esa-sceva/satcom-chunk-collection huggingface dataset:
# Agricultural Waste-Based Biochar for Agronomic Applications
## 3 Biochar as a Soil Amendment
### Impacts on Soil Microbiology and Enzymes
Since biochar application stimulates changes in soil structure and composition, the production of enzymes by microbial communities will also change; the production of some enzymes will increase, while others will decrease [125]. [PERSON] and [PERSON] [140] claimed that biochar amendments in mangrove sediments induced an increase in enzymes such as phenol oxidase and \(\beta\)-glucosidase, while other enzymes such as peroxidase, N-acetyl-glucosamine, and acid phosphatase decreased. The influence of biochar on the activity of soil enzymes can vary. This depends on the soil type and the presence of substances that interact with biochar and can thus make it inaccessible to soil enzymes [141]. As compared to activated carbon exhibiting much higher surface area, biochar amendments may preserve most of enzyme'sactivity when adsorbed on its surface due to substrate availability [125]. The effect of biochar application on soil enzyme composition has been proven; however, the mechanisms controlling biochar interactions with enzymes and the response of each enzyme in soil to biochar application still have to be clarified in further investigations.
## 2 Materials and Methods
### Trunk Numerical Model  
The trunk biomechanical model is a lumped type numerical model that enables calculation of the nonlinear static response of the torso, to an input set of applied loads, at a given posture of the body. The model (Figure 2) includes the spine, the rib cage, and 174 muscles [21; 22]. The vertebrae, the intervertebral discs, the sternum, and the ribs are represented by beam elements and the ligaments are modeled as linear springs. Each muscle is represented by its tensile force and stiffness while taking into account the unilateral behavior of the muscle-tendon complex [10]. The maximum active force of a muscle is evaluated proportionally to its largest physiological cross-sectional area (PCSA).  
The tone is taken proportional to the maximum active muscle force. The calculation of the model trunk stiffness, in the specified displacement direction, proceeds as follows [2; 10]. An equilibrium problem (Equation (1)) is run for the trunk subject to a specified horizontal force, applied at the T10 level, with the muscle stiffnesses held constant. A trunk stiffness can be calculated (Equation (2)) as the ratio of the applied horizontal force to the induced displacement at T10. Often, the muscle activations are assumed to remain constant, at the tone level, prior to the induction of the paraspinal reflex. However, the actual muscle active stiffnesses may decrease during the displacement perturbation, due to excessive shortening or shortening velocity. Therefore, accurate calculation of trunk stiffness should take those changes in muscle stiffness into account.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Direction \#** & **1** & **2** & **3** & **4** & **5** \\ \hline Trunk stiffness (N/mm) & 1.30 & 1.46 & 2.11 & 1.90 & 1.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average experimental trunk stiffness [18] reduced to five directions.  
Figure 1: Directions (from 1 to 5) of displacement perturbations. The five among the eight directions retained from [18], considering sagittal symmetry.
## 3 Performance Analysis
### Acr  
Only when the conditions \(\Gamma\) and \(\Lambda\) are met at the same time, the covert rate can be achieved, which is defined as \(R=\log_{2}(1+\gamma)\). With a fixed transmit power \(P_{C}\), taking \(\theta\) into account, the ACR of this system can be obtained as:  
\[R_{AC}=\theta\int_{u}^{\infty}f_{\left|h_{SR_{m}}\right|^{2}}(x)dx\int_{v}^{ \infty}\log_{2}\left(1+\frac{P_{C}y}{\sigma_{U}l^{2}}\right)f_{\left|h_{R_{m} }\right|^{2}}(y)dy, \tag{36}\]  
where \(u=\frac{\gamma_{th}\sigma_{R_{m}}}{P_{S}}\), \(v=\frac{\gamma_{th}\sigma_{U}l^{2}}{P_{M}-(\gamma_{th}+1)P_{C}}\).  
According to Equations (23) and (31) and variable \(Y\) substitution, Equation (36) can be derived as:  
\[\begin{array}{l}R_{AC}=\theta\left[1-F_{\left|h_{SR_{m}}\right|^{2}}(u) \right]\frac{N}{ln2}\sum\limits_{k=0}^{N-1}\binom{N-1}{k}(-1)^{k}e^{-(k+1)v} \\ \times\frac{\sigma_{U}l^{2}}{P_{C}}\int_{0}^{\infty}In\left(Y+\frac{P_{C}y}{ \sigma_{U}l^{2}}+1\right)\exp\left(-\frac{(k+1)v_{l}l^{2}}{P_{C}}Y\right)dY \end{array}, \tag{37}\]  
where \(Y=\frac{P_{C}(y-v)}{\sigma_{U}l^{2}}\).  
