esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 EMS Optimization
### Grid Power and Grid Price
Beginning at 91 USD/kWh at the eighth hour, the price increases to a peak of 111 USD/kWh around the twenty-second hour, then decreases back to 91 USD/kWh by the twenty-fourth hour, eventually reaching 75 USD/kWh. This figure reflects the dynamic nature of electricity pricing, which is influenced by market conditions, demand patterns, and supply factors throughout the day. Figure 4: Grid Power. Figure 5 illustrates the variations in grid electricity prices (in USD/kWh) over a 24 h period, reflecting the pricing strategy managed by the energy market. The horizontal axis represents time from 0 to 24 h, while the vertical axis indicates the grid price in increments of USD 10/kWh. * Daytime Period (6:00 AM-6:00 PM, 6 to 18 h): During the daytime, the grid price shows an increase starting around 7:00 AM (7 h) and maintains higher values, peaking between 10:00 AM to 4:00 PM (10 to 16 h). This likely corresponds to increased demand for electricity during working hours, where businesses and households consume more energy, pushing prices higher. The price stabilizes after this period, indicating a balance between supply and demand. * Nighttime Period (6:00 PM-6:00 AM, 18 to 6 h): At night, the grid price drops back to a lower level after 6:00 PM (18 h), reflecting reduced electricity demand as residential and commercial activities decrease. The price remains stable and low throughout the night, aligning with typical lower energy consumption during these hours. Figure 5 highlights the impact of daily demand cycles on electricity prices, with distinct price patterns emerging during day and night. Understanding these price fluctuations is critical for optimizing the operation of the energy management system (EMS), particularly in deciding when to draw energy from the grid or deploy stored energy to reduce costs. These numbers offer vital information for examining the effects of price and power supply fluctuations on the battery energy storage systems (BESSs) and hydrogen energy storage systems (HESSs) inside the energy management system (EMS) and their optimization techniques. The development of efficient energy management plans with the goals of lowering expenses, boosting grid stability, and raising overall system efficiency is supported by this analysis.
## 2 Related Work
In [27], the authors present an adaptive method for determining the beacon interval within a position-based protocol, taking into account vehicular traffic conditions. This strategy relies on three main mobility factors: vehicular density, the average speed difference among neighboring vehicles, and the spatial distribution of these neighbors. A context-aware hybrid beaconing algorithm is proposed in [28], aiming to enhance safety by disseminating driver status via beacons to neighboring vehicles in IEEE 802.11p-based networks. The comprehensive context is taken into account to derive driver status and initial network status. Real-time network congestion status is considered, and adjustments to transmission range and power parameters at the PHY layer, as well as beacon interval and contention window at the MAC layer, are dynamically made using the enhanced distributed channel access (EDCA) mechanism. Ensuring the effective adjustment of beaconing transmission parameters requires the consideration of various factors such as channel conditions, traffic situations, and link quality. Many existing schemes in the literature concentrate on just one or two of these aspects, constraining their adaptability. In our study, we introduce an innovative scheme that simultaneously takes into account all three crucial factors. Furthermore, our proposed scheme is crafted to dynamically respond to alterations in network density and environmental conditions, offering a comprehensive and adaptive strategy for beaconing across diverse scenarios.
## 2 Related Work
Intuitively, a node \(i\) should give more weight to the direct observations made by itself than to the evidence obtained from other nodes. Furthermore, the evidence from different nodes should be weighted on the basis of their respective reputations. The beta reputation system [23] and recent implementations for Mica2 modes [24] are based on these observations to provide resistance against badmouthing attacks. A different scheme is proposed in [25], based on the separation between action trust and recommendation trust. Either because they hold encryption keys or for other reasons, many approaches demand that the nodes be tamper-proof secure [26]. But this need is more difficult to fulfill with every passing day [27, 28, 29]. CONFIDANT's authors reason out that their protocol does not need tamper-proof hardware because other nodes' reputation tables are not alterable by the attack. As far as we know, it is not very clear how they maintain the local trusted nodes list or how trust information is updated. It could be difficult to detect a sybil attack [30, 31] or the impersonation of a trusted node by first reading the trusted node list from a tampered node. In an early design stage we decided not to depend on the hardware being tamper-proof. In fact, it is our assumption that it isn't _and_ that the communications between nodes with limited resources are not secure. Our approach compensates those drawbacks by taking advantage of temporal and spatial redundancy. In general, most of the studied architectures provide security (by just preventing attacks or by simultaneously detecting attacks and providing countermeasures) in the routing protocol at network level. In [10] we proposed a security infrastructure designed for intelligent environments that takes advantage of the environment and uses information from the application layer. The premises of this solution were redundancy and continuous evolution. We maintain this system as a good solution but it can be improved. In this work we have introduced SOM algorithms to make the system faster in terms of adaptation.
