esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Challenges and Future Research Directions
### Data Quality Issues  
As noticed from the survey work, the current approaches for DDoS attack detection employ machine and deep learning models due to their ability to learn traffic patterns quickly with good detection accuracy. These models, however, heavily rely on the dataset. The quality as well as the size of the datasets used to train the algorithms are crucial for better performance. The quality of the training datasets plays an essential role in developing an efficient DDoS attack detection method. However, a fundamental issue with this approach is the lack of readily accessible, high-quality datasets. In some cases, there is a data class imbalance that occasionally reduces the detection accuracy of these models. Although the data class imbalance is the current focus in some studies [208, 209, 210, 211], ensuring a sufficient high-quality dataset would improve the accuracy of DDoS attack detection methods and should be a focus for future studies. Further investigation in this direction should be sustained, which is a future research direction. In addition, most of the datasets are outdated, have low traffic diversity, and are unreliable for modern attack detection methods.
## 2 Methodological Framework
### Data Collection  
According to [PERSON] et al. [4], data collection is a crucial step in order to apply the proposed approach. Specifically, data collection is related to the human body movements and it may be performed by means of both optical and non-optical motion capture based systems.  
Together with the motion tracking system, it could be useful also to get additional data, such as the ones related to the exerted forces, by means of specific devices (force sensors, cyber gloves, etc.) to carry out the analysis.  
It is important to define the number of cycles to be acquired during data acquisition session. This is required (see Section 3.1) in order to compute the basic statistical moments (mean and standard deviation) of ergonomic indexes, useful to assess whether a deep investigation is needed to evaluate critical ergonomic issues. The number of cycles to acquire during data collection phase strictly depends on the cycle time of the working task: in particular, the smaller the cycle time, the greater the number of acquisitions will be due to the increase of the variability of acquisitions [28].  
Once the number of cycles to acquire have been defined, data acquisition session may start. If data acquisition is performed by means of wearable sensors, ethical requirement must be signed by the workers wearing them. Typically, a questionnaire about the usability of the system is also submitted to workers.  
Acquired data are transferred to an appropriate software able to perform human simulations, such as Tecnomatix by Siemens(r)[29] or Delmia by Dassault Sistemes(r)[30], and able to integrate such  
Figure 1: Methodological framework for monitoring ergonomics performance during the production process.  
a data to the DHM, which accurately replicates the real workers' working tasks. Depending on the type of device used during the acquisition session, the way to transfer data to the software changes. In some cases, as shown later on in the case study, custom plugins and a re-sampling of data are necessary to integrate them in the simulation scenario.
## 3 Multi-Objective Optimization Algorithms
### End-to-End Delay Feature  
This feature measures the time required to successfully transfer the data packet from the sensor node to sink node, including, the transmission time of packet (\(T_{packet}\)), inter-frame space-time (\(T_{IFS}\)), backoff time (\(T_{bo}\)), turnaround time of transceiver's (\(T_{TA}\)), and acknowledgment of receipt time (\(T_{ACK}\)). The end-to-end delay (\(T_{l}\)) can be expressed by the following formula [20]:  
\[T_{l}=T_{packet}+T_{IFS}+T_{bo}+T_{TA}+T_{ACK}, \tag{8}\]  
\(T_{packet}\) is a transmission time that is required for any data packet to reach the destination. It can be defined as follows:  
\[T_{packet}=\frac{L_{PHY}+L_{MHR}+payload+L_{MFR}}{R_{data}}, \tag{9}\]  
where:  
* \(L_{PHY}\) is the size of physical header in byte;
* \(L_{MHR}\) is the size of MAC header in byte;
* _payload_ is the size of data in the packet in byte;
* \(L_{MFR}\) is the size of MAC footer in byte;
* \(R_{data}\) is the data transmission rate.  