Further, with the help of [16], the ACR can be re-written as:  
\[\begin{array}{l}R_{AC}=\theta\alpha_{SR_{m}}\sum\limits_{n=0}^{m_{SR_{m}}-1 }\sum\limits_{k=0}^{n}\frac{\left(-\delta_{SR_{m}}\right)^{n}\left(1-m_{SR_{m} }\right)_{n}}{\left(k!\right)\left(\beta_{SR_{m}}-\delta_{SR_{m}}\right)^{n-k+ 1}}u^{k}e^{-\left(\beta_{SR_{m}}-\delta_{SR_{m}}\right)u}\\ \times\frac{N}{ln2}\sum\limits_{k=0}^{N-1}\left(\begin{array}{c}N-1\\ k\end{array}\right)\frac{\left(-1\right)^{k}\exp\left(-\left(k+1\right)v \right)}{k+1}\left[In\left(1+\frac{P_{C}y}{\sigma_{U}l^{2}}\right)-\exp( \omega)Ei(-\omega)\right]\end{array}. \tag{38}\]  
where \(\omega=(k+1)\left(v+\frac{\sigma_{U}l}{P_{C}}\right)\) and \(Ei(x)=\int_{-\infty}^{x}\frac{e^{t}}{t}dt\) is the exponential integral function.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 6 Experimental Simulation
### I-DEEC Simulation Results
#### 6.2.2 Comparison of Data Transmission  
In Figure 12, it can be found that the throughput by I-DEEC is much higher than the other two protocols. That is because I-DEEC has much higher energy efficiency than others. Therefore, with the limited energy, the network by our algorithm can send more data. At the same time, because the DEEC just considers the residual energy, the throughput by DEEC is minimum. For example, when the round is 2500, the throughput by DEEC, IEE-LEACH, and I-DEEC is, \(0.3\times 10^{5}\), \(0.8\times 10^{5}\) and \(2.2\times 10^{5}\), respectively.
# Deling with Uncertainty in the MRCPSP/Max Using Discrete Differential Evolution and Entropy
## 3 Methods
### Computational Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & \multirow{2}{*}{**Measure**} & \multicolumn{2}{c}{**MM30**} & \multicolumn{2}{c}{**MM50**} & \multicolumn{2}{c}{**MM100**} \\ \cline{3-8}  & & **ABC** & **DDE** & **ABC** & **DDE** & **ABC** & **DDE** \\ \hline \multirow{3}{*}{S1} & Avg. Dev. & 0.00176 & 0.00580 & 0.04571 & 0.03104 & 0.04424 & 0.04031 \\  & Std. Dev. & 0.00664 & 0.01952 & 0.03946 & 0.06002 & 0.03257 & 0.04090 \\  & Avg. RM. & 102.75556 & 116.62593 & 116.62593 & 117.31481 & 117.39630 & 115.85185 \\ \hline \multirow{3}{*}{S2} & Avg. Dev. & 0.09690 & 0.09524 & 0.10132 & 0.09640 & 0.08497 & 0.07570 \\  & Std. Dev. & 0.05793 & 0.08394 & 0.05917 & 0.08180 & 0.04307 & 0.05348 \\  & Avg. RM. & 132.72593 & 131.86296 & 133.81481 & 136.22593 & 137.12963 & 134.46670 \\ \hline \multirow{3}{*}{S3} & Avg. Dev. & 0.05041 & 0.02491 & 0.05387 & 0.03711 & 0.04373 & 0.04259 \\  & Std. Dev. & 0.04794 & 0.04856 & 0.05220 & 0.05615 & 0.04067 & 0.04371 \\ \cline{1-1}  & Avg. RM. & 100.62593 & 123.80370 & 124.45926 & 125.99259 & 127.43704 & 124.52593 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary results for the MRCPSP/Max benchmark evaluations for each algorithm. Figure 3: Avg dev vs. BKO CI for each algorithm in Stage 1 (MRCPSP/max). Figure 5: Avg dev vs. BKO CI for each algorithm in Stage 3 (MRCPSP/max). Figure 6: Robustness measure CI for each algorithm and stage for MM30 instances. Figure 7: Robustness measure CI for each algorithm and stage for MM50 instances. Compared to the previously obtained results (columns marked with ABC), the most significant difference observed for the obtained solutions is their stability. Though the standard deviation of both algorithms increases with complexity, this increase is lower and slower when using DDE. Furthermore, the methodology remains stable and yields schedules with robust makespans near the best-known optima. In summary, better implementations of optimization algorithms can further improve the performance of the proposed methodology.