###### Abstract  
This study extends our previous work showing that a process-based (PB) model, the _DIMona_ PB model, could accurately simulate peatland soil water dynamics when driven by water table depth, \(\mathrm{d_{WT}}\), or by near surface soil water contents, \(\mathrm{\SIUnitSymbolFace{0}}\). Here, we explore the model's ability to simulate the peatland canopy photosynthesis, growth, biomass, height, and gross primary productivity (GPP) of vascular plants and bryophytes--thus ecosystem GPP--using either of these drivers. The _DIMona_ PB model is embedded into the _DIMona_ online modelling platform, a web application capable of ingesting data from the Internet and performing machine learning (ML) modelling and Internet of Things (IoT) modelling complementary to PB modelling. We test whether the _DIMona_ PB model, driven by \(\mathrm{d_{WT}}\) (Hypothesis 1) and by near-surface \(\mathrm{\SIUnitSymbolFace{0}}\) (Hypothesis 2), can successfully simulate peatland ecosystem GPP at the Mer Bleue bog (Ontario, Canada). Two model runs were generated, one driven by \(\mathrm{d_{WT}}\) and another by near-surface \(\mathrm{\SIUnitSymbolFace{0}}\). Both model runs performed with similar accuracy. Data fit for simulated on observed GPP reached 0.72 for R\({}^{2}\), 1.7 umol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\) for RMSE, and 0.88 for [PERSON]'s index of agreement at an hourly time step and 0.91, 0.8 g C m\({}^{-2}\) d\({}^{-1}\), and 0.92, respectively, at a daily time step. We use the output from the two model runs to examine whether the model's modifiers (i.e., equations) for water control can capture the specifics of contrasting hydrological conditions on peatland GPP (Hypothesis 3). Both model runs closely simulated the observed GPP to contrasting peatland hydrological conditions under similar meteorological forcing. We illustrate the ability of the _DIMona_ platform to facilitate the parameterization of _DIMona_ models for any geographic location, as well as its ability to perform IoT modelling of real-time photosynthesis at two site locations and ML modelling for ecosystem GPP as a complementary tool to PB modelling.  
Keywords:process-based modelling; ecosystem and plant productivities; new modelling approach for plant water control on productivity; online modelling platform; peatlands +
Footnote †: journal: water
# A Scalable and Consistent Method for Multi-Component Gravity-Gradient Data Processing
## 2 Methodology
### Classical Equivalent Layer
, \tag{9}\]  
where \(N_{t}=2N\) and  
\[\mathbf{H}^{uv}=\frac{1}{2}(\mathbf{H}^{yy}-\mathbf{H}^{xx})\,. \tag{10}\]  
The estimated mass distribution \(\hat{\mathbf{m}}\) minimizing the data-misfit function \(\Phi(\mathbf{m})\) (Equation (5)) without any constraint can be obtained by solving the normal equations  
\[\left(\mathbf{H}^{\top}\mathbf{H}\right)\hat{\mathbf{m}}=\mathbf{H}^{\top} \mathbf{g}^{o}\,. \tag{11}\]  
Notice that, for large datasets, there exists a very large computational demand to store the required matrices and vectors, solve the normal equations (Equation (11)), and perform matrix-vector operations. Our approach to overcome this computational demand is adapting the convolutional equivalent-layer methods proposed by [33] and [34] to take advantage of the particular structure of matrix \(\mathbf{H}\) (Equations (7) and (9)). We call attention to the fact that the Equations (2)-(10) highlight key differences between our method and the convolution-based formulation proposed by [34]. Unlike their approach, which constructs the sensitivity matrix as a weighted sum of directional components derived from magnetic data, our formulation is based on gravity-gradient observations and builds the sensitivity matrix by vertically stacking the individual contributions from each measured component (Equations (7) and (9)). Moreover, whereas the method proposed in [34] presumes data-acquisition geometries strictly aligned with the north-south and east-west directions, our approach is designed to maintain effectiveness when flight lines are oriented along rotated geometries that do not conform to these alignments.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 5 Summary and Outlook
### Key Challenges  
#### 5.2.1 The Array Can Be Located on Any Undulating Carrier and Terrain  
In practical engineering applications, the actual environment deviates from the ideal situation, and the antenna array may be located on any undulating and shaped carrier or terrain; for example, carrying the array on the satellite load cannot achieve the ideal area array. This also makes the actual array layout of the array elements different from theory, and the antenna pattern will inevitably cause deviations. This deviation will increase with the expansion of the array scale. To avoid losing generality, two methods can be considered to solve this problem; the first method is to use a three-dimensional layout space surface array in the pattern design. The second method is to equalize the three-dimensional layout array antenna to a sparse planar array, but the actual error needs to be considered, and some parameters to correct the error can be introduced to reduce the error and improve the design accuracy.
