esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 4 Research Classification
### Electrochemical Energy Storage System
Moreover, it can be used for small-scale systems, such as microgrids [91]. An energy management system can help industrial production companies plan and use energy wisely while expanding production, reducing energy consumption per unit of product, improving economic efficiency, and reducing CO\({}_{2}\) emissions [92]. It has the following five roles: monitoring, analysis, targets, control, and interaction. This also shows the basic technologies needed for an energy management system, including a control system responsible for data collection and monitoring systems, as well as smart grids and smart meters that can collect detailed information and help users and generation communicate in both directions. Energy storage converters, also known as the bi-directional energy storage inverters PCS, are used in grid-connected energy storage and micro-grid energy storage, and, in other AC coupling energy storage systems, connecting the battery and the grid (or load) is a device to achieve bi-directional conversion of electrical energy. It can reverse the DC power of the battery into AC power and transmit it to the grid or to the AC load; it can also rectify the AC power of the grid into DC power and charge the battery [93]. An energy storage converter mainly has two working modes: grid connected and off-grid. The grid-connected mode realizes the two-way energy conversion between the battery bank and the grid. It has the characteristics of the grid-connected inverter, such as anti-islanding, automatic tracking of grid voltage phase and frequency, low voltage ride-through, and so on. When the power quality is not good, it feeds or absorbs active power to the grid and provides reactive power compensation [94]. The off-grid mode, also known as isolated network operation, means that the PCS can be disconnected from the main grid, according to the actual needs and under the condition of meeting the set requirements, providing the local part of the load with AC power to meet the power quality requirements of the grid. In a micro-grid system composed of multiple energy sources, the energy storage converter is the core equipment because renewable energy sources such as photovoltaic and wind power are volatile, the load is also volatile, and the fuel generator can generate power but not absorb it [95]. If there are only PV, wind, and fuel generators in the system, the system operation may be unbalanced, and the system may fail when the power of renewable energy is greater than the load power; thus, it is difficult for the PV grid-connected inverter to run in parallel with the fuel generator, the energy storage converter can absorb energy and also emit energy, and the response speed is fast, which plays a balancing role in the system [96].
## 5 Conclusions  
Based on the long-term analysis from this experimental study over the subtropical forest region of the Arunachal Pradesh state of India, it was observed that the EVI was the most sensitive parameter to the PDSI in a long-term observation based on promising correlation, low uncertainty, and low error, where most of the existing studies on drought severity showed a high sensitivity of GPP and NDVI in determining drought conditions. It is, therefore, stated that the EVI is the simple, effective, and most complementary indicator (among the vegetation parameters) for assessing the PDSI over forest regions of a tropical ecosystem. Besides, the EVI can also be used as a promising tool for the effective evaluation of long-term drought impacts on a forest ecosystem that indicates actual water deficit-induced stress conditions. In other words, the EVI can also act as a direct proxy of the actual drought conditions of the region. Similarly, after the EVI, the NDVI can be considered as the next promising sensitive indicator that is highly responsive to the PDSI. In future studies, more advanced vegetation parameters like Sun-induced fluorescence or SIF [25] can be added for PDSI-based drought monitoring. The authors assumed that this study will support sustainable forest management practices and drought monitoring under climate change scenarios for tropical ecosystems.  
Conceptualization, S.B.; methodology, S.B. and S.G.; software, S.B., S.G., and D.A.C.S.; validation, S.B., S.G., and D.A.C.S.; formal analysis, S.B. and S.G.; investigation, S.B., S.G., and D.A.C.S.; resources, S.B. and S.G.; data curation, S.B. and S.G.; writing\(-\)original draft preparation, S.G.; writing\(-\)review and editing, S.B. and D.A.C.S.; visualization, S.B. and S.G.; and project administration, S.B. All authors have read and agreed to the published version of the manuscript.  
Analyses of the data were supported by the Polish National Research Centre (NCN) within the Project Sun Induced florescence and photosynthesis of the peatland vegetation response to stress caused by water deficits and increased temperature under conditions of climate manipulation experiment (No. 2016/21/B/ST10/02271).  
Derived data supporting the findings of this study are available from the corresponding author [SG/SB] on request.  
We sincerely appreciate the anonymous reviewers and members of the editorial team for their valuable comments and suggestions.  
