esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy-Regularized Federated Optimization for Non-IID Data
## 6 Discussion  
### Fixed Entropy Regularization Schedule  
One key insight from our experiments is that a moderate entropy regularization strength in the range of \(5\times 10^{-4}\)-\(10^{-3}\) yields robust performance across very different federated settings. In the UNSW-NB15 intrusion detection experiments, we employed a _decayed_ schedule starting from  
\[\lambda_{0}=10^{-3},\]  
and reducing it linearly to 0 over 100 communication rounds. In the PneumoniaMNIST image classification experiments, we used a _fixed_ coefficient of  
\[\lambda=5\times 10^{-4}\]  
throughout all rounds.  
Empirically, this moderate, task-agnostic choice of \(\lambda\) improved minority-class recall in the highly skewed network intrusion task while not degrading performance on the easier pneumonia detection task. In other words, we did not need to finely tune \(\lambda\) per dataset or dynamically adjust it at each round; a single, intermediate value provided benefits in one scenario and remained safe in the other.  
This finding suggests that ERFO's entropy regularization is not overly sensitive to the exact value of \(\lambda\) in practice. By promoting a modest amount of entropy, client updates avoid extreme concentration (which drives drift) without being overly damped (which would undercut local learning). As a result, we did not perform exhaustive hyperparameter sweeps for \(\lambda\) in our main results, unlike some federated methods that demand careful per-application tuning. This simplicity enhances ERFO's practical appeal, reducing the hyperparameter-tuning burden for new deployments. We acknowledge that an optimal \(\lambda\) may vary in more extreme settings (e.g., very large models or large numbers of clients), but our results demonstrate that a single, fixed coefficient in the \(10^{-4}\)-\(10^{-3}\) range is effective across at least two markedly different tasks.  
An interesting direction for future work is to derive guidelines or theoretical bounds for choosing \(\lambda\) or to develop adaptive schemes that adjust \(\lambda\) based on observed trainingdynamics--although in our experiments such adaptation proved unnecessary for robust, cross-task performance.
## 2 Methodology
### Grey Prediction Modelling
#### 2.2.3 Multiple Grey Prediction  
For the multiple prediction strategy shown in Figure 5, the sampling period is the same as that of multi-step prediction, which is set at 5 s. Compared with the first two predicting strategies, the remarkable feature of this strategy is that the input model sequence of this strategy covers the power demand data of 6 time points and expands the prediction number to 3 times; that is, the power demands of \(\text{t}_{\text{m+1}}\), \(\text{t}_{\text{m+2}}\) and \(\text{t}_{\text{m+3}}\) are predicted. Specifically, the prediction of \(\text{t}_{\text{m+1}}\) is calculated based on a series of power demands at six time points from \(\text{t}_{\text{m-5}}\) to \(\text{t}_{\text{m}}\). The power demand at 6 time points is selected for each step to ensure that the method is consistent with the above two strategies when predicting the power demand at \(\text{t}_{\text{m+3}}\) time and that there are definite data for 4 known moments.
## 5 Feasibility Study Through System Simulations
### Scenario for Simulations, Area Intended for Positioning, and Positioning Area
The positioning area is colored with red, green, and blue. The base stations are depicted in the figure with four triangles numbered from one to four and are located in this scenario in the positions listed in Table 7.  
Figure 12: Scenario layout depicting the positions of four base stations (triangles) and the area intended for positioning. As an example, the selected size of the tiles results in a positioning area equivalent in size to the area intended for positioning. The positioning area is fully covered by fixed tiles (colored region) and shows one snapshot of the radio channel state. Each sub-tile has a vector of fingerprint elements from the four base stations associated with it, which is color-coded with the RGB and opacity channels. In the example shown, all of the tiles cover the area intended for positioning with \(N_{SS}=10\), and \(S_{S}=50\) mm. Figure 12 depicts an example in which the whole positioning area is covered by fixed tiles of the size 500 mm by 500 mm, with \(N_{SS}=10\), and \(S_{S}=50\) mm, to exemplify the tiling of the area intended for positioning (the creation of the actual positioning area). As described in Section 4.2, each sub-tile has a vector of fingerprints associated with it. Here, the elements in the vector of fingerprints are the RSS values received in downlink from the four base stations. Aligned with this, in Figure 12, each sub-tile is color-coded with the RGB channel colors and the opacity from the alpha-channel to represent the fingerprints from each one of the four base stations. The signal strength from base stations 1, 2, and 3 is coded in the colors red, green, and blue, respectively. The signal strength from base station 4 is coded as the opacity from the alpha-channel. The color strength or opacity decreases with the distance from each base station, analogous to the attenuation in the RSS of the radio signal. In addition, the RSS contains a random component that models the shadow fading. In the figure, this random component is depicted by the lack of gradual uniformity in the continuity of the colors between adjacent sub-tiles. The color coding is used to create a visual aid showing that the fingerprints vary with the position and the shadow fading. This example shows one snapshot in time of the time-varying radio channel state. Hereafter, we refer to this snapshot in time as _radio channel realization_.