## 2 Methodology
### Backbones  
Backbones are usually part of the networks responsible for extracting features from images. CNNs are often pre-trained on large databases. These backbones allow leveraging of the learned features as a starting point for a particular task. In this research, several backbones were evaluated in Faster R-CNN, SSD, and YOLO v4 architectures. The residual Network (ResNet) [33] is a CNN, and among the most well-known variants are ResNet-50 and ResNet-101. ResNet was trained with over a million images from 1000 categories in the ImageNet database [34]. The size of the input image was 224 \(\times\) 224 px. ResNet was designed to mitigate the problem of vanishing gradients that occur as network depth increases. To achieve this, residual connections are introduced, as shown in Figure 6a. These residual connections allow input \(X\) to skip the convolutional layers and be added to the output of those layers, \(F(x)\). MobileNets [35] is designed for use in embedded and mobile applications.  
MobileNet was trained using the ImageNet database. This network introduces two hyperparameters that allow for low latency, while maintaining adequate accuracy. Figure 6b illustrates these hyperparameters. First, it transforms the standard convolution into a 3 \(\times\) 3 depthwise convolution through factorization. Then, it performs pointwise convolution using a 1 \(\times\) 1 convolution. The introduction of these parameters primarily aims to reduce the model size and computational cost. The combination of these layers is known as depthwise separable convolution (DSC). MobileNetv2 [36] has a structure that is very similar to that of MobileNet. It utilizes the layers mentioned above; however, in a different manner, the input is transformed with a pointwise convolution layer to expand the channels. It passes through a 3 \(\times\) 3 depthwise convolution to decrease the computational complexity. It passes through another pointwise convolution layer. These steps form the basic unit of MobileNetv2, which is called at the residual bottleneck block.  
YOLO v4 uses CSPDarkNet53 [32] as its backbone. This was derived from the DarkNet53 backbone of YOLO v3 [37]. It was trained on the Common Objects in Context (MS COCO) database [38], which contains over 200,000 labeled images with approximately 1.5 million instances of objects in 80 categories. The input image size was 416 \(\times\) 416 px. The  
Figure 6: Units of ResNet and MobileNet. (**a**) Basic unit of ResNet [33]. (**b**) Basic unit of MobileNet [35].  
addition of cross-stage connections (CSP) allows the integration of feature maps from the initial to the final stage. The main goal is to achieve a rich combination of gradient while reducing computational costs. CSP reduced the computational cost by approximately 20%.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 1 Introduction
Moreover, previous studies have shown that bamboo-derived hard carbon exhibits favorable electrochemical properties for sodium storage, including high capacity and stable cycling performance [11]. Given the spatial concentration of bamboo industries and the consistent generation of residues, BR represents a stable, scalable, and underutilized resource for cost-effective hard carbon production in key provinces such as Fujian, Jiangxi, and Zhejiang [12]. Techno-economic analysis (TEA) is a widely adopted tool for assessing the commercial viability of emerging technologies, particularly in the fields of bioeconomy and energy storage. TEA provides a structured framework to evaluate capital investments, operating costs, and economic returns under defined technical assumptions and financial conditions [13]. In recent years, TEA has been extensively applied to biomass thermochemical conversion pathways and carbon material manufacturing. For instance, one study reported that bio-based activated carbon production could achieve an energy efficiency of 8.0%, with a minimum selling price of 9.72 CNY/kg, an internal rate of return (IRR) of 13.93%, and a payback period of 7 years [14]. [PERSON] et al. optimized the co-carbonization of waste mushroom substrate and bagasse, resulting in a carbon yield of 66.7%, a net present value of USD 80.5 million, and a climate benefit of \(-\)2.37 kg CO\({}_{2}\)-eq per kg of product [15]. Another study by [PERSON] al. used a portable refining system to produce biochar from cattle manure, estimating a production cost of USD 237 per tonne and emissions of 951 kg CO\({}_{2}\)-eq per tonne [16]. Although techno-economic analyses (TEAs) have been widely applied to assess various biomass-derived solid materials, the economic feasibility of producing biomass-based hard carbon--a critical anode material for sodium-ion batteries--remains unexplored. To the best of our knowledge, this study represents the first TEA focused specifically on biomass-derived hard carbon production, addressing a crucial gap within the emerging sodium-ion battery supply chain literature. In this study, we conduct a process-level techno-economic analysis of hard carbon production from bamboo residues, focusing on ten representative provinces in China with the highest bamboo resource availability. Based on an established life cycle inventory (LCI) framework and a discounted cash flow (DCF) model, we estimate capital and operational costs and determine the minimum selling price (MSP) required to achieve a 15% internal rate of return. The results provide critical insights for investors, policymakers, and technology developers aiming to scale up sustainable anode material production from regional biomass resources.