The second equation that should be defined is a backoff periods for the node that wants to transmit the data packet through the network. This can be calculated by determining the probability of any node (\(p_{s}\)) of accessing the medium in a successful way. \(p_{s}\) can be calculated by the following formula:  
\[P_{s}=\sum_{a=1}^{a=b}P_{c}(1-P_{c})^{(a-1)}, \tag{10}\]  
where, \(p_{c}\) is the assessment probability of the ideal channel that achieves by any node at the end of any backoff period while \(b\) is the maximum number of backoff periods. \(p_{c}\) can be calculated by the following equation:  
\[P_{c}=(1-q)^{n-1}, \tag{11}\]  
where, \(q\) is the transmit probability at any time that achieves by any node and \(n\) is the number of devices that operate on the network. The average of backoff periods (\(R\)) can be expressed as:  
\[R=(1-P_{s})b+\sum_{a=1}^{a=b}aP_{c}(1-P_{c})^{(a-1)}, \tag{12}\]  
Hence, the total of backoff time (\(T_{bo}\)), can be calculated as:  
\[T_{bo}=FractionalPart[R]T_{bo}(IntegerPart[R]+1)+\sum_{a=1}^{a=IntegerPart[R]}T_{bo }(a), \tag{13}\]  
where, \(T_{bo}\) is the average backoff period, which can be calculated as:  
\[T_{bo}(a)=\frac{2^{maxMinB+a-1-1}}{K_{data}}T_{basic}, \tag{14}\]  
\(macMinBe\) is the initial value of backoff, and \(T_{buslot}\) is the backoff time at one slot duration. For IEEE 802.15.4\(\backslash\) Zigbee, one-slot-duration is equal to the duration of 20 symbols.
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Laser Integration, Reliability and Performance
#### 2.2.2 Quantum Dot Fabry-Perot Laser Diode Performance at High Temperature  
In order to accommodate an integrated laser solution, the laser must be exceptionally reliable at higher temperatures. For this purpose, a Quantum Dot Fabry-Perot Laser Diode (QD-LD) with an operational wavelength of 1310 nm was integrated in the IOCore micro-transceiver.  
As shown in the L-I curves of Figure 7a, the QD-LD has a characteristically stable optical output even at high temperatures, with little deviation in output power over a wide temperature range. In addition, as shown in the RIN characteristics of Figure 7b, the QD-LD exhibits strong suppression of noise due to back-reflected light; therefore, they can  
Figure 5: Silicon photonics chiplets in a co-packaged assembly with (**a**) integrated laser source, (**b**) external laser source mounted on the board and (**c**) front-pluggable and completely field-replaceable external light source on faceplate.  
be coupled into a silicon photonic circuit without an isolator, allowing further packaging and cost reduction.
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 3 Preliminaries and Definitions
### Silhouette Index  
The _Silhouette Index_ (SI) is a widely used internal cluster validation technique, introduced in ([PERSON] et al., 2017). SI can be used to judge the quality of any clustering solution \(C=\{C_{1},C_{2},\ldots,C_{k}\}\). It assesses the separation and compactness between the clusters. Suppose that \(a_{i}\) represents the average distance of item \(i\) from all the other items in the cluster to which item \(i\) is assigned, and \(b_{i}\) represents the minimum of the average distances of item \(i\) from the items of the other clusters. Then, the _Silhouette score_\(s(i)\) of item \(i\) can be calculated as  
\[s(i)=(b_{i}-a_{i})/\max\{a_{i},b_{i}\}. \tag{6}\]  
\(s(i)\) measures how well item \(i\) matches the clustering at hand. \(s(i)\in[-1,1]\), and if \(s(i)\) is close to \(1\), this means that item \(i\) is assigned to a very appropriate cluster. The situation is different when \(s(i)\) is near zero. Specifically, item \(i\) lies between two clusters. The worst case is when \(s(i)\) is close to \(-1\). Evidently, this item has been misclassified.  
