esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods  
### Case Studies Description  
According to the criterion in Reference [20], severe rainfall events in the Mediterranean area can be classified in two main types: type I-long-lived (duration \(\geq\) 12 h) and spatially distributed (more than 50 \(\times\) 50 km\({}^{2}\)) and type II-brief and localized, having a shorter duration (\(\leq\) 12 h) and a spatial extent smaller than 50 \(\times\) 50 km\({}^{2}\). The experiments of this study were conducted by considering one event of type I and one of type II. Events of type I correspond to the equilibrium convection, where it is assumed that CAPE (Convective Available Potential Energy [20]) production by large-scale processes is nearly balanced by its consumption by convective phenomena and, thus, CAPE values stay small. In events of type II, a larger amount of CAPE is available, as a result of building up from large-scale processes over long time-scales. However, the extent to which it produces convection and precipitation is restricted by the need for a trigger to overcome the convective inhibition energy (CIN).  
As underlined in the Introduction, Sentinel missions have guaranteed a significant improvement of the temporal resolution of EO data with respect to previous satellite missions (e.g., ENVISAT). However, finding two severe weather events, corresponding to type I and II categories and characterized by suitable Sentinel observations (i.e., performed in a time range compliant with the reliability of the model forecasts \(\sim\)48 h, see Reference [11]) turned out to be a difficult task.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 4 System Design and Simulation Configuration
### Simulation Cases  
We have considered mainly two cases here:  
Outdoor BD with outdoor Rx: in this case, the BD and the Rx are both located in an outdoor environment. The ray tracing model and ITU outdoor D2D model are used to find the propagation path loss of forward link and backscatter link, respectively. Indoor BD with indoor Rx: in this case, the BD and the Rx are both located in an indoor environment. A RT model is used as a propagation model for the forward link and 3 GPP InH model is used for backscatter link, respectively. We have considered a mixed type indoor environment, and computed the received power at the indoor receiver assuming old and new building type.
# Hamming Code Strategy for Medical Image Sharing
## 3 Hamming Codes
### Using (7,4) Hamming Code
Even though the other 25% of pixels have the same values as those of the original pixels (Case 3), one does not know the position of these pixels. From the above description, we cannot obtain the original image \(I_{\rm j}\) from one \(S_{\rm j}\). The security condition is achieved. Next, we prove the threshold condition. Suppose that we have both shadows \(S_{1}\) and \(S_{2}\). From \([p^{\prime}{}_{1}\ p^{\prime}{}_{2}\ p^{\prime}{}_{3}\ d^{\prime}{}_{1}\ d^{ \prime}{}_{2}\ d^{\prime}{}_{3}\ d^{\prime}{}_{4}]\), the correct codeword \([p_{1}\ p_{2}\ p_{3}\ d_{1}\ d_{2}\ d_{3}\ d_{4}]\) is recovered, and thus the value of \((x_{1}\ x_{2}\ x_{3})\) is obtained. Finally, the original pixels are recovered.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 4 Proposed Schemes
### B-Dbr
#### 4.3.1 Explanation of Algorithm 2  
The main steps of the B-DBR protocol are represented in Algorithm 2. If a node is in the fall back recovery mechanism, a new data packet will be queued until the selected node in the backward direction has neighbors in the direction of the destination to resume the greedy forwarding mechanism. If it finds the forwarder nodes greater than zero, the data packet is forwarded. However, when the forwarder is not available, instead of dropping the data packet, it finds an alternate route to reschedule the data transmission. This process repeats itself until the death of all the nodes in the network.  
```
if void node or known sinks = 0 then  Queue the data packets  Re-schedule forward data packet() else \(f_{i}\longleftarrow get\_next\_hop\_forwarder(n)\) if\(|f_{i}|>0\)then  Forward the data packet else  Queue the data packet  Re-schedule \(forward\_data\_packet()\)  Proposed_mechanism() endif endif
```  
**Algorithm 2** Main steps of the B-DBR scheme
## 2 Uncertainty Analysis and Wind Farm Modeling
### System Load  
To reflect a proper probabilistic approach, the uncertainty in the system load must be considered. The pattern of the system load is assessed by accumulating the consumption periodically over daily, weekly, or monthly periods. Uncertainties related to the predicted load data are usually considered by using a normal distribution with a standard deviation. A normal probability distribution function can be expressed as  
\[f_{L}(P_{L})=\frac{1}{\sqrt{2\pi{\sigma_{L}}^{2}}}exp\left[-\frac{\left(P_{L}- \varepsilon_{L}\right)^{2}}{2{\sigma_{L}}^{2}}\right] \tag{5}\]
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
###### Abstract  
Timely and accurate agricultural information is needed to inform resource allocation and sustainable practices to improve food security in the developing world. Obtaining this information through traditional surveys is time consuming and labor intensive, making it difficult to collect data at the frequency and resolution needed to accurately estimate the planted areas of key crops and their distribution during the growing season. Remote sensing technologies can be leveraged to provide consistent, cost-effective, and spatially disaggregated data at high temporal frequency. In this study, we used imagery acquired from unmanned aerial vehicles to create a high-fidelity ground-truth dataset that included examples of large mono-cropped fields, small intercropped fields, and natural vegetation. The imagery was acquired in three rounds of flights at six sites in different agro-ecological zones to capture growing conditions. This dataset was used to train and test a random forest model that was implemented in Google Earth Engine for classifying cropped land using freely available Sentinel-1 and -2 data. This model achieved an overall accuracy of 83%, and a 91% accuracy for maize specifically. The model results were compared with Rwanda's Seasonal Agricultural Survey, which highlighted biases in the dataset including a lack of examples of mixed land cover.  