## 6 Results
### Energy  
Figure 8 shows the results obtained in the energy consumption tests. The results are obtained by measuring the energy consumed by a node when replying to ten consecutive requests. In fact, a fair comparison that may account for the persistent TCP connection used in HTTP persistent requires measurement of the energy consumed in more than one transaction.  
Figure 7: Details of the latency results. The better memory management of TinyCoAP allows the performance of CoapBlip to be improved.  
The results of this test do not take into account the energy wasted by the radio chip for listening the channel. Consequently, our evaluation does not need to consider power-saving protocols for radio duty-cycling. We only measured the energy consumed for receiving, processing and sending a packet. As mentioned in section IV the energy consumed by the CPU for packet processing is considerable. However, the energy consumed by the radio chip for receiving and sending a packet is still predominant over that used in packet processing. In this test, the consumption due to the radio chip has the same impact on the results of each implementation. The differences between the performance of each implementation are only due to the effects that the packet processing has on consumption. For each different payload size, we ran tests sampling the energy consumption each 0.02 ms. The device used for these measures is the Agilent Technologies DC power Analyzer N67705A.  
As expected, a more implementing HTTP solutions based on TCP consumes more energy than others. Once again, the message overhead caused by TCP proves to be costly for constrained networks. As seen in the latency tests, the HTTP persistent improves the performance of HTTP/TCP. This is due to the use of a persistent TCP connection. However, its performance is still much worse than that obtained by TinyCoAP or HTTP/UDP. The management of TCP connections requires a high degree of complexity and the maintenance in memory of the connection state. Consequently, there is a growth in the energy drawn by the RAM memory for keeping these states.  
CoapBlip has a performance comparable to that of HTTP persistent. The onset of the CoapBlip trend is lower than that of HTTP persistent and comparable with that of TinyCoAP or HTTP/UDP. However, the increase in packet size causes a sharp rise in the CoapBlip energy trend, which exceeds that of HTTP persistent at 500 bytes. Once more, the mechanism implemented in CoapBlip for allocating and managing RAM memory proves to be unsuitable for constrained devices. Instead, TinyCoAP benefits from the different memory allocation mechanism in use. Furthermore, the different design of TinyCoAP allow to it to have less impact on energy consumption. Similarly to that seen in  
Figure 8: Energy consumption. TinyCoAP has a lightweight packet processing that allows the energy consumption approaching the trend of HTTP/UDP to be lowered.  
the latency tests, TinyCoAP has a performance that is highly comparable to that of HTTP/UDP. This proves once again that TinyCoAP is able to minimize the consumption of resources.
## 2 Methodology and System Modeling
### Energy Storage System Model
#### 2.3.1 Battery Energy Storage System (BESS)  
The amount of discharging and charging power drawn or sent to the battery energy storage system, respectively, is subject to the previous state of charge (SOC) as well as the ESS system constraints. The SOC at any given \(t\) is determined by the following equation.  