In addition, the overall Silhouette score for the whole clustering solution \(C\) of \(n\) items is determined as  
\[s(C)=\frac{1}{n}\sum_{i=1}^{n}\frac{(b_{i}-a_{i})}{\max\{a_{i},b_{i}\}}. \tag{7}\]  
The SI can also be calculated for each cluster \(C_{j}\) (\(j=1,2,\ldots,k\)) of \(n_{j}\) objects as follows:  
\[s(C_{j})=\frac{1}{n_{j}}\sum_{i=1}^{n_{j}}s(i). \tag{8}\]  
The FedCO algorithm proposed in this study uses the Silhouette Index at each iteration round for assessing the current workers' partitioning and, based on this assessment, selects what optimizing actions to conduct. For example, we used SI to check whether a representative is still firmly tied to its current cluster of workers. It may happen that some representatives will change their clusters. If we have a worker that produces a negative SI value for all clusters, this means that this worker cannot be assigned to any of the existing clusters, and it will form a new singleton cluster; i.e., a new concept appears. In addition, SI is applied to assess whether an intended splitting of a cluster will improve the quality of the whole clustering solution, i.e., whether it should be conducted. For more details, see Section 4. Note also that in the implemented version of our FedCO algorithm, we use Euclidean distance to measure the similarity between each pair of workers. In particular, the Euclidean distance between the worker (the representative) and the cluster centers (medoids) has been computed.
## 1 Introduction
Therefore, there is a strong demand to create a MEMS energy harvester that could be tuned during manufacturing to take advantage of the batch fabrication capabilities. Researchers have investigated methods to tune the frequency by etching or removing sections of the cantilever or length [33]. However, this affects the dynamics and requires complex fabrication techniques. Researchers have tuned the frequency by adding mass through deposition or pick-and-place machines to reduce the frequency [31; 34]. A recent conference paper by the authors demonstrated tuning capability using a proof mass cavity array to distribute the mass along the cantilever, which is the basis of this study [35]. This paper investigates a method to resolve the issue with tuning the frequency during manufacturing by creating a custom-designed cavity array proof mass, which can be filled individually to redistribute the mass load on the cantilever using solid micro/nano particles. Filling specific cavities will alter the total mass, thus changing the resonant frequency; but by varying the location, volume, and density of the filler, the resonant frequency can be finely tuned with a long frequency range. The goal is to increase both frequency tuning range and resolution. This study investigates altering the mass load distribution to tune the frequency using a macro-scale piezoelectric cantilever structure, but the concept could be scaled down to the micro-scale and could also be applied to various shaped structures. Accomplishing this task would allow manufacturers to create a single energy harvester device, that could be customized using back-end fabrication to fill specific cavities to meet the end user's specifications. The method is validated both experimentally as well as numerically through finite element modelling (FEM) using various particles and a commercial low-frequency piezoelectric cantilever.