esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Issue and Modeling Challenges  
The most obvious challenge in the grid neuron modeling is to generate the periodic hexagonal grid patterns of different scales and orientations from body movement input. The grid neuron activation is shown in the Figure 1.  
The neuron is like a bulb that turns on only when the agent comes within the firing range (grid ring); otherwise, it remains off. The firing locations constitute the hexagonal grid pattern. The firing intensity of the neuron inside the rings depends on the distance of the agent from the center of the ring. At the center, the neurons firing intensity will be the highest and vice versa. Each grid neuron possesses different orientations and spacing of firing locations. In Figure 2, the hexagonal grid pattern of different spacing and orientation is shown.  
As per the biological brain research, the grid neuron works in collaboration with the place neurons. The connectivity between the grid and the place neuron is shown in Figure 3. The place neuron learns the grid neurons activation value through forward connectivity and in turn activates the grid neurons during localization through backward connectivity.  
The task of a place neuron is to learn a unique location of a cognitive map in terms of code as it is activated in a single location in an environment [15; 21]. The challenge in the modeling of a place neuron is to learn the interference pattern of grid neurons without any ambiguity. The interference of two different activation patterns is shown in Figure 4, where a location is associated with a unique combination of the activations of two different grid neurons. If a place neuron simply learns the activation values of grid neurons as a grid code, then there can be the possibility of ambiguity. The case of ambiguity is shown in Figure 5, in which the two different interference patterns are shown correspond to the same grid code.  
Figure 1: Grid neuron activation.  
Figure 2: Grid activation patterns of different spacing and orientations.  
In previous works on navigation, a robot always considered as a single entity, but in the case of object handling tasks, there is a need to consider the motion of each individual body part. The integration  
Figure 4: Localization using overlapped grid patterns.  
Figure 5: Ambiguity in grid code.  
Figure 3: The connectivity of place and grid neuron.  
of the sensory perception of each individual body part in a single semantic map of an object is a challenging task.
## 2 Methodology Overview
### Calculation of AMS and RMS  
The calculation of an AMS given an ACS and the \(\mathbf{B}\) matrix have been thoroughly discussed in [1; 2; 9]. Since the AMS is a convex set composed of the mapping of all the points from the ACS, it can be easily computed by evaluating the produced moments at all the ACS vertices (extremes of inputs) and then determine the convex hull as the exterior of the AMS by the readily developed _conthull_ function in MATLAB(r)[9; 11; 12].  
The calculation of RMS is elaborated in [8]. The idea is to evaluate disturbance perturbed dynamic models at each trim point and find the hull of the moments of all the perturbed models as the disturbance required moment set, and then generate a set of linearly spaced query directions to evaluate the maneuver required moments, which is assembled with the former one. The result is a convex polytope in the moment space with vertices on the query directions as an unconservative estimation of all the required moments.
## 2 Related Work  
Many improvement methods have been proposed for prediction models based on the TCN. These methods can be roughly categorized into two main groups: (1) TCN-based combinatorial modeling methods and (2) TCN structure-based improvement methods. The former aims to apply new attention mechanisms and combinatorial networks on the TCN to improve the accuracy of prediction, while the latter is based on the network structure of the TCN and tries to improve the model's performance by improving the activation function, weight initialization, and other aspects.
# Applying Deep Learning to Medical Imaging: A Review
## 1 Introduction
### Challenges and Opportunities  
Despite the promising results achieved by DL in medical imaging, several challenges remain [47; 48; 49; 50; 51; 52]. One major challenge is the limited availability of annotated medical image datasets due to the time-consuming and costly nature of manual annotations [58]. Additionally, data privacy concerns and the sharing of sensitive patient information pose significant obstacles to the development of large-scale, multi-institutional datasets [59]. Another challenge is the interpretability of DL models, as they often act as \"black boxes\" that provide limited insights into their decision-making processes [60]. Ensuring the explainability and trustworthiness of these models is crucial for their adoption in clinical practice, as clinicians need to understand the rationale behind their predictions [61].  