## 5 Experiments
### Evaluation Metrics  
In the study of OSV, three common evaluation metrics were employed to assess the performance of the proposed method: False Rejection Rate (FRR), False Acceptance Rate (FAR), and Accuracy (ACC). The use of these evaluation indicators can help researchers carry out reasonable algorithm design and selection. Among them, the FAR refers to the probability of falsely detecting a genuine signature as a forged signature, that is, the ratio of failing to successfully identify a genuine signature. The FRR refers to the probability of mistaking a forged signature as a genuine signature, that is, the rate of failing to successfully identify a forged signature. The accuracy rate is one of the indicators for the comprehensive evaluation of the performance of the multi-language OSV algorithm. It refers to the ratio of the correct verification of genuine signatures and forged signatures after all the test samples are verified. Therefore, a higher accuracy indicates that the proposed algorithm can better verify the signature, demonstrating stronger discriminability and better processing capabilities.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Copper Nickel Oxide Films  
The Cu\({}_{x}\)Ni\({}_{x-1}\)O(x = 0, 0.25, 0.5, 0.75, 1) nanostructured films were deposited on amorphous glass slides [160]. The band gap (3.34 to 2.01 eV) was reduced, and smoother films could be observed with the increase of copper (Cu) concentration during the experiment.
# Effective IoT Congestion Control Algorithm
## 2 Related Work
On such devices, a special TCP/IP stack called micro-IP (uIP) sets the sending window to one segment size by default. Congestion control in such a scenario relies heavily on careful management of the RTO. In [20], the author proposes an enhanced scheme for RTO to manage the problem of large RTT variation caused by certain systems. By using the idea of weak RTT estimate from CoCoA [21], this technique changes the RTO so that it is variable, rather than the fixed RTO set by the original TCP uIP. This kind of congestion control is limited to very small buffers, less than or equal to one maximum segment size. In [22], the authors propose a new technique to reduce the delay in multipath TCP (MPTCP) by reducing the number of transmissions using an Opportunistic Routing (OR) technique. The OR routing model is implemented to increase the throughput and reliability of wireless networks via the use of the broadcasting method. The authors compare the proposed scheme with other MPTCP algorithms. Though this scheme may be applicable for large memory devices such as smartphones, smartwatches and tablets due to their heterogeneous interfaces, it may not be applicable for devices with limited buffers and single connections deployed for IoT, such as esp32 [23] and esp8266 [24]. The author in [25] analyzed and designed QoS-aware personalized privacy for MPTCP for use in Industrial IoT (IIoT) to optimize the tradeoff between efficiency and privacy protection. IIoT data may be vulnerable to attack and requires more attention when MPTCP is the use case. A second group of researchers have taken a novel approach and employed state-of-the-art controllers, such as fuzzy controllers, to fine-tune and optimize the congestion control technique. Fuzzy controllers were used by [PERSON] et al. [26] to improve the TCP Protocol in a mobile network. Other networks, such as Software Defined Networks (SDN), also impact congestion. Researchers have used the Neural Network approach to solve congestion problems in 5G communication [27]. However, examining these forms of controllers and networks is beyond the scope of our study.
## 4 Experiments and Analysis  
### Dataset Introduction  
To assess the performance of PSANet in segmenting building areas from SAR images, we conducted experiments using the SARBuD 1.0 dataset produced by the team of the Chinese Academy of Sciences, which uses GF-3 SAR images as the data source and covers the land area of 20 provinces, cities, and autonomous regions in China. The dataset contains 60,000 sample images, each having a size of 256 \(\times\) 256 pixels and a spatial resolution of 10 m. Considering the massive size of the dataset, direct use will consume a significant amount of computing resources and time. In addition, directly dividing the dataset into training and validation sets cannot accurately evaluate the model's generalization ability in different geographical regions. Therefore, we selected Anhui Province and Jiangsu Province from the SARBuD 1.0 dataset for experiments, considering that these two regions have different geographical characteristics and significant differences in sample size, pixel spacing, and incident angle. Specifically, compared to Anhui Province, Jiangsu Province has a larger sample size, smaller pixel spacing, and a smaller incidence angle. These differences help in evaluating the model's effectiveness under various conditions.  