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Group** & **Test** & **Mean** & **SD** & **\(t\)-Test (\(p\))** & **ICC** & **95\% CI** & **CV** & **SD** & **SEM** & **SEM\%** & **MDC** & **MDC \%** \\ \hline \multirow{2}{*}{G\({}_{9\text{-}10}\)} & Test & 148.28 & 20.28 & \multirow{2}{*}{0.379} & \multirow{2}{*}{0.651} & \multirow{2}{*}{(0.255–0.858)} & \multirow{2}{*}{0.065} & \multirow{2}{*}{0.041} & \multirow{2}{*}{10.94} & \multirow{2}{*}{7.19} & \multirow{2}{*}{30.32} & \multirow{2}{*}{19.94} \\  & Retest & 155.88 & 16.28 & & & & & & & & & \\ \hline \multirow{2}{*}{G\({}_{11\text{-}12}\)} & Test & 219.47 & 55.12 & \multirow{2}{*}{0.513} & \multirow{2}{*}{0.853} & \multirow{2}{*}{(0.634–0.946)} & \multirow{2}{*}{0.069} & \multirow{2}{*}{0.075} & \multirow{2}{*}{22.80} & \multirow{2}{*}{10.52} & \multirow{2}{*}{63.20} & \multirow{2}{*}{29.17} \\  & Retest & 213.90 & 65.22 & & & & & & & & & \\ \hline \multirow{2}{*}{G\({}_{13\text{-}14}\)} & Test & 282.92 & 28.86 & \multirow{2}{*}{0.065} & \multirow{2}{*}{0.696} & \multirow{2}{*}{(0.634–0.946)} & \multirow{2}{*}{0.057} & \multirow{2}{*}{0.024} & \multirow{2}{*}{17.56} & \multirow{2}{*}{6.05} & \multirow{2}{*}{48.67} & \multirow{2}{*}{16.76} \\  & Retest & 298.01 & 33.86 & & & & & & & & & \\ \hline \multirow{2}{*}{All Girls} & Test & 215.26 & 81.35 & \multirow{2}{*}{0.029 \({}^{*}\)} & \multirow{2}{*}{0.937} & \multirow{2}{*}{(0.843–0.974)} & \multirow{2}{*}{0.075} & \multirow{2}{*}{0.052} & \multirow{2}{*}{21.86} & \multirow{2}{*}{9.85} & \multirow{2}{*}{60.6} & \multirow{2}{*}{27.29} \\  & Retest & 228.83 & 92.85 & & & & & & & & & \\ \hline \multirow{2}{*}{All Boys} & Test & 222.21 & 56.64 & \multirow{2}{*}{0.804} & \multirow{2}{*}{0.800} & \multirow{2}{*}{(0.591–0.909)} & \multirow{2}{*}{0.096} & \multirow{2}{*}{0.078} & \multirow{2}{*}{26.69} & \multirow{2}{*}{11.96} & \multirow{2}{*}{74.00} & \multirow{2}{*}{33.16} \\  & Retest & 224.18 & 62.76 & & & & & & & & & \\ \hline \multirow{2}{*}{All Sample} & Test & 218.74 & 69.43 & \multirow{2}{*}{0.120} & \multirow{2}{*}{0.892} & \multirow{2}{*}{(0.814–0.938)} & \multirow{2}{*}{0.086} & \multirow{2}{*}{0.067} & \multirow{2}{*}{24.29} & \multirow{2}{*}{10.91} & \multirow{2}{*}{67.35} & \multirow{2}{*}{30.25} \\  & Retest & 226.50 & 78.44 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Muscle power (W) values at 60% of 1 RM in LE. Figure 6: Average evolution of the power values obtained with loads at different percentages of 1 RM in the LE and SBP exercises. The numbers above the standard deviation bar denote significant difference when comparing with the load cited. (The complete analysis of ANOVA is shown as Supplementary Materials online, Table S1). the MDC% was different in each age group ranging from 18.79 to 30.01 and boys showed lower value than girls (MDC% = 22.06).
## 4 Results and Discussion
### Radionuclide Activity Concentrations
 & **Variance** & **Skewness** & **Kurtosis** & **CV** \\ \hline U-238 & 380 & 62.2 & 20.8 & 13.6 & 130.9 & 60.1 & 64.3 & 433 & 0.70 & 0.60 & 0.33 \\ Th-232 & 380 & 84.2 & 23.3 & 30.0 & 192.0 & 81.9 & 86.6 & 541 & 1.20 & 2.91 & 0.28 \\ K-40 & 380 & 949.4 & 172.5 & 375.6 & 1471.1 & 932.0 & 966.8 & 29,753 & \(-\)0.11 & 0.31 & 0.18 \\ Ra\({}_{\rm eq}\) & 380 & 255.7 & 50.6 & 107.6 & 433.8 & 250.6 & 260.8 & 2563 & 0.70 & 1.10 & 0.20 \\ H\({}_{\rm in}\) & 380 & 0.9 & 0.2 & 0.4 & 1.5 & 0.84 & 0.88 & 0.033 & 0.72 & 0.83 & 0.21 \\ H\({}_{\rm ex}\) & 380 & 0.7 & 0.1 & 0.3 & 1.2 & 0.68 & 0.70 & 0.019 & 0.70 & 1.10 & 0.20 \\ Jr & 380 & 0.9 & 0.2 & 0.4 &The comparison between the present results of the \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K activity concentrations with the previous review data is listed in Table 4. This comparison shows the radionuclide activity concentrations results pivot essentially on the geological description of any studied area. The present results of the \({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K activity concentrations display either lower or similar to the previous data.