Despite these challenges, DL in medical imaging presents numerous opportunities for advancing healthcare and improving patient outcomes. With ongoing research, interdisciplinary collaboration, and the development of more sophisticated algorithms, DL has the potential to revolutionize medical imaging and contribute significantly to the future of medicine.
## 3 Development of Thermal Condition Monitoring System
### Overview of WSNs and Communication Links Characteristics
#### 3.3.1 Attribution of Emerging Wireless Communication Links
LoRa has outstanding performance as compared to other modulation methods and has unique spread spectrum techniques, which deliver robustness against signal interference for long ranges and low signal-to-noise ratio for the receiver to be able to demodulate. LoRa is thus a suitable solution for systems that require a very long battery lifetime and reduced cost. Besides, its technical prowess, it suffers from less operational coverage in the rural and remotes areas due to low data rate and rare messages receipt acknowledgement [67; 69; 70]. Again, challenges on ISM spectrum application results as another disadvantage being identified when LoRa solutions are implemented, to operate intrinsically on an ISM band. Specifically, current international laws demand a stringent duty-cycle of 1%; LoRa falls short of that. This means the radio channel does not occupy more than 36s per hour. In reality, this value is denoted as the maximum transmission duty cycles (TDC) permissible by the nodes to operate in ISM channels [69]. In addition, LoRa consents to tuning of other physical transmission properties, such as the bandwidth and central frequency of the communication, hence the coding rate (CR). This impacts the ratio of the length of the packet and the error-correction code length. The transmission power and the spreading factor (SF) thus defines the symbol rate and chip rate. Higher SF values improve the sensitivity and range of communication at the detriment of increasing the over-the-air time of the packets, hence consuming more TDCs [65; 68; 69]. Furthermore, LoRa/LoRaWAN uses the adaptive data rate (ADR) algorithm in estimation of CR and SF parameters under specific channel mandate. Sigfox also belongs to the LPWAN group and is deployed on licensed network, thus performs well in its enactment territory but globally having challenges in operational expansion in relation to LoRa. The LPWAN characteristics are compared with ZigBee in the preceding subsections.
## 3 Results  
### Spectral Separability between Land Cover Classes  
The mean spectral reflectance curves extracted from the pixels of the respective LCLU classes in the two study areas are plotted with their standard deviations (SDs) (Figure 2). Overall, within 1 SD, the seven Landsat bands recorded considerable spectral overlaps of the five LULC classes. More spectral overlaps were exhibited by the visible bands (bands 1 to 4, i.e., coastal aerosol, blue, green, and red). The lowest reflectance values were recorded by the visible bands, followed by band 7 (Short-wave 2), 6 (Short-wave 1), and 5 (Near Infrared), respectively. Near Infrared (band 5) showed the largest difference in reflectance values between water and non-water bodies. Only Near Infrared and Short-wave 1 (bands 5 and 6, respectively) were helpful in separating water and the other classes, beyond 1 SD of uncertainty.  
Table 2 shows the spectral separability of the five LULC classes, i.e., water, other forests, mangrove forests, bare land, and agriculture, calculated from bands 1-7 of the Landsat OLI imagery using transformed divergence (TD) and the Jeffries-Matusita (J-M) distance. According to the results, for the Landsat OLI, ETM+ (2009), ETM+ (2000), and TM (1995) images, the TD index showed that all classes attained values higher than 1.9; therefore, they were all separable. The same applied to the J-M distance except for agriculture/other forests, which reported a separability value < 1.9. The Landsat TM (1990)  
Figure 2: Mean spectral response—extracted from the Landsat OLI imagery—of land-cover and land-use classes in the Chwaka and Menai study areas.  
image shows that, according to the TD index, other forests/mangrove forests and other forests/agriculture and J-M's other forests/mangrove forests, other forests/agriculture, and mangrove forests/agriculture did not attain the 1.9 separability threshold. The Landsat MSS image showed that for both the TD index and J-M distance, other forests/mangrove forests, other forests/bare land, other forests/agriculture, mangrove forests/bare land, mangrove forests/agriculture, and bare land/agriculture were not separable.