Anhui Province comprises 1735 images collected between February and March 2019 in Anhui, China. We divided the dataset into two subsets: 80% for training and 20% for testing. The sample images are shown in Figure 6a.  
Jiangsu Province comprises 3451 images collected between January and May 2019 in Jiangsu, China. Similarly, the dataset was divided into two subsets: 80% for training and 20% for testing. The sample images are shown in Figure 6b.
# Low-Light Photodetectors for Fluorescence Microscopy
## 3 Single Point Detectors
### Photomultiplier Tube (PMT)  
Photomultiplier tubes (PMTs) are the most widely used point detectors in fluorescence microscopy. A PMT is a vacuum tube that contains a photocathode, focusing electrodes, an electron multiplier (dynodes), and an anode (Figure 4) [23]. Incident photons are absorbed by the photocathode, which ejects primary electrons (~3 eV). The electrons are accelerated by a high voltage to hit a series of dynodes. Then, additional electrons (5-10 electrons) are ejected and exponentially amplified. The electron current is then detected by an external electrical circuit. Typical PMTs have 8-10 dynodes with a cathode-to-anode voltage gap of ~1 kV and current gain of 10\({}^{6}\) to 10\({}^{7}\).  
Figure 3: Spectral response of photocathodes.  
The main photocathodes used in PMTs are bi-alkali, with spectral response peaks around 400 nm and range of up to 700 nm. The advent of the GaAsP photocathode with its spectral response peak around 500 nm invigorated the LSM market because its spectral response ranges from visible light (500-700 nm) to near infrared light (900 nm). Laser scanning microscopes with a GaAsP photocathode were released to the market in the late 2000s. Current laser scanning microscopes incorporate PMTs with these multi-alkali photocathodes.  
PMTs have been the leading low-light point photodetector for some time and, thus, a large number of power supplies and signal processing circuits for them are available. However, troubles can arise from unwanted noise generated by residual gas molecules (ion feedback), and large variation in responsivity and gain caused by difficulty in controlling metal evaporation to produce photocathodes and secondary electron surfaces. In addition, single photons can be detected with PMTs, but discrimination of single versus multiple photons is difficult.
## 2 Literature Review
### The Maximal Accessibility Equality Model  
Aiming to address the equality issue, [PERSON] and [PERSON] [28] initiated a novel location-allocation model, termed the \"maximal accessibility equality\" (MAE) model. The MAE model quantifies the equality dimension of facilities' spatial configuration as the sum of squares of differences in the accessibility to different facilities. The optimal configuration would minimize the disparity in the accessibility to facilities [28]. The objective functionof this problem represents how spatial equality is understood and quantified. Solving the problem thus addresses the policy concerns over equality in public services. The MAE model can be expressed and solved as a quadratic programming problem.  
[PERSON] et al. [29] applied the maximal equality model to analyze optimal configuration of residential facilities and introduced the particle swarm optimization heuristic algorithm to solve the model. Another study tried to extend it by selecting newly added facility locations rather than reallocating resources at existing or given locations [43]. Two studies further introduced a two-step procedure, with the first step to optimize locations of facilities, while the second step to optimize the respective sizes of the facilities [30,44]. [PERSON] et al. [31] incorporated a random allocation mechanism into the MAE model to optimize educational opportunities. To date, however, few have paid attention to the equality optimization of hierarchical facilities. To achieve this goal, the MAE model needs to be extended to account for hierarchical nature of facilities.  
Note that a few recent studies have made efforts to model the spatial accessibility to hierarchical healthcare facilities [32, 33, 34]. These studies adapted the spatial accessibility measurements, e.g., the two-step floating catchment area (2 SFCA) method, in order to account for the hierarchical characteristics of facilities, including variable service scopes, different distance frictions, and different transport modes for facilities at various levels. Although these studies fail to improve and optimize the accessibility to hierarchical facilities, the above advancements in modelling accessibility to hierarchical facilities can help to develop a hierarchical version of MAE model. The current study makes further efforts to combine the measurement of accessibility to hierarchical facilities and the MAE model that optimizes the equality of accessibility.