## 2 Related Works
### Global Standardization in ITU-R and 3 GPP
#### 2.2.2 Standardization Trends for 5G and Beyond in 3 GPP  
For seamless communication and the improvement of communication quality, a range of features including multi-connectivity solutions have been developed in 3 GPP across successive generational advancements [9; 33; 34]. Preceding the establishment of 5G standards, the prevalent approaches to enable multi-connectivity predominantly stemmed from assessments within RAN domains. These approaches were designed to ascertain the optimal selection of radio channels, both within a communication system with a single connection to a certain RAN and in interworking scenarios involving 3 GPP systems (e.g., LTE) and non-3 GPP systems (i.e., Wi-Fi) [35]. With the integration of satellite access components as part of the 5G landscape in 2022 [36; 37; 38], a critical challenge has arisen concerning the efficient utilization of multiple RANs within a cohesive communication framework. This challenge aims to harness the potential for augmented throughput, enhanced reliability, and diminished energy consumption in the context of 5G and subsequent standardization efforts [39; 40]. The forefront of 5G technology pertaining to the efficient resource utilization of multiple RANs will undergo continuous advancement in the subsequent generation. These capabilities are of paramount importance in meeting the demands for ubiquitous connectivity within the IMT-2030 system. A comprehensive exploration of these features will be presented in the forthcoming sections.
# Pseudorange Measurement Method Based on AIS Signals
## 3 Detective Feature of Timestamp
### Amplitude Characteristics of Timestamp
#### 3.2.2 Timestamp Selection Scheme in AIS-R Mode  
Based on the conclusions of the previous section, it can be seen that the errors between the outputs are obtained by single pulse signal hopping edges through the Gaussian filter and the amplitude midpoint obtained by the unit step signal hopping edge through the Gaussian filter is greatest.  
For a single pulse signal, Equation (9) can be simplified as  
\[O_{G}(t)\ =\left\{erf(kt)-erf[k(t-1)\,]\right\}=\frac{2}{\sqrt{\pi}}\int_{k(t-1)} ^{kt}e^{-\eta^{2}}d\eta \tag{11}\]  
As the integrand function \(e^{-\eta^{2}}\) is even function, so  
\[O_{G}(0)\ =O_{G}(1)\ =\frac{1}{2}erf(\frac{\sqrt{2}\pi BT}{\sqrt{\ln 2}}) \tag{12}\]  
For AIS systems, the maximum normalized amplitude error of the bit-0-phase in the Gaussian baseband waveform is about \(1.27\times 10^{-3}\) due to \(BT\) = 0.4 [25].  
In the AIS system, the minimum length of each frame packet is 56 bits [25]. The conditions \(N\) = 56 are brought into Equation (10) for numerical analysis. The results are shown in Table 1.  
Figure 5: Amplitude error of the timestamps.  
From the results of the numerical analysis shown in Table 1, it can be seen that in addition to each two bits at the beginning and end of an AIS packet, the remaining bit-0-phases can be used as the timestamp for measuring the transmission delay in the AIS-R mode.
# Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles
## 2 Tracking of UAVs Using a Kalman Filter Algorithm with Error and Uncertainty Sensitivity
### Error and Uncertainty Aware Kalman Filter Algorithm  
The recursive equation is obtained from the inverse matrix condition defined in Equation (14). Existing models predominantly use the Kalman filter for UAV target tracking; however, target tracking models based on the Kalman filter perform poorly under the dynamic model and, in the absence of/unknown prior knowledge/information. Figure 2 shows the KFUEA flow diagram. To overcome the research problems, the authors introduce a proven Kalman filter for a nonlinear environment, and its measurement is performed using the following equation:  
\[x_{l+1}=g(x_{l})+\delta y_{l} \tag{15}\]  
and  
\[z_{l}=i(x_{l})+w\alpha_{l} \tag{16}\]  
Figure 2: KFUEA Flow Diagram.  