The authors declare no conflicts of interest.
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.5 Random Forest (RF)  
Random Forest (RF) amalgamates the outcomes of multiple decision trees to produce a consolidated result, thereby mitigating overfitting and rendering the model less susceptible to noise and outliers within the data [34]. In the context of classification tasks, the output of the random forest corresponds to the class most frequently selected across the constituent trees. Alternatively, for regression tasks, the returned prediction is typically the mean or average prediction derived from the individual trees.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 2 Related Work
### Noise-Adding Mechanism in Differential Privacy  
Because differential privacy only allows access to the database through counting and summation, random noise needs to be added to each query result to protect data privacy. Only in this way can the attacker be prevented from guessing the existence of the target in the database and obtaining specific information from the set of query results. Next, we will introduce the common noise mechanism in differential privacy.  
(Laplace mechanism) If we send a series of query requests to database \(D\): Query = \(q_{1}\), \(q_{2}\), \(q_{3}\),..., \(q_{n}\), the database will produce a real answer \(q(D)\). Differential privacy protection adds noise to the numerical results to obtain a series of results with the same probability distribution [31; 32]. The Laplace mechanism solves this problem well and needs to provide a parameter \(\lambda\). It is calculated as follows:  
\[\lambda=\frac{QS}{\varepsilon} \tag{4}\]  
The Laplace distribution has a mean of 0 and a variance of \(\lambda^{2}\), and it satisfies \(\varepsilon\)-differential privacy. The noise added to the query set result \(q(D)\) during the query is \(\eta\sim Laplace(0,\lambda)\). Therefore, the returned result is:  
\[A(D)=q(D)+\eta \tag{5}\]  
Clearly, the smaller the privacy budget, the greater the noise when adding to the original data, thereby enhancing the level of protection, but inevitably compromising the data's availability. To cater to the personalized privacy protection requirements of users, this paper categorizes protection levels by assigning different privacy budgets.
# Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation
## 4 False Data Injection Attacks Detection Modelling and Methods
### Mathematical Modelling of False Data Injection Attacks Detection  
An attacker can inject a malicious attack in a vectorial form with perfect knowledge of the Jacobian matrix and FDIA calculation described previously. So, the mathematical formulation of FDIA detection should be understood clearly. It is aimed to make sure that the FDIA vector elements are the same in the sense of energy, so the comparison stage and simulations are current and significant.  
The classic FDIA detector \(J(\hat{x})\) is created first in [132] with hypothesis test in to detect FDIA, \(H_{0}\) and \(H_{1}\). \(H_{0}\) is the null hypothesis, where the measurement is valid; and \(H_{1}\) is the alternative hypothesis, where the measurement is under attack. So, \(J(\hat{x})\) as follows:  
\[r^{T}Wr_{H_{1}}^{H_{0}}\ \gtrless\ \gamma \tag{5}\]  
Meanwhile in [133], if \(\varepsilon_{0}=Trace(\Sigma_{x}-KH\Sigma_{x})\) offers minimum mean square error (MMSE), when it is in the asset of the attack would be \(\varepsilon_{0}+\left\|Ka\right\|_{2}^{2}\) which refers MMSE can be controlled by energy. In the MMSE, an optimum attack is produced with the minimum residue to limit the probability of detection that can be formed: \(min\|Ga\|_{2}^{2}\) subject to \(\left\|Ka\right\|_{2}^{2}\ \geq\) C where \(G\triangleq I-HK\) and C is attack's minimum energy value. The sparsity pattern presented in [134] and assumed the full measurement assumption.