## 2 Related Work
### Deep Learning-Based DR Grading  
Deep learning has shown promising results in the field of diabetic retinopathy grading. The use of deep learning can help improve the accuracy and efficiency of DR grading, which is critical for the early detection and timely treatment of DR. For example, [PERSON] et al. [38] proposed a deep learning system for the automated grading of DR using a large dataset of retinal images. It presented that the deep learning algorithms had high sensitivity and specificity for detecting diabetic retinopathy and macular edema in retinal fundus photographs. [PERSON] et al. [39] introduced a deep learning system for the automated grading of DR using a multi-level approach. This technology offers the potential to increase the efficiency and accessibility of DR screening programs. [PERSON] et al. [40] proposed a Vision Transformer-based method to recognize the grade of diabetic retinopathy, highlighting that an attention mechanism based on a Vision Transformer model is promising for the diabetic retinopathy grade recognition task. Achieving interesting early results using only a few colour features, [PERSON] et al. [41] introduced a novel Gaussian-sampling approach based on a multiple instance learning (MIL) framework which enables ophthalmologists to assess the reliability of the system's decision. Building upon this idea, [PERSON] et al. [42] addressed the multiple instance classification problem in OCT images using MIL techniques and achieved significant success. This is particularly effective because the morphological changes induced by diabetic macular edema (DME) are sparsely distributed in the B-scan images of the OCT volume. Additionally, OCT data are commonly labeled at a coarse level, making MIL an appropriate approach. [PERSON] et al. [43] presented preliminary numerical results obtained from the classification of healthy eye fundus images versus those with severe diabetic retinopathy using the MIL algorithm. [PERSON] et al. [44] tackled the challenge of classifying referable diabetic retinopathy (rDR) and segmenting lesions using self-supervised equivariant learning and attention-based MIL.
## 6 Simulation Results  
As mentioned in previous sections, during the fault condition, the converter's current reaches such a high value within a short time to cause the breakage of converters. Therefore, the application of SSCB is necessary to maintain the system's safety. Different simulation scenarios, such as load side and distribution feeder faults, are carried out to verify the performance of the proposed topology. In the first scenario at \(t=1.5\) s, a pole-to-ground fault (\(F_{1}\)) is initiated at the load side in area A. In the second scenario, a pole-to-ground fault is applied in area B at 1.7 s. The third DC fault is applied on the feeder, which connects area A and B at time 2 s. In regard to the main objective for this study, which is fast fault interruption, the operational safety of the rest of the system is maintained and keeps the system components in an acceptable range. As shown in Figures 6-11, during the three fault scenarios, the current in each case starts to increase exponentially, and is only limited by the DC line resistance (\(R_{Line}\)). When the fault current reaches the positive or negative threshold values, the breaker interrupts the fault current within a few \(\upmu\)s to prevent system failure and maintain a stable power for the load demands. Additionally, Figure 10 shows the DC bus voltage during \(F_{2}\), where as soon as the bus voltage decreases more than the threshold value, the nearest CB to the fault in area B suddenly disconnects the fault branch and the bus voltage returns to its normal value. The simulation results for the current, voltage waveforms, and State of Charge (SoC) of the battery during the fault conditions are depicted in the following figures.  
Figure 5: SSCB circuit diagram and the fault current control system.  
Figure 8: Fault current during DC short circuit \(F_{1}\) and \(F_{2}\).  
Figure 6: The current flow from area A to B during \(F_{1}\).  
Figure 7: State of charge of batteries in three areas during \(F_{1}\).  
Figure 9: Current flow from B to A during \(F_{2}\).
# Review of the Research Progress in Combat Simulation Software
## 3 Simulation Engine and Simulation Framework
### Simulation Framework
#### 3.2.4 E-Cargo  
There are many challenging requirements in combat simulation software, e.g., trust establishment among soldiers and commanders, highly autonomous and well-equipped individual soldiers, and situation awareness in the simulation systems. E-CARGO (Environments--classes, agents, roles, groups, and objects) [7] is an abstract model providing a framework for role-based collaboration (RBC) [39] that can address the aforementioned requirements. E-CARGO can help analyze, design, and simulate complex systems by formalizing each of them to a nine-tuple: \(<C,O,A,M,R,E,G,H,s_{0}>\), whose elements correspond to classes, objects, agents, messages, roles, environments, groups, human users, and the system's initial state, respectively.  
* The first-class components in E-CARGO are classes, objects, groups, agents, environments, and roles. They are the fundamental structures and depict the constructions or compositions of a complex system.
* The second-class components include messages, the initial state, and human users, which reveal the complexity of systems. For instance, messages reflect the numerous ways for the components to communicate, interact, coordinate, and cooperate.  
E-CARGO and RBC are a good fit for simulating complex systems and have been utilized in various scenarios [7; 40; 41]. For example, [PERSON] and [PERSON] [42] investigated the team management problem with the E-CARGO framework and revealed interesting conclusions that could help decision makers understand the complexity of the assignment and choose a pertinent path when conducting team management.