esa-sceva/satcom-chunk-collection huggingface dataset:
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 3 Heterogeneity-Aware Federated Optimization Framework
### Heterogeneity-Aware Weighted Aggregation Strategy  
Based on the heterogeneity quantification mechanism proposed in Section 3.2, each client can compute a heterogeneity metric (HM) after local training in each round by leveraging the statistical information of intermediate feature representations. This metric reflects the dispersion of the client's data distribution in the feature space. The heterogeneity metric not only serves as a part of local training constraints through the loss function but is further utilized to optimize the global model aggregation strategy.  
During the aggregation phase, we propose a heterogeneity-aware weighted aggregation strategy, which integrates both the local dataset size and heterogeneity information of each client. Specifically, for client \(k\), the aggregation weight of its model parameters is determined by its local sample size \(n_{k}\) and the heterogeneity metric \(HM_{k}\), calculated as follows:  
\[\omega_{k}=\frac{n_{k}}{1+HM_{k}} \tag{9}\]  
The core idea of this mechanism is that clients with more stable and internally consistent data distributions are more reliable and thus should receive a greater aggregation weight. Conversely, clients with highly dispersed data and unstable training dynamics should have their influence on the global model suppressed to improve the overall convergence and generalization of the global model. This strategy maintains the communication efficiency of federated learning while dynamically modeling the quality of client data, thereby enhancing the robustness of the federated system under heterogeneous environments.  
This chapter has presented a comprehensive solution under the framework of heterogeneity-aware federated optimization, aiming to balance non-IID sensitivity and federated learning performance. Starting from client-side feature modeling, a heterogeneity quantification mechanism was established using statistical characteristics (e.g., mean and variance) to measure the divergence in client data distributions. Building upon this, a multi-loss function was introduced that integrates cross-entropy loss, heterogeneity loss, center constraint, and L2 regularization, enabling the local training process to adaptively respond to data heterogeneity. Furthermore, a heterogeneity-weighted global aggregation strategy was proposed, where each client's heterogeneity metric (HM) serves as a weighting factor to regulate the model fusion process. This effectively mitigates the performance fluctuations caused by distribution inconsistencies among clients. The overall framework significantly enhances the training stability and generalization ability of federated learning in heterogeneous environments, while preserving privacy and communication efficiency.
## 4 Resonance Transition Periodic Families in the CRTBP Model
### Summation  
In the CRTBP model, the families \(IB(p-(p+1))\) connect the \"Branch-II\" of the interior \(p\):\((p-1)\) resonance with the \"Branch-I\" of the interior \((p+1)\):\(p\) resonance. The families  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Orbit/Name** & \(\mathbf{x_{0}}\) & \(\hat{\mathbf{y_{0}}}\) & **T/2** & **C** \\ \hline _RTPF-3:2(a)-orbit (\(\mu=5\times 10^{-4}\))_ & 0.996693105698827 & \(-\)0.606721682695370 & 13.572632053631988 & 2.986678114083724 \\ _RTPF-3:2(b)-orbit (\(\mu=5\times 10^{-4}\))_ & 0.839807356007294 & 0.324698985902574 & 17.371772692048165 & 2.985500211312612 \\ _RTPF-4:3(a)-orbit (\(\mu=10^{-4}\))_ & 0.999071125547079 & \(-\)0.498067952701473 & 17.147437111884469 & 2.992921442199951 \\ _RTPF-4:3(b)-orbit (\(\mu=10^{-4}\))_ & 0.872399628274439 & 0.253293221227850 & 21.274174849052791 & 2.990627628838839 \\ _RTPF-2:3(a)-orbit (\(\mu=\mu_{S1}\))_ & 1.003548207343015 & 0.656988112184208 & 16.124846254286862 & 2.989316084875661 \\ _RTPF-2:3(b)-orbit (\(\mu=\mu_{S1}\))_ & 1.240062798333267 & \(-\)0.432604173138644 & 21.223783359411804 & 2.969522450671411 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Initial states of the example _RTPOs_ shown in Figure 11.  
Figure 11: **(a)** Example orbit in _RTPF-3:2(a)_; **(b)** Example orbit in _RTPF-4:3(a)_; **(d)** Example orbit in _RTPF-4:3(b)_; **(e)** Example orbit in _RTPF-2:3(a)_; **(f)** Example orbit in _RTPF-2:3(b)_. The initials of these example orbits are given in Table 2.  