## 1 Introduction
In this study, we build a 48 V module simulation platform designed to replicate real-world applications and thoroughly evaluate SVC, K-means, and GMMs in terms of their ability to form well-balanced groups of second-life Li-ion cells. By simulating the module's operational metrics, such as the standard deviation in final cell charging voltages, energy throughput, and delta capacity over time, we demonstrate how different clustering strategies can significantly influence overall module performance and battery longevity. Our findings show that while all three methods have their merits, GMM-based clustering tends to yield more balanced cell groupings and better overall performance under a range of load scenarios.
## 6 Circuit Design
### DC/DC Conversion Circuit and Power Supply Circuit  
The DC/DC conversion circuit and power supply circuit are shown in Figure 13. In this study, the DC/DC conversion circuit can provide better stability to the proposed power system over wide ranges of input and output voltages, and enable more stable and accurate current limiting operation. A thermal shutdown in this circuit is implemented to prevent damages owing to excessive heat.  
Figure 12: Block diagram of the standalone renewable energy system by hybrid solar-wind system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Terminal Name** & **Description** & **Direction** \\ \hline P6.7 & Voltage monitoring & Input \\ P7.5 & Current monitoring & Input \\ P6.0 & Battery condition monitoring & Input \\ P6.1 & Wind turbine unloading & Output \\ P1.1, P1.2, P4.5, P4.6 & PWM control & Output \\ P4.0, P4.1, P4.2, P4.3 & PWM drive & Output \\ \hline \hline \end{tabular}
\end{table}
Table 8: Terminal names and general descriptions of the electrical interface of the control system.  
The output voltage of DC/DC conversion circuit can be set by external resistors and the resistors are calculated as follows.  
\[R_{2}=V_{ref}/70\mu A \tag{23}\]  
\[R_{3}=R_{2}\Big{(}12/V_{ref}-1\Big{)} \tag{24}\]  
where \(R_{2}\) and \(R_{3}\) are external resistors; \(V_{ref}\) is the reference voltage set inside the circuit (1.238 V). The resistances of \(R_{2}\) and \(R_{3}\) are 18 K\(\Omega\) and 156 K\(\Omega\), respectively. Other relevant parameters of the DC/DC conversion circuit can be seen in Table 9.  
The power supply circuit includes a primary voltage-regulator and a secondary voltage-regulator. Primary voltage-regulator is designed by LT1129 (Analog Devices Inc., Norwood, MA, USA), which can generate 3.3 V at supply of 12 V for MCU, some sensors and some detection circuits in this power system. LM78M05 (Texas Instruments, Dallas, TX, USA) is selected as the core of the secondary voltage-regulator, which can generate 5 V at supply of 12 V to fulfill the requirements of the amplifier. Relevant parameters of the power supply circuit can be seen in Table 10.  
Figure 13: The DC/DC conversion circuit and power supply circuit.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Unit** \\ \hline \(C_{1}\) & 1 & \(\mu\)F \\ \(C_{2}\) & 1 & \(\mu\)F \\ \(C_{3}\) & 3.3 & nF \\ \(C_{4}\) & 0.1 & \(\mu\)F \\ \(C_{5}\) & 10 & \(\mu\)F \\ \(R_{1}\) & 13 & K\(\Omega\) \\ \(R_{2}\) & 18 & K\(\Omega\) \\ \(R_{3}\) & 156 & K\(\Omega\) \\ \(L_{1}\) & 3.3 & \(\mu\)H \\ \hline \end{tabular}
\end{table}
Table 9: Relevant parameters of the DC/DC conversion circuit.