## 3 Research Method and Process
### Numerical Model Construction and Experiment
#### 3.1.3 Simulation Test Process  
First, according to the physical and mechanical test data of the rock and soil layers of representative geological boreholes in the study area, the parameters of each numerical model are assigned (Table 2); second, the boundary conditions of each numerical model are set in the FLAC\({}^{\text{3D}}\) version 6.0 software. The front, rear, left, and right interfaces of the model are unidirectional constrained boundaries; the lower boundary is a fully constrained boundary, and the upper interface is a free boundary. Third, the initial equilibrium of each numerical model is achieved by running the calculation; fourth, the gradual excavation test is carried out for each numerical model, and 20 m is excavated at each step until full mining is achieved. Fifth, the amount of subsidence and horizontal movement of the model surface slope that reaches the full mining state is extracted.
# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 4 Cluster-Based Channel Allocation Method
### Adaptive Module
Therefore, in this case, a cloud server constructs a graph \(G(t)=(N(t),E(t),W(t))\) by using the locations of the WBANs in \(N(t)\) and calls the base module to determine the set \(C(t)=\{C_{1}(t),\cdots,C_{|\mathcal{C}(t)|}(t)\}\) of clusters and the channels for the WBANs in each cluster \(C_{i}(t)\). The adaptive module is summarized in Algorithm 2. ```
1:Input:
2:\(C(t-1)=\{C_{1}(t-1),\cdots,C_{|C(t-1)|}(t-1)\}\), \(\vec{p}(C_{i}(t-1))\), \(\forall C_{i}(t-1)\in C(t-1)\),
3:\(N(t)\), and \(\vec{p}(\
u)\), \(\forall\
u\in N(t)\)
4:Output: a channel allocation vector \(\alpha(t)\)
5:\(i=0\)
6:while\(i\leq|C(t-1)|\)do
7:\(Y=\mathbb{C}_{i}(t-1)\)
8:while\(\
u_{i,j}\in Y\wedge Y\
eq\emptyset\)do
9:if\(\
u_{i,j}\
otin N(t)\)then
10:\(C_{i}(t-1)=C_{i}(t-1)-\{\
u_{i,j}\}\)
11:\(Y=Y-\{\
u_{i,j}\}\)
12:\(C_{i}^{\prime}(t)=C_{i}(t-1)\)
13:\(i++\)
14:\(Z=N(t)\)
15:while\(v\in Z\wedge Z\
eq\emptyset\)do
16:\(C_{x}^{\prime}(t)=\operatorname*{argmin}_{C_{i}(t-1)\in C(t-1)}||\vec{p}( \
u)-\vec{p}(C_{i}(t-1))||_{2}\)
17:if\(v\
otin N(t-1)\)then
18:\(C_{x}^{\prime}(t)=C_{x}^{\prime}(t)\cup\{v\}\)
19:else
20:if\(\
u\in C_{i}(t-1)\wedge C_{i}(t-1)\
eq C_{x}^{\prime}(t)\)then
21:\(C_{x}^{\prime}(t)=C_{x}^{\prime}(t)\cup\{v\}\)
22:\(Z=Z-\{v\}\)
23:ifmodularity of \(C^{\prime}(t)=\{C_{1}^{\prime}(t),\cdots,C_{|C(t-1)|}^{\prime}(t)\}<\delta_{m}\)then
24:call Base Module
25:else
26:\(i=0\)
27:while\(i\leq|C(t-1)|\)do
28:if\(C_{i}^{\prime}(t)\
subseteq C_{i}(t-1)\)then
29: allocate a channel for each WBAN in \(C_{i}^{\prime}(t)\) (line 7 \(\sim\) line 17 in Algorithm 1)
20: update center point of each cluster \(C_{i}^{\prime}(t)\)
21:\(i++\)
```  
**Algorithm 2** Adaptive Module
# Organic Nanostructured Materials for Sustainable Application in Next Generation Solar Cells
### The Operatzon Principle of OPVs
#### 1.2.2 Exciton Diffusion and Charge Dissociation  
The second step involves the energy offset in LUMO between donor and acceptor materials which breaks the Coulomb attraction that ultimately causes the excitons to dissociate [34]. As most of the conjugated polymers exhibit a shorter lifetime of the excitons, the diffusion lengths are limited to a few nanometres, less than 20 nm, which is much shorter than the optical absorption pass length that is approximately 100-200 nm. It is, therefore, a prerequisite that excitons must be generated within their diffusion length (LD) for efficient charge generation [35]. Exciton diffusion length is defined as the distance travelled by an exciton before recombination (see Figure 4b) [36]. The reported excitons diffusion length for various conjugated polymers (of free carriers) significantly varies from 5 to 20 nm [25]. Thus, the thickness of the photoactive layer is very much critical for an efficient charge generation. [PERSON] and colleagues have recently released their work which demonstrates that single-material OPVs are being revived, not only because of their conceptual simplicity and considerably increased stability, but also because they may be used to learn about exciton dissociation and charge separation mechanisms. The production of intermediate inter-molecular charge-transfer (CT) excitons at the homojunction of a single p- and n-type doped organic semiconductor or at the interface of undoped domains with differing molecular orientation and packing has been shown to aid exciton dissociation in such devices [37].