\(OB(p-(p+1))\) connect the \"Branch-I\" of the exterior \((p-1)\):\(p\) resonance with the \"Branch-II\" of the exterior \(p\):\((p+1)\) resonance. This genealogy of the periodic families has already been pointed out by some previous studies [4, 11, 31, 32].  
With the mass parameter \(\mu\) increasing, starting from larger values of \(p\), the first-order resonant periodic families \(IB(p-(p+1))\) and \(OB(p-(p+1))\) gradually disappear because they are overlapped by the 1:1 resonance. The RTPOs have part of the orbit resembling the planar Lyapunov orbits around the \(L_{1}\) or \(L_{2}\) point, and the other part resembling the interior or exterior resonant periodic orbits. The collinear libration points along with the orbits around them can be taken as unstable'saddle points' of the 1:1 resonance. As a result, the RTPOs can be taken as the connection orbits between the 1:1 resonance region and the interior or the exterior resonance regions. By following the breakup and recombination process of the families \(IB(p-(p+1))\) and \(OB(p-(p+1))\) with the RTPFs, the details of the resonance overlapping process are revealed by the approach of periodic orbits.
## 4 Simulation Results and Discussion
### Comparison with the Traditional Control Way  
The results about microgrid secondary control for average voltage restoration and reactive power sharing are usually based on periodic communication mechanism [9; 26]. Therefore, a comparison between the proposed self-triggered control and the traditional control is made in this subsection. The simulation results of the traditional control used periodic communication mechanism are shown in Figure 9. To compare the communication burden with the proposed strategy, for traditional control, periodic communication between neighboring DGs with a 5 ms interval is considered [15]. The comparison results about communication numbers and the average time interval during the time frame 1-2.5 s is depicted in Table 2.  
As illustrated in Figure 9, due to the periodic communication mechanism, the voltage and reactive power curves are smoother by using traditional control. The proposed self-triggered control strategy only updates the control inputs according to the triggering conditions which cause a little oscillation in triggering time instants. However, in Table 2, the number of communication transmissions  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**DGs**} & \multirow{2}{*}{**Controllers**} & \multicolumn{2}{c}{**Number of Communications**} & \multicolumn{2}{c}{**Average Time Intervals**} \\ \cline{3-6}  & & **Traditional Control** & **Our Method** & **Traditional Control** & **Our Method** \\ \hline \multirow{2}{*}{DG1\&DG2} & Voltage & 300 & 141 & 5 ms & 11 ms \\ \cline{2-6}  & Reactive power & 300 & 121 & 5 ms & 12 ms \\ \hline \multirow{2}{*}{DG2\&DG3} & Voltage & 300 & 165 & 5 ms & 9 ms \\ \cline{2-6}  & Reactive power & 300 & 123 & 5 ms & 12 ms \\ \hline \multirow{2}{*}{DG3\&DG4} & Voltage & 300 & 139 & 5 ms & 11 ms \\ \cline{2-6}  & Reactive power & 300 & 106 & 5 ms & 14 ms \\ \hline \multirow{2}{*}{DG1\&DG4} & Voltage & 300 & 102 & 5 ms & 14 ms \\ \cline{2-6}  & Reactive power & 300 & 83 & 5 ms & 18 ms \\ \hline \end{tabular}
\end{table}
Table 2: Communication comparison of secondary control.  
Figure 8: Triggering time instants of neighboring DGs: (**a**) triggering time instants for average voltage restoration control; (**b**) triggering time instants for reactive power sharing control.  
and the average time interval between two contiguous communications show the advantage of the self-triggered strategy compared with the periodic control strategy. It is concluded that, with the proposed self-triggered control strategy, the communication amount between neighboring DGs can be significantly reduced during the operation.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 2 Positioning Architecture
### Slave EKF-Based Doppler Positioning Model
#### 2.2.1 EKF Model and Parameters  
The state vector of the EKF system was defined as Equation (9).  
\[x=\left[q_{s}^{T},\,p_{s}^{T},\,v_{s}^{T},\,c\delta t_{r},\,c\dot{\delta t}_{r }\right]^{T}, \tag{9}\]  
The attitude quaternion vector, the three-axis velocity, and the three-axis position vectors were updated by a similar kinematic INS equation mentioned in the previous part. Moreover, the dynamic model for the clock bias, \(\delta t_{r}\), and the clock drift, \(\dot{\delta t}_{r}\), of the receiver after discretization are given by Equations (10) and (11) [31].  