## 5 Performance Analysis
### Discussion  
The jitterless task execution achieved by designing perfectly periodical scheduling for all the criticality levels in an MCS brings advantages in applications regarding message synchronization, signal processing, control applications, or simply different types of certifications. Having jitterless  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Criticality Mode**} & \multirow{2}{*}{**Task**} & \multicolumn{4}{c}{**Jitter Value**} \\ \cline{3-6}  & & P\_FENP\_MC & P-EDF-VD & TT-Merge & **Energy-Efficient TT-Merge** \\ \hline \multirow{2}{*}{**Lo**} & \(M_{1}\) & 0 & 0 & 5 & 0 \\  & \(M_{2}\) & 0 & 4 & 0 & 2 \\  & \(M_{3}\) & 0 & 1 & 1 & 1 \\ \hline Hi & \(M_{1}\) & 0 & 0 & 5 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Jitter values of the task set example scheduled using four algorithms: P_FENP_MC, P-EDF-VD (non-preemptive variant), TT-Merge, and Energy-efficient TT-Merge.  
Figure 10: Scheduling of the task set example using four methods: Partitioned Fixed Execution Non-Preemptive Mixed Criticality (P_FENP_MC), Partitioned Earliest Deadline First with Virtual Deadlines (P-EDF-VD), a non-preemptive variant, Time-Triggered Merge (TT-Merge), and Energy-efficient Time-Triggered Merge (Energy-efficient TT-Merge).  
task execution and respecting the time constraints imposed by MCSs, we have full determinism and predictability regarding task execution.  
Still, the tradeoff for jitterless scheduling on a uniprocessor is a lower success ratio value compared to using an event-driven method. An algorithm such as EDF-VD can reach up to 80% success ratio for a total utilization factor of 1 for the lowest criticality mode [13]. However, comparative results are harder to obtain with a time-triggered algorithm without using any resource enhancements, such as frequency scaling, for instance [10; 12].  
For a multiprocessor platform, the success ratio is not only influenced by the scheduling algorithm but also by the function used to map tasks to processors (see Figure 8). If we use a proper partitioned mapping function, we have comparative results between time-triggered and event-driven schedulers in terms of success ratio. As illustrated in Figure 8, our proposed scheduling algorithm obtained better schedulabilitily results by using a well-tailored partitioned function in comparison to P-EDF-VD (with an FFD partitioned mapping function).
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Propagation Models for Simulation  
In V2V and V2I communication, propagation models are fundamental in defining the channel properties. Path loss and signal fading are two propagation parameters that alter the signal intensity as it travels from the source to the destination in a wireless medium. The Friis path loss/FreeSpace, two-ray ground, log distance path loss, Nakagami, Rician, Rayleigh, range propagation, and shadowing models are all used in this study.
## 1 Introduction  
### Motivation  
In several modern applications, it is often necessary to estimate the state of a system, given a mathematical model for the system and a stream of noisy observations. Particle Filters (PFs) are typically used in this context. The key idea is to sample \(N\) hypotheses (i.e., particles) from an arbitrary proposal distribution to approximate the probability density function (pdf) of the true state. However, at some point, the particles experience a numerical error, called particle degeneracy, which makes the estimates diverge from the true state. A _resampling_ algorithm is then applied to correct for degeneracy by replacing the particles that are diverging from the true state with copies of the particles that are not doing so [1]. This sampling-resampling approach is highly flexible, such that PFs find application in a wide range of fields, ranging from machine learning [2] to medical research [3], fault prediction [4], weather forecasting [5], tracking [6] or, broadly speaking, any domain involving decision making in response to streaming data. Resampling is also used in other Monte Carlo methods, such as sequential Monte Carlo samplers [7, 8] and PHD filters [9]. For brevity, this paper focuses exclusively on PF contexts.  
Modern efforts of making models more detailed have translated to an increasing demand in making PFs more accurate. This demand can be satisfied in several ways, ranging from applying better proposal distributions [10] to collecting more measurements [11] and using more particles [12, 13]. Using more particles is especially important in settings where we are more interested in computing the probability that the true state falls within a certain state-space region, rather than simply estimating its mean [14]. However, the likely side-effect of any of these approaches is a significant increment to the run-time, and this may be critical, especially in real-time applications [12]. Parallel computing becomes necessary in order to compensate for this side-effect.
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 3 Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective  
In this section, identification and assessment of drought will be described through analyzing changes in different climatological and ecological variables.