Similar to the standard linear KF, we linearized closer to the equilibrium point, e.g., \(x_{l}=\overline{x}\), so that the filter becomes  
\[x_{l+1}=g(\overline{x})+\left.\frac{\partial g}{\partial x}\right|_{x=\overline{ x}}(x_{l}-\overline{x})+\mathrm{I}\Big{(}\|x_{l}-\overline{x}\|^{2}\Big{)}+ \delta y_{l}, \tag{17}\]  
Otherwise, the configuration, \(\widetilde{x}_{l}=x_{l}-\overline{x}_{i}\mathcal{O}=\left.\frac{\partial g}{ \partial x}\right|_{x=\overline{x}^{i}}\sigma g_{l}=g(\overline{x})\), gives the above equation,  
\[\widetilde{y}_{l+1}=B\widetilde{y}_{l}+\sigma g_{l}+\mathcal{P}\Big{(}\left. \left\|\widetilde{y}_{l}\right\|\right|^{2}\Big{)}+\delta x_{l}. \tag{18}\]  
Next, we approximate the residual errors and identify the noise using the following equation  
\[\sigma g_{l}=\mathrm{I}\Big{(}\|\widetilde{x}_{l}\|^{2}\Big{)}\cong\Big{(} \delta_{x}+\widetilde{\delta}_{x}V(\left|\widetilde{x}_{l}\right|)\Big{)} \alpha_{l}^{x}, \tag{19}\]  
Similarly, the measurement model is calculated using the following equations  
\[x_{l+1}=Ox_{l}+\delta y_{l}+\big{(}\delta_{x}+\overline{\delta}_{x}V(\left| \widetilde{x}_{l}\right|)\big{)}\alpha_{l}^{x}, \tag{20}\]  
\[z_{l}=Lx_{l}+w\alpha_{l}+\big{(}\delta_{z}+\overline{\delta}_{x}V(\left| \widetilde{x}_{l}\right|)\big{)}\alpha_{l}^{z} \tag{21}\]  
Here \(\mathcal{O}\) defines the ambient matrices, and \(L\) defines the measurement matrices, and \(\delta_{x}\), \(\overline{\delta}_{x}\), \(\delta_{z}\), \(\overline{\delta}_{z}\) are modified Kalman filter matrices. The sequence set \(y_{l}\), \(\alpha_{l}^{x}\), \(\alpha_{l}\), \(\alpha_{l}^{z}\), \(l\geq 0\) represents the Gaussian with zero mean and uniform covariance and \(\delta\delta^{R}\succ 0\), \(ww^{R}\succ 0\). The modified KF is constructed using its covariance and state estimate in the update and prediction phases. The measured value \(z_{l}\), the state estimate \(x_{||l}\), and the corresponding covariance \(D_{||l}\) are defined by the data collected during phase \(l\). The expected trajectory is calculated as follows  
\[F\Big{\{}\cdot\big{|}z_{l},x,D_{||l}\Big{\}}=F_{l}\{\cdot\} \tag{22}\]
## 4 Results
### Simulation 1
Figure 8: PDR for types of jamming in a WSN of 500 m\({}^{2}\) with different number of nodes under collaborative and cooperative schemes. A similar analysis was carried out in Figure 9, for the RSSI, discovering that reactive jamming is the one that shows the best behavior for the network, followed by random, and far apart from these, deceptive and constant jamming, which have a noticeable dBm increase, reaching very low coverage levels. Once again it was shown that when there is no jamming, the cooperative scheme is better, because the nodes obey the sink node, and when the signal is lost due to jamming, the nodes take longer to know what to do because they are caught off guard and perform more information processing rediscovering the network, taking more time to find a fast route than with fewer hops to get the packets. PDR and RSSI have a significant impact on energy. Figure 10 shows that constant jamming is the one that makes the network consume more energy because it is the most demanding, while the reactive jamming takes less energy because it only attacks when it notices that there is activity on the network. As the number of nodes in a network increases, so does the energy that the cooperative scheme consumes with respect to the collaborative one. Figure 9: RSSI for types of jamming in a WSN of 500 m\({}^{2}\) with different number of nodes under collaborative and cooperative schemes. The results obtained in the simulation of the performance metrics studied (PDR, RSSI and Energy) provide an approach to operating paradigms based on cooperation and collaboration, showing numerically an advantage of collaborative networks only when the network is experiencing a jamming attack: 4.9% improvement in RSSI metric when using a collaborative rather than cooperative network approach when being attacked by jamming, 4.4% improvement in PDR metric when using a collaborative network approach instead of cooperative when being attacked by jamming; and 10.7% energy is saved when using a collaborative rather than cooperative network approach when jammed.