## 3 Results and Discussion
The 0.5% Nd-doped crystal showed a response with retaining the linearity from 0.06 to 60 Gy/h with retaining the linearity whereas that of 0.1 and 1% Nd-doped crystals were 0.3-60 Gy/h. In the conventional model [48], the scintillation light yields (\(LY\)s) depend on the \(QY\); thus, the 0.5%Nd-doped crystal is considered to show the highest \(LY\) among the present crystals, although the quantitative evaluation of the \(LY\)s in NIR ranges could not be done owing to the decay times with several hundred \(\upmu\)s of Nd\({}^{3+}\). The difference in \(LY\) would result in the difference in dynamic ranges among the crystals. In the past study, some of the Nd-doped materials were evaluated as NIR-scintillators by using the same detector form: SrY\({}_{2}\)O  
Figure 3.0 X-ray-induced scintillation decay curves of Nd-doped CaWO\({}_{4}\) monitored (**a**) UV–visible ranges and (**b**) visible–NIR ranges. (0.06-60 Gy/h [49]), Bi\({}_{4}\)Ge\({}_{3}\)O\({}_{12}\) (0.01-60 Gy/h [38]), and LuVO\({}_{4}\) (0.006-60 Gy/h [50]). The present results were inferior to the lowest dose rate retaining the linearity (0.006 Gy/h) reported in the past studies; however, they were comparable with the second-best dose rate (\(\sim\)0.01 Gy/h). When compared with other red or NIR-scintillators combined with Si-based detectors, the detection limit in the present result was superior to that of Cs\({}_{2}\)HfI\({}_{6}\) (2 Gy/h) and commercial Pr-doped Gd\({}_{2}\)O\({}_{2}\)S (GOS) (0.8 Gy/h) [20; 51]. Additionally, we compared the present results with previous ones of visible-scintillation detectors such as CdWO\({}_{4}\) combined with a PMT and Tb-doped GOS, which coated to a polymethyl methacrylate-based fiber combined with a Si-CCD. The lowest detectable dose rates of CdWO\({}_{4}\) and Tb-doped GOS-based detectors were \(\sim\)0.05 and \(\sim\)60 Gy/h [52; 53], and the present results surpassed these widely used commercial scintillators. The dose rate response of the CdWO\({}_{4}\) and Tb-doped GOS could not be measured in this research since their emission wavelength (\(\sim\)500 nm) was out of the wavelength sensitivity of the InGaAs-PD (\(>\)900 nm). Figure 3.1 Afterglow curves of Nd-doped CaWO\({}_{4}\). Figure 3.2 Relationships between the X-ray exposure dose rate and the intensity in the NIR ranges of the crystals.
# Experimental Study on Permeability Characteristics of
###### Abstract  
The timely injection of gangue slurry into the mining space formed after coal mining can scale up the disposal of gangue and control surface deformation. However, the waterproof effect of gangue slurry in the mining space remains unclear, necessitating urgent investigation into the permeability characteristics of compacted backfill bodies of gangue slurry under the action of overburden. In this study, a multi-field coupled seepage test system for backfill materials was developed based on Forchheimer's nonlinear seepage law, and a laboratory preparation method for compacted backfill body (CBB) of gangue slurry after grouting and backfilling in mining space under pseudo-triaxial conditions was proposed. Additionally, the pressure bleeding characteristics of gangue slurry under the action of overburden were studied, the variation law of permeability of the CBB with the axial pressure, a particle size range, and cement dosage was revealed, and the determination method for the permeability level of the CBB and its optimization method were put forward. The research results indicate that there are obvious staged characteristics in the pressure bleeding changes in gangue slurry. Axial pressure, particle size range, and cement dosage all have a significant impact on the permeability of the CBB. The permeability level of the CBB of gangue slurry is within the range of poor permeability and extremely poor permeability. After backfilling into the mining space, gangue slurry exhibits a significant water-blocking effect.  
mining space; gangue grouting and backfilling; the compacted backfill body; seepage testing system; permeability characteristics
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
###### Abstract  
The serpentine belt drive system is used in the auto industry. To avoid thermal destruction inside the belt drive and improve the thermal fatigue life of pulley materials under a variety of operating conditions, the temperature information for each load case must be determined within only a few seconds. To this end, this paper proposes an advanced thermal model to calculate the temperature distribution of a serpentine belt drive at static state operating conditions in an efficient manner. In this model, using analytical and numerical methods, a set of equations is developed according to the thermal flows and heat exchanges occurring in the system. After calculating the thermal flows of each pulley and the belt temperature, the baseline numerical simulations are modified to output the temperature distribution for each pulley. In this manner, the time-consuming numerical calculations for each pulley are performed only once and then analytically modified to provide the temperature predictions for various designed load cases, which dramatically reduces the computational time while maintaining the accuracy. Furthermore, experiments were performed to obtain the temperature data, and the results exhibited a good agreement with the corresponding calculated results. The proposed model can thus be effectively utilized for several types of belt systems and the material development of pulleys.  
thermal analysis; analytical algorithm; numerical simulation; serpentine belt drive +
Footnote †: journal: Applied Physics Letters
## 7 Experimental Results of the Proposed Low-Cost Sensor Node Architecture and AIS  
This section presents the experimental results of the proposed low-cost sensor node architecture and AIS for use in a nanosatellite, which is designed for IoST applications as previously discussed. The architecture, shown in Figure 22, is based on low-cost sensors and includes Arduino Atmega 2560 as the data acquisition system and Xbee pro as the communication system within the sensor network.  