Sentinel-1; Sentinel-2; UAV; Google Earth Engine; sub-Saharan Africa Article  
Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 7 Discussion and Future Research
### More and Effective Security Application Support  
Currently, advanced fuzzing techniques like hybrid fuzzing [54], directed fuzzing [55], and stateful fuzzing [56], along with dynamic bug detectors such as _MSan_[57] and _UBSan_[58], are not widely adopted for firmware targets. This is primarily due to two reasons: first, the black-box nature of binaries makes instrumentation and context inference, such as state variables, challenging for directed, stateful fuzzing and sanitizers. Second, although symbolic execution has been integrated into emulation environments, it is mainly used for peripheral modeling rather than continuous hybrid fuzzing like _QSYM_[54]. This limitation arises from the need to modify machine code for improved symbolic execution performance, which is tightly coupled with host architecture. The diverse and distinct nature of firmware architectures presents challenges for such applications. To advance fuzzing technology, future efforts should combine it with statistical analysis for better information inference and focus on instrumenting intermediate representations rather than directly on binary code to support diverse CPU architectures.  
Firmware emulation security applications are mainly limited to fuzz testing due to low data and memory fidelity. As emulation fidelity improves, more security analysis technologies, such as taint analysis for detecting vulnerabilities like command injections,can be applied to firmware targets. Additionally, MCU devices are used in security-sensitive environments. Compliance checks with hardware operations are crucial, as errors that do not affect control or data flow can still cause serious issues, such as failing to engage brakes in an ASDS.
# Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks
## 1 Introduction
### Related Work
We can observe that none of these contributions presents a CI-based solution for maximizing the sum rate of the NOMA-based secondary network by optimizing overall system transmit power while guaranteeing the QoS requirement of the primary network. This article aims to fill this research gap by presenting the following contributions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Major Focus** & **Primary DL** & **Performance Analysis** \\ \hline
[22] & 2018 & Single cooperative relay to assist distant PU & Yes & OP and ergodic sum capacity showed improved performance compared to conventional NOMA networks \\ \hline
[23] & 2016 & Select the single best relay to forward the primary signal & Yes & Improved OP, but not suitable for long physical separation between primary transmit-receive pair \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Existing Works.
## 5 Experimental Results
### Metrics of Interest  
Depending on the nature of the metric of interest, we have characterized it (statistically) in terms of either average value, _Probability Mass Function_ (PMF) or _Cumulative Distribution Function_ (CDF). The performance metrics considered in our experimental campaign can be summarized as follows.  
* The number of _DATA_ messages retransmitted at each SU--the ratio between this quantity and the total number of transmitted packets is correlated to the Probability Assignment Function (PAF) of the IF protocol defined in Equation (1).
* The PMF of the hop index at which the _DATA_ messages are received. This indicator is strictly related to the distance between a PU and an SU.
* The number of missing _DATA_ messages, before the first _REQUEST_ message. This metric is a coarse indicator of the wireless channel quality. In ideal conditions, the PMF should concentrate only at zero. However, in realistic conditions, this statement is not valid, due to radio channel interference and fading.
* The number of _DATA_ messages requested when receiving a _REQUEST_ message. This metric is representative of the \"attitude\" of a node to receive requests for missing chunks from surrounding nodes.
* The PMF of the archive reception time. This metric indicates the amount of time it takes to a node to receive all the chunks of the archive sent by the PU.