\[SOC(t)=\left[(PV_{p}(t)+WT_{p}(t))-\frac{L_{D}(t)}{\beta_{c}}\right]\times \beta_{ch}+SOC(t-1)\left(1-dr\right) \tag{3}\]  
where \(SOC(t-1)\) and \(SOC(t)\) and is the BESS state of charge for the previous and current period in kWh, respectively. \(L_{D}(t)\) is the load demand, \(\beta_{c}\) denotes the power converters efficiency, \(dr\) and \(\beta_{ch}\) is the hourly self-discharge rate and BESS charging efficiency respectively. Whenever the total generation cannot meet the load demand, BESS shifts into the discharging mode. Consequently, the current state of charge at any given time \(t\) is given by:  
\[SOC(t)=\left(\frac{L_{D}(t)}{\beta_{ds}}-\left(PV_{p}(t)+WT_{p}(t)\right) \right)/\beta_{ds}+\left(SOC(t-1)\left(1-dr\right)\right) \tag{4}\]  
where \(\beta_{ds}\) is the discharging efficiency. The energy storage level (SOC) must be constrained within the upper \(SOC_{max}\) and the lower \(SOC_{min}\) bounds of the BESS.  
\[SOC_{min}\leq SOC(t)\leq SOC_{max} \tag{5}\]
## 3 Methods
### Electro-Mechanical Circuit Model  
Figure 2a shows a cross-sectional sketch of an electrodynamic actuator, which may be represented by the electro-mechanical equivalent circuit schematic diagram shown in Figure 2b, which accounts both for its electrical and mechanical parts [11].  
Figure 2: Electrodynamic actuator: (**a**) schematic representation (parts: (1) magnet, (2) iron piece, (3) coil, (4) chassis, (5) suspension, (6) base); (**b**) electro-mechanical equivalent circuit.  
The equation that governs the electrical behavior of such actuators is given by [18]  
\[(R_{E}+j\omega L_{E})i=e-Bl(v_{T}-v_{B}), \tag{6}\]  
where \(R_{E}\) and \(L_{E}\) are the resistance (in \(\Omega\)) and inductance (in H) of the coil, respectively, \(i\) is the electrical current (in A), \(e\) is the open-circuit applied voltage (in V), \(Bl\) is the electro-mechanical transformation factor or force factor (in Tm), and \(v_{T}\) and \(v_{B}\) are the velocities (in m/s) on the top mass (i.e., the magnet) and base of the actuator, respectively (i.e., \(v_{B}\) = 0 m/s when the actuator is mounted on a rigid floor).  
On the other hand, the equation of motion that governs the dynamic behavior of the actuator mass can be written as  
\[j\omega M_{M}v_{T}=Bli-(R_{M}+K_{M}/j\omega)(v_{T}-v_{B}), \tag{7}\]  
where \(M_{M}\) is the mechanical mass of the actuator (in kg) including the extra load, \(R_{M}\) and \(K_{M}\) are the mechanical resistance (in Ns/m) and stiffness of the suspension (in N/m), respectively, and the \(Bli\) term corresponds to the electromagnetic force (in N).  
Both the velocity on the top mass and base of the actuator can in turn be related to the corresponding mechanical forces\(f_{T}\) = \(v_{T}/Y_{T}\) and \(f_{B}\) = \(v_{B}/Y_{B}\) (both in N) from \(f_{T}\) = \(-f_{B}\) so that  
\[v_{B}Y_{T}=-Y_{B}v_{T}, \tag{8}\]  
where \(Y_{T}\) = \(1/j\omega M_{M}\) is the mobility of the loaded actuator mass and \(Y_{B}\) is the mobility function of the system at the base of the actuator (both in ms\({}^{-1}\)/N).  
The electrical input impedance of the whole system can thus be derived as [10]  
\[Z_{ET}=\frac{e}{i}=Z_{E}+\frac{(Bl)^{2}}{Z_{M}-\frac{(j\omega M_{M})^{2}Y_{B}} {1+j\omega M_{M}Y_{B}}}, \tag{9}\]  
where \(Z_{E}\) = \(R_{E}+j\omega L_{E}\) (in \(\Omega\)) and \(Z_{M}\) = \(R_{M}+j\omega M_{M}+K_{M}/j\omega\) (in Ns/m) are the electrical and mechanical impedances of the electrodynamic transducer, respectively [19].  
After some algebraic manipulations of Equations (6)-(9), the mobility function of the same slab-material arrangement used in the standard procedure (including the extra load and the actuator) can be obtained from  
\[Y_{B}=\frac{Z_{M}\Big{(}1-\frac{Z_{ET,0}-Z_{E}}{Z_{ET}-Z_{E}}\Big{)}}{\left(j \omega M_{M}\right)^{2}} \tag{10}\]  
where \(Z_{ET,0}\) stands for the electrical input impedance (in \(\Omega\)) of the mass-loaded actuator when mounted on a rigid floor.