## 2 Modeling and the Proposed Method
### Estimation of Attitude Angle  
In order to obtain the attitude angle, the edge in the 2D TF spectrum needs to be extracted. Before the edge detection, threshold processing is needed to reduce the error of extraction. The update of the threshold in this work is adaptive based on the least square method. In the first step, the parameters in this proposed algorithm need to be determined. To be specific, the maximum value of the 2D TF spectrum \(g_{max}(t,f)\) at time \(t\) corresponds to the Fourier transform of the echo signal. The Fourier transform of noise is relatively small. The mean value is denoted as \(g(t,f)\). Accordingly, the threshold \(\chi\) is expressed as  
\[\chi=J\times[g_{max}(t,f)] \tag{9}\]  
Here, \(J\) denotes the coefficient and ranges between 0 and 1. The initial value is set as \(J=0.1\). During the iteration, the threshold is updated as  
\[J=kn+b \tag{10}\]  
Here, \(k\) denotes the slope, \(n\) is the step, \(b=0.1\) is the intercept. The optimization function is expressed as  
\[\Xi=min\Big{[}(\hat{\alpha}_{n}-\alpha)^{2}\Big{]},n=1,2,\ldots,N \tag{11}\]Here, \(\hat{\alpha}_{n}\) represents the estimated value of attitude angle, and \(\alpha\) is the real value of attitude angle. \(N\) is the maximum number of iterations; that is, the threshold value \(\chi\) is optimized by the least square between \(\hat{\alpha}\) and \(\alpha\). According to the calculation, \(J=0.5\) is a suitable value.
# Instant Center Identification of Single-Loop Multi-DOF Planar Linkage Using Virtual Link
## 3 Virtual Link Method for Instant Center Identification
### Criteria for Instant Center Identification
#### 3.1.1 Coupled Loop Chain Characteristic  
In the view of loop chain, the instant centers of the Stephenson linkage (Figure 3) can be located as follows. Firstly, three loop chains are found: loop 1-2-3-4, loop 1-4-3-5-6, and loop 1-2-3-5-6. The coupled loop chain 3-5-6-1 is the common part of two loops if the loop 1-4-3-5-6 and loop 1-2-3-5-6 are grouped. For the loop 1-2-3-5-6, there exists one instant center \(\text{I}_{13}\)1. However, for the other loop 1-4-3-5-6, the other instant center \(\text{I}_{13}\)2 can also be obtained. Only when the instant centers \(\text{I}_{13}\)1 and \(\text{I}_{13}\)2 come cross, can the Stephenson linkage be formed. In other words, only if the common instant centers \(\text{I}_{13}\)1 and \(\text{I}_{13}\)2 of two loops coincide, the Stephenson linkage exists. In the loop 1-2-3-5-6 and loop 1-4-3-5-6, the instant centers \(\text{I}_{13}\)1 and \(\text{I}_{13}\)2 are all A type third instant centers, which lie on the extension lines of link 2 and link 4, respectively. That is, the instant center \(\text{I}_{13}\) occurs at the point of the intersection of the two line when the instant center \(\text{I}_{13}\)1 and \(\text{I}_{13}\)2 coincide. Similarly, if combining the two loops: loop 1-2-3-4 and loop 1-4-3-5-6, the same result can be obtained. This property, here called coupled loop chain characteristic, is discussed and demonstrated in Refs. [11; 12; 13; 14; 18].
