where \(\alpha\) is an amplitude and \(t\) is a term or expressions like \(t+u\) where the symbol \"\(+\)\" represents the superposition of terms. However, superposition is controlled by a restrictive notion of \"orthogonality\" between terms, defined employing a notion of inner product between judgments [4]. Intuitively, \(t\bot u\) holds when \(t\) and \(u\) are \"distinguishable in some way\" [39]: in other words, one can derive the judgement \(\mathsf{true\_false}\) from \(t\bot u\) by means of orthogonality rules. Orthogonality of judgments is automatically inferred by static analysis of QML's terms [48].  
Quantum superposition can be combined with the conditional construct. The syntax includes both the if then else and the quantum conditional if\({}^{\circ}\) then else ; the quantum conditional if\({}^{\circ}\) then else is allowed when the values in the branches are orthogonal. For example, the following term represents the encoding of the Hadamard gate in QML: had \(x=\mathrm{if}^{\circ}x\) then \((\frac{1}{\sqrt{2}}\mathsf{false}+(-\frac{1}{\sqrt{2}})\mathsf{true})\) else \((\frac{1}{\sqrt{2}}\mathsf{false}+\frac{1}{\sqrt{2}}\mathsf{true})\). The denotational semantics of QML has been defined in terms of _superoperators_.  
Starting from QML and as a step toward higher-order quantum programming, [PERSON] et.al successively introduced the Quantum IO-monad [44]. The QIO-monad is a functional interface to quantum programming, implemented as a Haskell library and provides a constructive semantics for quantum programming. The main idea to Quantum IO- monad is to split reversible (i.e., unitary, purely quantum) and irreversible (i.e., probabilistic) computations and provides a reversible let operation, supports the use of ancillas (i.e., auxiliary qubits). Various central quantum procedures (e.g., the complete implementation of [PERSON]'s algorithm) are implemented.  
As pointed out in Reference [19], the QIO-monad represents the early step towards embedding quantum computation inside a functional host language, the key idea behind languages as Quipper and QWIRE. See also [PERSON]'s Ph.D. thesis, where the author embedded the Quantum IO-Monad inside Agda, a dependently-typed programming language, showing the usefulness of dependent types in the setting of quantum languages. Dependent types prevent some instances of qubit cloning and represent an important tool to address quantum data linearity (see the language QWIRE and qPCF below).
## 6 Proposed MAC Protocol for Wireless Healthcare Environments--WhMAC  
Considering the presented network architecture, there is the possibility of lowering the amount of unnecessary overhead and idle listening in order to achieve efficient wireless communications in health measurement applications. The idea is to use a very simple, but efficient, TDMA MAC strategy and use the MN for coordinating the synchronisation, wake-up signals and data transmission to the monitoring stations.  
We integrated the developed protocol described in [27] with the WUR in order to obtain a WUR-enabled wireless healthcare MAC protocol (WhMAC) for efficient stream data transfer and efficient infrequent monitoring for a WBAN network. Protocol [27] is an energy-efficient MAC protocol suitable for communication in a Wireless Body Area Network. The protocol takes advantage of the fixed nature of the Body Area Network to implement a TDMA strategy with very little communication overhead, long sleep times for the sensor transceivers and robustness to communication errors. Simulations have been done to determine this robustness. The protocol is implemented on the Analog Devices ADF7020 RF transceivers for which power modelling and estimations have been done. This model will be extended with the WUR to accurately predict the energy consumption.
## 3 Results and Discussion
### Development of a Mathematical Model for Flange-Forming Force
The discrepancy in the flange-forming force between these two instances amounts to 2.96%, as indicated in Table 11 and illustrated in Figure 9b. In the subsequent case, employing parameters (\(H0/D0=0.6\); \(S0/D0=0.2\); and \(\alpha=0^{\circ}\)), the simulated and experimental profiles reveal creases during the flange-forming process, as depicted in Figure 9c. The deviation in forming force between these profiles reaches 3.52%, as illustrated in Figure 9d. Finally, when utilizing the parameter set (\(H0/D0=0.4\); \(S0/D0=0.3\); and \(\alpha=20^{\circ}\)), corresponding to the largest punch angle among the cases, the simulated and experimental profiles in Figure 9e demonstrate a consistent geometry of the flange detail during the forming process for C45 billet tubes. The deviation in forming force between these profiles is the smallest, with a value of 1.42%, as depicted in Figure 9f. Additionally, a comparison of forming force results between the mathematical model and the experiment is presented in Table 11 and Figure 10. Notably, the largest deviation of 4.26% is observed for the mathematical model when compared with the experimental results. These findings provide robust evidence for the high dependability of the mathematical model in effectively characterizing the flange-forming force during the flange-forming process of C45 billet tubes, based on the pertinent geometric parameters of the procedure. Figure 8: Relationship between the flange-forming force and the forming process parameters: (**a**) fixed \(H0/D0=1.0\); (**b**) fixed \(S0/D0=0.2\); (**c**) fixed \(\alpha=5^{\circ}\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case No.** & **\(H0\)/D0** & **S0/D0** & \(\alpha\) & \begin{tabular}{c} \(P_{M}\) \\ **(ton)** \\ \end{tabular} & \
\begin{tabular}{c} \(P_{S}\) \\ **(ton)** \\ \end{tabular} &
\begin{tabular}{c} \(P_{E}\) \\ **(ton)** \\ \end{tabular} & \(\Delta P_{M}\) **(\%)** & \(\Delta P_{S}\) **(\%)** \\ \hline
17 & 1.0 & 0.25 & 10 & 17.62 & 17.4 & 16.9 & 4.26 & 2.96 \\ \hline
18 & 0.6 & 0.2 & 0 & 14.58 & 14.7 & 14.2 & 2.68 & 3.52 \\ \hline
19 & 0.4 & 0.3 & 20 & 34.12 & 35.8 & 35.3 & 3.34 & 1.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of the flange-forming force during the flange forming of C45 billet tubes, determined from both mathematical and experimental models. Figure 9: Comparison between the deformed shapes and corresponding flange-forming force for different case No. 17 **(a,b)**, No. 18 **(c,d)** and No. 19 **(e,f)**.