\[x_{clk,r}(k+1)=F_{clk}.x_{clk,r}(k)+n_{clk,r}(k), \tag{10}\]  
\[x_{clk,r}=\left[c\delta t_{r},\,\dot{c\delta t}_{r}\right]^{T},\,F_{clk}= \left[\begin{array}{cc}1&T\\ 0&1\end{array}\right], \tag{11}\]  
where \(T\) is the sampling time interval and \(n_{clk,r}\) is the discretized process white Gaussian noise sequence with covariance matrix \(Q_{clk,r}\), which is defined in the Equation (12). Additionally, \(c\) is the speed of light.  
\[Q_{clk,r}=\left[\begin{array}{cc}P_{\delta t_{r}}&P_{\delta t_{r}}\\ 0&\frac{1}{T}P_{\delta t_{r}}\end{array}\right]\left[\begin{array}{cc}T&0\\ \frac{T}{3}&\frac{T}{2}^{2}\end{array}\right]\odot\left[\begin{array}{cc}1& 1\\ 1&\frac{4}{3}\end{array}\right], \tag{12}\]  
where \(\odot\) is the element-wise product sign, and \(P_{\delta t_{r}}\) and \(P_{\delta t_{r}}\) are defined as the process noise power spectra for the clock bias and the clock drift of the receiver, respectively. These two parameters are related to the power law coefficients, \(\left\{h_{a,r}\right\}_{a=-2}^{2}\). The approximations of these parameters are \(P_{\delta t_{r}}\cong\frac{h_{0r}}{2}\) and \(P_{\delta t_{r}}\cong 2\pi^{2}h_{-2,r}\), as discussed in [32].
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 5 PA-Based Communication and Localization  
The photoacoustic mechanism is commonly used in medical imaging. Yet, it has a great potential as a means of air to underwater communication. In the previous sections we have discussed the generation and properties of linear and nonlinear photoacoustic signals. In this section, we will highlight some notable research work that has focused on exploring the photoacoustic effect in communication. We will start with discussion of some key challenges that are associated with photoacoustic communication.
_articles_  
_Article_  
**Privacy-Preserving Lightweight Authentication Protocol for Demand Response Management in Smart Grid Environment**  
[PERSON] **1**\({}^{\copyright}\), [PERSON] **2**\({}^{\copyright}\), [PERSON] **1**\({}^{\copyright}\), [PERSON] **1,**\({}^{\copyright\left(}\), [PERSON] **3,**\({}^{\copyright\left(}\),  
**[PERSON] **2** and [PERSON] **2**  
1 School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea;  
[EMAIL_ADDRESS] (S.Y.); [EMAIL_ADDRESS] (J.L)  
2 Electronics and Telecommunications Research Institute, Daejeon 34129, Korea; [EMAIL_ADDRESS] (K.P.);  
[EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (B.C.)  
3 School of Computer Engineering, Keimyung University, Daegu 42601, Korea  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS] (Y.P.); [EMAIL_ADDRESS] (Y.P.); Tel.: +82-53-950-7842 (Y.P); +82-53-580-5229 (Y.P).  
Received: 24 January 2020; Accepted: 27 February 2020; Published: 4 March 2020  
With the development in wireless communication and low-power device, users can receive various useful services such as electric vehicle (EV) charging, smart building, and smart home services at anytime and anywhere in smart grid (SG) environments. The SG devices send demand of electricity to the remote control center and utility center (UC) to use energy services, and UCs handle it for distributing electricity efficiently. However, in SG environments, the transmitted messages are vulnerable to various attacks because information related to electricity is transmitted over an insecure channel. Thus, secure authentication and key agreement are essential to provide secure energy services for legitimate users. In 2019, [PERSON] et al. presented a secure authentication protocol for demand response management in the SG system. However, we demonstrate that their protocol is insecure against masquerade, the SG device stolen, and session key disclosure attacks and does not ensure secure mutual authentication. Thus, we propose a privacy-preserving lightweight authentication protocol for demand response management in the SG environments to address the security shortcomings of [PERSON] et al.'s protocol. The proposed protocol withstands various attacks and ensures secure mutual authentication and anonymity. We also evaluated the security features of the proposed scheme using informal security analysis and proved the session key security of proposed scheme using the ROR model. Furthermore, we showed that the proposed protocol achieves secure mutual authentication between the SG devices and the UC using Burrows-Abadi-Needham (BAN) logic analysis. We also demonstrated that our authentication protocol prevents man-in-the-middle and replay attacks utilizing AVISPA simulation tool and compared the performance analysis with other existing protocols. Therefore, the proposed scheme provides superior safety and efficiency other than existing related protocols and can be suitable for practical SG environments.  