## 3 Results
### Question 4: How to Collect Data
#### 3.4.3 Simulation  
In addition to on-site measurement and questionnaire surveys, simulation is another method used to collect data while developing IEQ models, especially when predicting IEQ performance in the design stages [74; 76]. According to [PERSON] et al. [81], data generated through simulations needs to be more consistent with the results collected through the questionnaire, to a substantial extent. In simulations, more building-related indicators that might influence IEQ performance (such as shape, window size, lighting locations, the number of sound absorption panels, HAVC systems, etc.) could be analyzed since computers could simulate more scenarios. For instance, an extensive database with 15,800 cases was created by [PERSON] and [PERSON] [76] using simulations, where eight input indicators with wide ranges (e.g., the temperature range varied from \(-8.8\)\({}^{\circ}\)C, representing Moscow in January, to 42.9 \({}^{\circ}\)C, representing Abu Dhabi in August) were considered. Using this database, they developed a set of individual models to predict the performance of the four aspects of IEQ, along with an integrated model to calculate the overall IEQ index, which was proven to be very accurate (R\({}^{2}\) > 0.9).
###### Abstract  
Aiming at the problem that the optical link may be too expensive or even impossible to achieve in a large number of locations in the central part of the backhaul line, the proof-of-concept (PoC) verification of a millimeter-wave integrated heterogeneous network (HetNet) is proposed. HetNet includes a traditional macrocell network and a new small unit that uses a millimeter wave for backhaul line and link access. The concept of a segmentation control plane and user plane was introduced. In the HetNet integrated millimeter wave, the control plane and the user plane were segmented to support the uninterrupted connection and enhance the capacity of the millimeter wave small base station. Millimeter wave communication could be used not only for access links, but also for wireless backhaul links, which will facilitate the installation of small millimeter wave cells. Through conceptual verification (PoC), the feasibility of millimeter-wave integrated HetNet prototype with millimeter wave technology used for return lines and link access is proved.  
5G cellular networks; millimeter wave; heterogeneous networks; backhaul; conceptual verification +
Footnote †: journal:
## 3 Results and Analysis
### Spatial Distribution Characteristics of Soil Nutrients  
#### 3.2.1 Determination of the Best Geostatistical Fitting Model  
Before diving into geostatistical analysis and the Kriging interpolation method, it was imperative to compute the parameters of the semivariogram functions across various fitting models. This was carried out to discern the optimal fitting model. As highlighted in Table 3, cross-validation was conducted on the predictive errors of these diverse models. The accuracy of a model elevates when the mean predictive error and standard mean converge to 0, the root mean square error is at its minimal, and the standard root mean square error and the average standard error are closely aligned [28; 29]. From our analysis, the soil pH aligns best with the Gaussian model. In contrast, metrics like organic matter, total nitrogen, and rapid-acting potassium fit optimally with the spherical model. Lastly, the exponential model proved to be the best fit for alkali-hydrolyzable nitrogen, available phosphorus, and slow-acting potassium.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Index** & **Min** & **Max** & **Mean** & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Median** & **CV/\%** & \begin{tabular}{c} **Distribution** \\ **Pattern** \\ \end{tabular} & **Skewness** & **Kurtosis** \\ \hline pH & 5.03 & 8.20 & 6.68 & 0.55 & 6.72 & 8.22\% & \begin{tabular}{c} Normal \\ Distributions \\ \end{tabular} & 0.10 & 3.71 \\ Organic matter & 7.40 & 40.13 & 25.04 & 5.27 & 25.54 & 21.04\% & \begin{tabular}{c} Normal \\ Distributions \\ \end{tabular} & \(-\)0.53 & 4.19 \\ Total N & 0.68 & 2.96 & 1.51 & 0.31 & 1.56 & 20.53\% & \begin{tabular}{c} Normal \\ Distributions \\ \end{tabular} & 0.44 & 6.79 \\ Alkalibrolyzed & 66.07 & 192.73 & 129.88 & 25.26 & 134.51 & 19.45\% & \begin{tabular}{c} Normal \\ Distributions \\ \end{tabular} & \(-\)0.39 & 2.77 \\ Olsen-P & 1.70 & 71.31 & 28.96 & 17.12 & 26.00 & 59.12\% & \begin{tabular}{c} Log-normal \\ distribution \\ \end{tabular} & 0.43 & 2.39 \\ Available K & 51.13 & 260.45 & 141.94 & 49.11 & 137.23 & 34.59\% & \