# Navigation of Autonomous Tug via Evolutionary Algorithms with Radar Plot Fitness Evaluation
## 1 Introduction
Among them, we can distinguish ant colony methods (ACO), which imitate the behavior of ant colonies in search of the optimal path [16; 17; 18] and genetic algorithms (GA), where potential routes are encoded in the form of chromosomes undergoing evolution [19; 20; 21; 22; 23]. Artificial neural networks (ANNs) are computational models inspired by the structure and operation of the human brain. A neural network can learn to determine an optimal route based on sample training data and then generalize this knowledge to new cases [24; 25; 26; 27]. Modifications of classical graph algorithms involves algorithms such as Dijkstra or A\({}^{\star}\), which were were originally used to find the shortest path in a graph. They can be adapted to the problem of ship route planning by appropriately representing the environment and modifying the cost function [5; 28; 29; 30; 31]. The increase in computer performance has allowed the use of computationally intensive reinforced learning methods, which are also suitable for calculating passage routes [32; 33; 34; 35; 36; 37]. Hybrid methods combine the advantages of different computational approaches. For example, a genetic algorithm can be used for global exploration of the solution space, and then its results can be improved by local optimization using potential fields. Such a combination allows for effective searching of the complex space of possible routes. This article presents a method for determining an energy-efficient passage route for an autonomous tugboat, using an evolutionary algorithm with a radar method of fitness assessment. The aim of the algorithm is to determine a passage route that minimizes energy consumption while maintaining the safety of navigation.
## 3 Model Evaluation and Implementation
### Mean Absolute Percentage Error  
The MAPE, a commonly used error metric for time series models, was used to evaluate the accuracy of the proposed model's predictions. The formula for MAPE is as follows:  
\[MAPE=\frac{100}{n}\sum_{i=1}^{n}\frac{Actual_{i}-\ Forecast_{i}}{Actual_{i}} \tag{1}\]  
where n is the total number of predictions, \(Actual_{i}\) represents the actual value at time \(i\), and \(Forecast_{i}\) represents the forecasted value at time \(i\).
## 3 Results and Discussion
### The Apparent Density and Specific Strength of AN-LAC  
Under the same volume, concrete with a better aggregate particle gradation has lower internal porosity and excellent packing compactness, resulting in a higher apparent density. As shown in Figure 8a, AN-LAC-1 had the highest apparent density among all groups. However, the wet and dry apparent densities of AN-LAC-3 were the lowest at 2155 kg/m\({}^{3}\) and 1883 kg/m\({}^{3}\), respectively. In all groups, the apparent density of AN-LAC exhibited the same increasing trend when the average particle size decreased, indicating that the incorporation of NSLA with a small particle size could improve the aggregate particle gradation and structural compactness of concrete. Moreover, the water absorption of concrete increased with an increase in the average particle size. This was due to the fact that NSLA with a larger particle size possessed a higher water saturation capacity [15], which led to more water loss of concrete after drying to remove water. Thus, the water absorption of AN-LAC with a large particle size was higher.  
Figure 7: The test results and analysis of the four-point bending test: (**a**) the flexural strength of AN-LAC; (**b**) the relationship between flexural strength and compressive strength; CEB-FIP Model Code 1990 [36].  
In addition, specific strength is the ratio of compressive strength at 28 days to the dry apparent density of concrete, which can reflect the characteristics of light weight and high strength of LWAC. The relationship between specific strength and average particle size is shown in Figure 8b. It is clear that the specific strength of AN-LAC-4 reached the maximum value of 29.0 kN\(\cdot\)m/kg. According to [PERSON] et al. [42], the highest specific strength of lytag and shale ceramics concrete at 28 d was 21.3 kN\(\cdot\)m/kg and 21.5 kN\(\cdot\)m/kg, respectively. Thus, the performance of the light weight and high strength of AN-LAC was better than that of sintered ceramics concrete. In addition, with the decrease in the average particle size of NSLA, the specific strength of AN-LAC increased, indicating that the property of AN-LAC with a small particle size was excellent. Equations (9) and (10) were used to fit the experimental data and the correlation was great:  
\[y =-0.43x+28.88 \tag{9}\] \[y =-1.02x+35.82 \tag{10}\]