# An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks
## 4 Energy Balanced and Lifetime Extended Protocol (EBLE)
### Problem Description
: _If there is only one relay node, multi-hop transmission is no longer energy efficient when \(d_{SR}^{k}a(f)^{d_{SR}}+d_{RD}^{k}a(f)^{d_{RD}}>d_{SD}^{k}a(f)^{d_{SD}}\)._  
Proof of Theorem 2.: From Equations (14) and (15) and we set \(E_{multi-hop}>E_{direct}\). We can obtain (20)  
\[\frac{LP_{0}}{Ra}(d_{SR}^{k}a(f)^{d_{SR}}+d_{RD}^{k}a(f)^{d_{RD}}-d_{SD}^{k}a( f)^{d_{SD}})+(m-2)\frac{LP_{rx}}{Ra}>0 \tag{20}\]  
As \(m\geq 2\), the inequality is always reasonable when \(d_{SR}^{k}a(f)^{d_{SR}}+d_{RD}^{k}a(f)^{d_{RD}}>d_{SD}^{k}a(f)^{d_{SD}}\). So it is proved that if there is only one relay node, multi-hop transmission is no longer energy efficient when \(d_{SR}^{k}a(f)^{d_{SR}}+d_{RD}^{k}a(f)^{d_{RD}}>d_{SD}^{k}a(f)^{d_{SD}}\). To further explain the energy consumption in UWSNs, we assume that \(d_{SR}+d_{RD}=\alpha d_{SD}\). Figure 4 illustrates the energy consumption for one-hop direct transmission and multi-hop transmissions with different \(\alpha\). The results show that the multi-hop transmission mode is often more energy efficient than one-hop direct transmission especially when the relay node is close to the middle position between the source and the destination. However, when \(d_{RD}+d_{SR}\) is too large, the multi-hop transmission scheme is not energy efficient any more no matter where the relay node is. So we need to consider the relative distance to the source and destination when choosing forwarding nodes and designing the cost function. The detailed design for choosing the next forwarding nodes is illustrated in Section 4.2.
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 5 Experiments
### Randomness  
To test if the covert channel can be detected by using a random number test suite, the exchanged passwords (with or without CC) were collected into a file of 256 kibibytes (MD5) or 512 kibibytes (SHA-3), respectively. Then we submitted the files to the NIST [44] suite. The results from test 1 of the NIST suite (frequency of zeroes and ones should be close to 0.5 for each), given as p-values, can be seen in Table 2. As the \(p\)-values are close to 1, all sequences seem random.  
Yet already the results from test 2 (cumulative sums) differ for the sequences, cf. Table 3. Thus we conclude that at least the CC without encryption might be detectable. This hope is fostered because in total 15 tests are available in the NIST suite, so there are more chances to detect CCs than we have explored. However, there is also a downside. Some of the tests need much more data than we provided, and already these data might need a long time to be collected. If we imagine that each minute an authentication takes place, then 11.3 days are needed to collect the data. Indeed, this assumption is only valid if authentications take place during night and day without any interruption or reduction of frequency. If limited to eight-hour working days Monday to Friday, it would take almost 7 weeks instead. Thus we have a tradeoff between chance of detection and time to detection.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### Candidate Positions  
In the proposed strategy, we define candidate positions (CPs) as pole positions of the electric power distribution network, including poles with distribution automation equipment attached to them. They are called candidates because their promotion to a router or gateway position will depend on the existence of SMs connected to their position at the end of the planning process.  
First, the method prioritizes the selection of poles with DA devices, as these equipment need to be directly connected to the backhaul's communication network. After that, regular poles are considered to connect the remaining smart meters.  
Using poles as candidate positions is justified because these elements are part of the energy distribution company's asset list and can be easily configured to meet the technical requirements for installing routers and gateways.  
Considering the irregularity of the regions' terrain and the high number of existing poles in each city, the AIDA method uses a grid approach to make an optimized selection of a subset of poles to minimize the computational effort required to choose the most ideal coordinates for positioning the key devices. More details about this process are presented in Section 4.4.3.