The proposed sensor node architecture includes the KY-026 sensor, specifically designed as a flame sensor to detect flames within a wavelength ranging from 760 nm to 1100 nm. In addition, forest fires release gases, such as CO\({}_{2}\), CO, CH\({}_{4}\), and VOCs, including benzene, toluene, and formaldehyde. To detect the released gases by forest fires, CO and CH\({}_{4}\) are commonly monitored using the MQ-7 sensor, although it should be used in conjunction with other sensors and monitoring techniques. The MQ-2 sensor can detect various flammable gases, smoke, and VOCs, making it another useful tool for forest fire detection. Temperature and humidity sensors can also help to predict and prevent fires by detecting increases in temperature and low humidity levels, respectively. The DHT11 sensor is an example of a digital temperature and humidity sensor that can be used for forest fire detection. Combining these sensors with other detection technologies can create a more comprehensive forest fire detection system. The experimental data are shown in Figures 23-26. During this four-minute experiment, significant variations in the experimental data were observed in these figures both before and after the occurrence of a fire at 50 s.  
The fire decision can be reached after the analysis of the above results, such as temperature, humidity, gas, and flame presence as criteria. The alarm threshold is based on a simple decision procedure according to the measurement data and determines a fire. In this experiment, the recorded temperature surpassed 50 \({}^{\circ}\)C, indicating the presence of a fire and the flame observed was at a lower level compared to previous measurements, further confirming the occurrence of a fire. The other sensor node measurements data are important to detect fire presence and deduce the causes. The wind speed measurements do not show the presence of fires but can aid in emergency response and firefighting planning.  
Figure 22: Low-cost sensor node architecture.  
Figure 23: Data of the temperature and humidity changes observed in the experiment.  
Figure 24: Data of the detection of gases at different levels in the experiment.  
Figure 25: Data of flame level detection in the experiment.  
In the application of AIS, the GPS dedicated to maritime application was used to show the coordinates, as presented in Figure 27.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 2 Vertical Plane Modelling
### EMAT-Reception Simulation  
The EMAT reception process has been reported quite a lot [21]. The received signal from simulations is shown in Figure 8, where DRW and RRW denote the directly transmitted Rayleigh waves and the reflected Rayleigh waves, respectively. The propagation distance of DRW and RRW is 100 mm and 300 mm, respectively, and the velocity of Rayleigh waves is 2.93 mm/us; hence, the theoretically arrival time of DRW and RRW is 34 us and 102.4 us, respectively. Figure 8 shows the numerically arrival time of DRW and RRW; these numerical and experimental results present a good agreement.
# Effect of Bentonite Addition on the Properties of Fly Ash as a Material for Landfill Sealing Layers
## 2 Materials and Methods
### Materials
As the amount of bentonite addition increases, the optimal moisture content decreases and the maximum dry density increases. Specific surface area is higher when the addition of bentonite is larger--for 5% of addition, specific surface area increased twice, whereas for 10% almost three times in the relation to the fly ash alone. The amount of 15% of bentonite in the mixture caused an increase in specific surface area of more than three times compared to fly ash. The sandy silty clay has the highest specific surface area--approximately 30% higher than for FA+15%B, which indicates higher clay particles' content.
## 3 Proposed Mechanism
### Channel Changing (CC)  
The channel changing (CC) component that resides in the sink issues explicit control messages that trigger all nodes in a overloaded TB to change their channel. Two types of _channel changing messages (CCM)_ are used: (i) CCM-1 changes the channel of all nodes in a TB, and (ii) CCM-2 assigns a new channel in response to a path update.  