\begin{tabular}{c} Log-normal \\ distribution \\ \end{tabular} & 0.37 & 2.21 \\ Slowly released K & 65.04 & 439.27 & 238.33 & 71.75 & 238.69 & 30.11\% &
\begin{tabular}{c} Log-normal \\ distribution \\ \end{tabular} & 0.36 & 3.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistical characteristics of soil nutrients in the study area.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 3 CubeSat Power Supply Model
### Lithium Battery Charge and Discharge Model  
Lithium-ion batteries have become a popular choice for CubeSat energy storage sources in recent years due to their high energy density and long working life. In this paper, the battery model is added to the scheduling model, and the remaining capacity of the lithium battery is expressed as [29]:  
\[E_{B,t}=E_{B,t-\Delta t}+I_{ch,t}P_{ch,t}\gamma\Delta t-I_{dis,t}\frac{P_{dis,t }}{\gamma}\Delta t \tag{2}\]  
where \(\gamma\) is the charging and discharging efficiency of the lithium battery; \(t\) means period \(t\); \(E_{B,t}\) is the remaining capacity of the lithium battery; \(I_{ch,t}\) is the 0-1 state variable, and when it is 1, it means that the battery is in the charging state; \(I_{dis,t}\) is the 0-1 state variable, when it is 1, it means that the battery is in the discharge state; \(P_{ch,t}\) is the charging power of the battery; \(P_{dis,t}\) is the discharge power of the lithium battery; \(\Delta t\) is the duration of each unit period.
## 4 Identification of Faulty Events
### Phase-A to Ground Fault  
A fault on phase-A involving ground (AG fault) was simulated as an incident at the sixth cycle. The currents recorded on the node 650 processed using the algorithm and results for a period of 0.2 s (12 cycles) are described in Figure 4. It can be seen from Figure 4a that the current corresponding to phase-A increases after the AG fault whereas the change in the currents of phase-B and phase-C is very small. Figure 4b depicts the WD-index corresponding to the phase-A with high magnitude when there is the incident of a fault.The magnitude of this index corresponding to the phases-B & C is low after the incidence of a fault. Figure 4c illustrates the ALN-index which has a high magnitude corresponding to all the phases at the instant of fault incidence. Figure 4d illustrates the fault index. It can be inferred from this figure that the magnitude of FI corresponding to the faulty phase (phase-A) is high compared to the threshold, whereas the magnitude of this FI corresponding to the healthy phases (phase-B & C) is low compared to the threshold. Hence, the algorithm proposed in this paper effectively detects the AG fault and also identifies the healthy and faulty phases.
# Food Allergies: Immunosensors and Management
## 3 Peanut Allergy
### Immunosensor  
Food hypersensitivity are serious resistant reactions, interceded by immunoglobulin E (IgE), to explicit food sources [35]. More than half of the plant allergens, including peanut allergens, have a place within four classifications of protein, including the prolamin superfamily, cupin superfamily, profilins, and Bet v-1-related proteins. The nut species of Arachis hypogaea accounts for 17 allergens with Ara h 4, which has been renamed Ara h 3.02, accounting for an overall number of 16 of the perceived allergens in nut seeds (Allergen Nomenclature Sub-Committee of the World Health Organization/International Union of immunological Societies, WHO/IUIS) [36]. Ara h 2 and Ara h 6 (prolamin classification, 2S egg whites family) and Ara h 1 and Ara h 3 (cupin classification) are viewed as significant allergens in peanuts, affecting >50% of allergenic individuals. Ara h 2 is viewed as the most powerful allergen protein because it is demonstrated by serum IgE tp affect over 90% of nut-allergenic patients. Ara h 6 offers comparative immunological highlights alongside Ara h 2, something that was demonstrated when as it was noticed that eliminating Ara h 2 and Ara h 6 allergen proteins together led to an overall decrease in the effector action of the nut seeds' rough protein extricate, highlighting the purposes behind our decision to examine these two homologs in this study. Further, this underlines the attractiveness of these homologs in restorative applications [37; 38]. Until this point in time, the WHO/IUIS Allergen Nomenclature Sub-Committee ([[http://www.allergen.org](http://www.allergen.org)]([http://www.allergen.org](http://www.allergen.org)), accessed on 20 April 2021), the solitary assortment of specialists approved to appoint official allergen assignments, perceives 16 nut allergens.