s smart grid; authentication; informal security analysis; BAN logic; ROR model; AVISPA
## 1 Introduction
In order to make the devices exhibit working efficiencies as high as possible, ideally one requires the designed meta-atoms to be either perfectly reflective:  
\[\mathbf{T}=0;\ |r_{ii}|=1 \tag{3}\]  
or perfectly transparent:  
\[\mathbf{R}=0;|t_{ii}|=1. \tag{4}\]  
Once such conditions are satisfied meaning that all meta-atoms are of highest working efficiencies, people then adjust the geometrical parameters of each meta-atom to make the whole metasurface exhibit desired polarization-dependent phase profiles (i.e., \(\varphi_{xx}(x,y)\) and \(\varphi_{yy}(x,y)\)) to achieve certain wave-manipulation functionalities. A typical example is schematically shown in Figure 2, for which the wanted bi-functionalities are focusing and beam bending, respectively. To achieve this end, the final phase distributions on the metasurface should be:  
\[\varphi_{yy}(x,y)=k_{0}(\sqrt{F^{2}+x^{2}+y^{2}}-F) \tag{5}\]  
with \(F\) being the focal length and \(k_{0}\) being the free-space wave-vector, and:  
\[\varphi_{xx}(x,y)=C_{1}+\xi\cdot x \tag{6}\]  
with \(\xi\) being the phase gradient responsible for the anomalous reflection angle and \(C_{1}\) being an a constant. We note that a conversion from propagating wave (PW) to surface wave (SW) can happen if \(\xi>k_{0}\). We emphasize that the high-efficiency conditions Equations (1) and (2) can only be approximately satisfied since losses inevitably exist, especially at high frequencies. Moreover, the achieved functionalities are not limited to the two described above. If different wave-manipulation functionalities are desired, one may easily replace Equations (5) and (6) by the phase distributions related to those functionalities.
## 4 System Level Perspectives of JCRS RF Hardware
### Link Level Perspective for Application Scenarios  
For V2V applications, communication-assisted radar functionality can be implemented by embedding one of the existing modulation schemes into FMCW waveforms, thus, enabling low-data-rate communication between the vehicles. For example, quadrature amplitude modulation (QAM) can be implemented by modulating the conducted power and phase of FMCW waveforms. As the phase information is used for velocity estimation, communication phase de-embedding is used.  
Figure 6: RF Self Interference Cancellation.  
When it comes to digital twining, radar-assisted communication reuses the existing infrastructure, which is based on the OFDM modulations scheme. The whole available bandwidth can be used for sensing and increasing the range resolution. However, this will reduce the sensing signal-to-noise ratio. Recent approaches allocate a number of subcarriers for sensing, maintaining others for communication [13].  
For both FMCW and OFDM modulation schemes, self-interference cancellation is crucial to suppress the transmitter signal leaked into the receiver chain. The conducted power (P\({}_{\text{TX}}\)) is normally regulatory limited, i.e., 20-24 dBm for the transmitter conducted power [88]. Ideally, we need to bring this below the noise floor, which is defined by the channel bandwidth. More isolation may be required in the case of substantial signal processing gain. Figure 7 shows a use case for a communication/sensing channel with 100 MHz bandwidth. As can be seen, 110 dB total isolation is needed to fully suppress the leaked signal. It is worth noting that, with increasing the bandwidth, the isolation requirement could be potentially relaxed, as P\({}_{\text{sens}}\) increases with that.