* The CDF of the number of _DATA_ messages received before each _REQUEST_ message. This metric is an indicator of the reception status of the traffic update before each round of _REQUEST_ messages.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
###### Abstract  
Massive MIMO technique offers significant performance gains for the future of wireless communications via improving the spectral efficiency, energy efficiency and the channel quality with simple linear processing such as maximum-ratio transmission (MRT) or zero-forcing (ZF) by providing each user a large degree of freedom. In this paper, the system performance gains are studied in a multi-cell downlink massive MIMO system under the main considerations such as perfect channel estimation, imperfect channel estimation and the effect of interference among cells due to pilot sequences contamination. Then, mathematical expressions are derived for these gains i.e., spatial multiplexing gain, array gain and spatial diversity gain. After that, essential tradeoffs among these gains are considered under the effect of non-orthogonal interference, these tradeoffs are: spatial diversity gain vs. spatial multiplexing gain and array gain vs. spatial multiplexing gain. Simulation results show that the unbounded number of base station antennas boosts the array gain through concentrating the energy to spatial directions where users are sited, hence diminishing loss in array gain due to pilot contamination. The simulation results reveal also that massive MIMO strengthens the spatial multiplexing gain through increasing the number of served users via the same system resources in spite the effect of inter-cell interference. Finally, the spatial diversity gain is measured in term of outage probability and the simulation results show that raising the number of antennas will improve the outage probability. Meanwhile increasing the number of served users will lead to degrade the outage probability per user due to non-orthogonal interference from other cells.  
massive MIMO; multiplexing gain; diversity gain; array gain; energy efficiency; spectral efficiency; outage probability; channel state information; pilot contamination +
Footnote †: journal: Applied
## 3 Methods and Modeling  
### Methods  
This study utilizes game theory to model the strategic decision-making processes of entities within the CBEC supply chain. This approach is particularly suitable for analyzing decision-making processes under different governance structures [30; 32; 34]. In the centralized decision-making scenario (hereinafter referred to as CD), channel members collaborate to maximize overall supply chain profit. In contrast, the decentralized decision-making scenario (hereinafter referred to as DD) involves a Stackelberg game, where the IT-adopting merchant acts as the leader, leveraging its technological edge to set strategies first, followed by responses from the non-IT-adopting merchant. This framework provides a clear lens through which to observe how decision-making structures influence supply chain strategies and interactions.  
Simultaneously, to better understand consumer behavior and its impact on supply chain decisions, we incorporate a consumer utility. This method is instrumental in modeling consumer purchasing decisions by constructing a utility function that incorporates factors such as product valuation, prices, and service levels [28; 29; 32]. By analyzing this function, we can derive demand functions for different channels and investigate how consumers' sensitivity to price and service affects market demand and channel selection. This analysis is vital for grasping market responses to supply chain strategies and aids in formulating supply chain decisions that align with consumer expectations.  
In the CBEC market, consumers exhibit varying degrees of sensitivity towards price and service. For some consumers, fluctuations in product pricing significantly influence their purchasing decisions, indicating a high level of price sensitivity. Conversely, other consumers place greater emphasis on the quality of the service provided, with changes in service levels having a more pronounced impact on their utility. This divergence in consumer behavior allows us to categorize them into two distinct groups: price-sensitive consumers and service-sensitive consumers. The differences between these groups are evident in how they perceive the value of product pricing and service quality.  
Since the same product or service can yield different levels of utility for different consumers, the utility function should incorporate a parameter to represent the consumer type. Additionally, as different products or services can have varying impacts on the same consumer, the utility function should also include a parameter that reflects the degree of differentiation among products and services.
# Lift-Thrust Integrated Ducted-Grid Fusion Configuration Design for a Ducted Fan Tail-Sitter UAV
## 4 Analysis of the Influence of Fusion Configuration Design Parameters
### The Influence of Grid Circumferential Distribution Angle  
The circumferential distribution angle determines the circumferential embedding range of the grid on the duct, and this range is not necessarily the larger the better. According to the design principle of this article, the reason why the duct with embedded grid generates lift is due to the difference in force components between the duct with and without grids. If the circumferential distribution angle exceeds 180 degrees, this difference relationship may actually deteriorate. This paper analyzes the circumferential distribution angles between \(10^{\circ}\) and \(30^{\circ}\), with a focus on the changes in aerodynamic characteristics of ducted propellers caused by angle variations.  
Figure 12 shows the influence of the grid circumferential distribution range on the aerodynamic characteristics of the entire ducted propeller. The increase in the circumferen  
Figure 11: Aerodynamic performance of the duct at different angles of attack.  
Figure 10: Flow mechanism with grid channel on the duct.  
tial range of the grid has almost no effect on thrust, but both torque and lift increase with the expansion of the circumferential distribution range. This result indicates that embedding the grid into the duct wall does not significantly affect the thrust effect. However, due to the increased leakage range near the blade tip, the circumferential resistance of the blade increases, resulting in an increase in torque. Within a certain range, the increase in the distribution range of the circumferential grid leads to a further increase in the difference in force components between areas with and without grids, resulting in an increase in lift. From a distribution range of 10\({}^{\circ}\) to 30\({}^{\circ}\), the lift has nearly doubled. Figure 13 presents the cross-sectional cloud images of these two circumferential distribution angle ranges. It can be seen that when the circumferential distribution angle is 30\({}^{\circ}\), the grid induces a slight expansion of the low-pressure area on the inner wall of the duct at the front end, and the pressure difference in the low-pressure area also increases significantly, further increasing the lift caused by the difference in the effective component.