To change the channel of an overloaded TB, the sink sends a unicast CCM-1 to the CDN of the TB. The message has the fields \(<sender\), \(receiver\), and \(new\_channel>\). All nodes broadcast the CCM-1 except the sink. After receiving a CCM-1, every node checks the \(sender\) field. If a node receives the CCM-1 from its downstream node, it forwards CCM-1; otherwise, it discards it. While forwarding the CCM-1, each node replaces the \(sender\) field with its own address and puts the broadcast address in the \(receiver\) field. When a forwarder hears that at least one of its upstream nodes has forwarded the same message (by snooping), it changes its own channel to the \(new\_channel\). However, a leaf node changes to the \(new\_channel\) after receiving the CCM-1 from its downstream node.  
The CCM-2 message is unicasted with the fields \(<sender\), \(receiver\), \(new\_channel\), and \(destination>\). The \(destination\) is the address of the PUMR generator. CCM-2 also includes the reverse path from the sink to the \(destination\), and the channel ID of each intermediate node. Every node forward the CCM-2, and changes its channel to the \(new\_channel\). When the \(destination\) receives the CCM-2, it converts it to CCM-1 and broadcasts it. Therefore, the path update and channel changing happen simultaneously.  
Because the CCM-1 messages are broadcasted, the nodes that are missing this message need to find the channel ID of the downstream node. LPMC uses a _What Is_ (WI) message to find the channel ID of a specific node. The node with the ID given in the WI message replies with a _What Is Reply_ (WIR) message.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 2 Lunar Scenario  
Most upcoming lunar missions target the lunar south pole and will require observation infrastructure that can provide support services to this area. This region is not only of interest for planetary scientists because of its regional geology, containing a very high number of impact craters, but it is also expected to store a significant quantity of cold-trapped volatiles, which may be used as a source of in situ resources [21].  
This section explores proposed orbital infrastructure and potential architectures that may deliver a ranging service. Orbital design has been optimised in a previous work [7], where this is introduced. Options for the proposed autonomous navigation system, which is the topic of this work and one of the key challenges for delivering a ranging service, are then introduced, alongside potential design limitations and challenges to performance.
# SROR: A Secure and Reliable Opportunistic Routing for VANETs
## 4 Opportunistic Routing and Problem Formulation
### Problem Formulation  
By optimizing the selection of next-hop relay node, the Packet Delivery Ratio (PDR) can be improved while the delay is reduced. The PDR is the ratio of the number of packets received by destination to the number of packets that should have been received. The delay is the End-to-End Delay (EED).  
Specifically, as Figure 4 shows, \(\theta_{0}\) and \(\theta_{m}\) represent a source node and destination, respectively. There is an \(m\) hops path from the \(\vartheta_{0}\) to the \(\vartheta_{m}\). The EED of the path is the sum of delay of \(m\) links. Therefore, the optimization is given by:  
\[P1:min\left[\sum\limits_{i=0}^{m-1}{{{T}}_{i,i+1}^{\omega}}+\prod\limits_{i=0} ^{m-1}{{(1-{PFR}_{i+1})}}\right], \tag{7}\]  
\[\text{s.t.}\ \ P_{i}\geq 0.63,\forall i=1,2,\ldots,m,\]  
\[0\leq{{v}_{i,i+1}}\leq 200,\forall i=0,1,\ldots,m-1,\]  
\[{PFR}_{i}\geq 0.4,\forall i=1,2,\ldots,m.\]  
where \(\sum\limits_{i=0}^{m-1}{{{T}}_{i,i+1}^{\omega}}\) is the sum of the EED delay of the \(m\) hops path and \(\prod\limits_{i=0}^{m-1}{{(1-{PFR}_{i+1})}}\) reflects the packet loss ratio of the path. For that, the smaller the EED delay and packet loss ratio is, the better the routing performance is. As indicated by the constraint \(P_{i}\geq 0.63\), the  
Figure 3: The idea of opportunistic routing.  
node with connectivity probability greater than 0.63 is allowed to add into the CFNs set. The constraint \(0\leq\
u_{i,j+1}\leq 200\) means that relative velocity of the candidate forwarding node with respect to \(\dot{\theta}_{i}\) should be more than 0, and less than 200. The constraint \(PFR_{i}\geq 0.4\) represents only one node with PFR greater than 0.4 can be selected to be a candidate forwarding node.