## 3 Experimental Results and Discussion
### Discussion  
We developed the 2D spatial and 1D CNN-based classifier with mammographic image classification for screening the disease present in normality (Nor) or abnormality (B and M classes). For the MIAS image database [22; 23], with the 10-fold cross-validation tests, as seen in Table 4, the experimental results indicated an average precision of 96.70%, average recall of 96.13%, average accuracy of 96.40%, and average F1 score of 0.9641 to quantify the classification performance for identifying the breast lesions. The performance of the proposed classifier was superior to that of the traditional 2D CNN-based classifier in design cycle, screening accuracy, parameters assignment (including convolutional masks and BPNN's network parameters), parameters adjustment, computational complexity level (iteration computations), and computational time. The BPNN's optimal parameters required determination by the ADAM algorithm in the training stage, and were updated by adjusting the network parameter, decay parameter, learning rate, and attenuation rate to minimize the error rate. Additionally, the classification methods, ML- and DL-based methods, were both used to carry out different classifier models for clinical/medical purposes, including breast density estimation, mass detection/mass segmentation, mammogram classification/breast lesions screening, and automated breast cancer detection [24; 25; 26; 27; 28; 29; 37; 38; 59; 60; 61], as seen in Table 5. ML was based on low-level image features, such as shapes, texture, and local key-point features [24; 25; 27; 59; 61], and the supervised ML-based models, such as SVM, ANN, and clustering methods [24; 25; 59], were used to establish various computer-aided vision classifiers. With the MIAS database, SVM and ANN methods had accuracy rates of 94% and 97.08% for mammogram classification and mass detection, respectively [24; 25]. Clustering methods, such as K-means, fuzzy C-means, and GA-based feature selection algorithms [27; 61], had accuracy rates of 91.18%, 94.12%, and 84.5% for mass segmentation and mammogram classification, respectively. However, the SVM and ANN required the manual labeled classes and the selected feature patterns to train the classifier, which also required the ongoing human participation and expert intervention to feed new training datasets and continuously model the purposed tasks. Hence, its model needed more datasets to feed the classifier and to confirm the accurate classification or correct response through the designers. In clinical application, over time, its model was able to handle the new dataset to retrain the classifier, resulting in inefficiencies to keep the classifier's performance, and it was not easy to make classifier adjustments on real-time application. Clustering methods (CM) [27; 61] were unsupervised learning to help the classifier's complex tasks to deal with large, highly flexible, and unpredicted/unlabeled datasets. However, the CM had no
# Recent Advances in Optical Detection of Aminoglycosodies
## 1 Introduction
### The Danger of Aminoglycosoides  
Because of their low price, good stability, and effective antibacterial activity, AGs have been used clinically in the treatment of Gram-negative bacteria, _Monas_ and _Staphylococcus_ infections, and tuberculosis [3]. In the treatment of animal disease, AGs are used to treat bovine mastitis, enteritis, metritis, peritonitis, sepsis, etc. In addition to being used as therapeutic drugs, AGs are also used for promoting the growth of animals [4], which inevitably causes the release of residues into the environment and undesirable residues in food. AGs have serious side effects including ototoxicity, toxicity to kidneys, damage of the vestibular nerve, muscle paralysis, etc. [5]. In severe cases, they can cause shock or even death. A small amount of antibiotics residue in food can induce antibiotic resistance cases, which will be a big problem in the future. Therefore, many countries and regions have set a maximum residue limit (MRL) for AG residues in animal products (Table 1). The simple and rapid detection of AGs with high sensitivity and selectivity is important for the routine detection of AGs.
# Power Efficient Random Access for Massive NB-IoT Connectivity
###### Abstract  
Sensors enabled Internet of things (IoT) has become an integral part of the modern, digital and connected ecosystem. Narrowband IoT (NB-IoT) technology is one of its economical versions preferable when low power and resource limited sensors based applications are considered. One of the major characteristics of NB-IoT technology is its offer of reliable coverage enhancement (CE) which is achieved by repeating the transmission of signals. This repeated transmission of the same signal challenges power saving in low complexity NB-IoT devices. Additionally, the NB-IoT devices are expected to suffer from congestion due to simultaneous random access procedures (RAPs) from an enormous number of devices. Multiple RAP reattempts would further reduce the power saving in NB-IoT devices. We propose a novel power efficient RAP (PE-RAP) for reducing power consumption of NB-IoT devices in a highly congested environment. The existing RAP do not differentiate the failures due to poor channel conditions or due to collision. After the RAP failure either due to collision or poor channel, the devices can apply power ramping or can transit to a higher CE level with higher repetition configuration. In the proposed PE-RAP, the NB-IoT devices can re-ascertain the channel conditions after an RAP attempt failure such that the impediments due to poor channel are reduced. The power increments and repetition enhancements are applied only when necessary. We probabilistically obtain the chances of RAP reattempts. Subsequently, we evaluate the average power consumption by devices in different CE levels for different repetition configurations. We validate our analysis by simulation studies.  
NB-IoT; random access; power consumption; coverage enhancement; collision probability +
Footnote †: journal: Sensors