# A Multidisciplinary Approach for Improving Resource Efficiency in the Indian Surimi Supply Chain
### Seafood Sector in India  
India is a self-sufficient food-producing nation with an annual loss of 40% in its food sector [11] indicating its supply chain has a great deal of improvements to be made to comply with UN 2030 standards [12]. India is a major producer and exporter of food products, and food processing is recognised as a priority sector in their new manufacturing policy [13]. The food processing industry generates a variety of valuable Rest Raw Materials (RRM) from seafood, meat, and fruits and vegetables processing that go unutilized [4]. In the case of seafood, the RRM can include viscera, skin, filleting frame, bone, and wash water, which cannot be used in the main products but can potentially be used in the preparation of valuable ingredients like marine proteins and oil, protein hydrolyses, and gelatine for food or feed applications.  
India, with a coastline of over 7500 km and about 7 million hectares of inland water bodies, is a major supplier of seafood to the world [14]. During the COVID-19 pandemic, India experienced a shock in its production and export ability as restrictions were imposed on national and international trade [15], which resulted in a USD 1 billion drop in the export value of fish in 2020 [16]. However, not all seafood caught in India makes it to the frozen or even export stage. Currently, seafood not fit for sale is being underutilized, and large amounts are discarded or turned into low-value products while there is a huge potential for their utilisation into high value-added products.  
One of the biggest concerns when dealing with seafood is post-harvest loss, which can be found at nearly all stages of fish supply chains throughout the world. In coastal African countries, a study found that spoilage contributed to a loss of 10 to 12 million tonnes of fish per year, equivalent to around 10% of the total fish production along the coast of Africa [17]. Many factors attribute to loss, but inadequate cooling sources leading  
Figure 1: Waste hierarchy in WFD.  
to rapid spoilages stands out. Spoilage's biggest foe is low-income nations' insufficient access to quality cooling and preservation storage. This defeat accounts for fisher's loss of income, food insecurity growth, and forced losses for the process industry and market [18]. There are other fish supply chains around the world still able to prosper regardless of costs. One prominent fishing nation is Norway, where 33% of the world's salmon supply is produced. The industry here can bear the costs of farming fish and the price that comes with this type of aquaculture, such as diseased fish, infestation, and changes in water temperature [19]. Unlike salmon supply chains, the surimi supply chain is under pressure to evolve its practices.
## 3 Virtual Inductive Control Strategy  
### Definition of Instability  
For CPLs, the input power is equal to the required power of the CPLs. And the power characteristics can be given by  
\[P=V_{bus}i_{load}, \tag{11}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Magnitude** \\ \hline Input Voltage \(V_{S}\) & 100 v \\ Bus Voltage \(V_{bus}\) & 200 v \\ Filter Inductor \(L/R\) & 13.68 mH/0.4\(\Omega\) \\ Bus Capacitance \(C\) & 840 \(\mu\)F \\ Switching frequency \(f_{s}\) & 10 kHz \\ Droop coefficient \(R_{d}\) & 0.5 \\ Resistive load \(R_{dc}\) & 100 \(\Omega\) \\ Line inductor \(L_{t}\) & 0.5 mH \\ Proportional Coefficient of Current Controller \(k_{pi}\) & 0.5 \\ Integral Coefficient of Current Controller \(k_{ii}\) & 100 \\ Proportional Coefficient of Voltage Controller \(k_{pv}\) & 1.2 \\ Integral Coefficient of Voltage Controller \(k_{iv}\) & 250 \\ Filter Coefficient \(T\) & 0.0005 \\ Droop Coefficient \(R_{d}\) & 0.5 \\ virtual inductive \(L_{d}\) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of DC microgrid system.  
Figure 4: Equivalent steady-state model of a DC microgrid.  
And (12) can be obtained by small signal analysis.  
\[\Delta i_{load}=-\frac{P}{V_{bus}^{2}}\Delta v_{bus}, \tag{12}\]  
where \(\Delta i_{load}\) and \(\Delta v_{bus}\) are the perturbation of load current and bus voltage, respectively. \(V_{bus}\) is the steady-state bus voltage value. It can be noted from (12) that the input current of CPLs varies inversely with bus voltage at a multiple of \(P/V_{bus}^{2}\). Thus, the rise (decrease) of voltage corresponds to the decrease (rise) of current. That is why the CPLs cause instability, also known as negative impedance characteristics. It is noteworthy in (12) that the negative impedance characteristic effect of the CPLs on the bus voltage can be eliminated by offsetting the current change. The virtual inductive control strategy of this paper will be introduced in detail below.