esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed System Design Overview
### Optimised FTL-Assisted Edge IDS Learning  
Due to privacy constraints, training the learning model with an enormous amount of centralised data is impossible in distributed IIoT domains. Although FL-based models effectively address the privacy constraint, an inadequate number of standard datasets and a lack of consideration of the unique characteristics of the IIoT lead to inaccurate intrusion detection. Hence, the FT-CID model integrates the concept of TL with FL to build accurate IDS models with minimum learning time, with the advantage of utilising the learned knowledge of a source domain in various target domains in the TL model. The FT-CID model adopts FTL to resolve the security issues associated with a heterogeneous IIoT environment. To handle heterogeneous data in distributed IIoT networks, the FT-CID model exploits the FL and TL models to aggregate the attack data of the various RPL-IIoT network and ensure the design of customised edge IDS models, respectively. Federated learning consists of two learning models: server-side and edge-side models. During network initialisation, the central security server trains its global model on a publicly available source dataset and initialises the global learning model.  
The edge devices execute the distributed local learning models for the IIoT network dataset. With the assistance of TL, the edge devices transfer local models in terms of model parameters, such as the number of hidden layers, hidden neurons, and activation functions; logits in terms of probability values (the vector of raw predictions); and weights from the learning model to the central security server. Instead of sending all data to the central server for global model generation, the proposed FT-CID model transfers only the logit outputs of the specified CNN layer of the local learning models and sends the obtained local information as knowledge to the server. Moreover, the proposed model optimises the parameters of the local learning model in terms of the number of iterations based on the gradient obtained from the local learning results. During aggregation in the FT-CID model, the server aligns the local parameters in the local model by employing the degree of importance of factors with the attention mechanism and applies FedAVG [36] to the received models of different edges to generate a global model. The FedAVG method is responsible for updating the model multiple times before sending the learning model back to the central or global server for aggregation. The FL model ensures that the central server aggregates the parameters from the sensors to update the global model in each communication round. With the centralised characteristics of the FedAVG model, there is a need for a very high number of communication rounds between the sensor and the server in order to collect parameters of the sensors and distribute the parameters to each sensor. Finally, the global parameters are sent back to the edge of the IIoT network to optimise the local learning models and detect intrusions.
# Recognizing Events in Spatiotemporal Soccer Data
## 4 Game Event Detection Pipeline  
### Player Movements Analysis  
In our work, we are only interested in the basic approximation of actual player movements. We divide an input game segment into equal intervals of user-specified duration and calculate player velocity components \(v_{x}\) and \(v_{y}\) using Equations (1) and (2) according to their positions \(x(t)\), \(y(t)\) at the beginning and end of each time interval \([t_{0},t_{1}]\). Note that this approach represents player movements with straight lines, approximating the general trajectory (see Figure 1):  
\[v_{y}=\frac{y(t_{1})-y(t_{0})}{t_{1}-t_{0}} \tag{1}\]  
\[v_{x}=\frac{x(t_{1})-x(t_{0})}{t_{1}-t_{0}}. \tag{2}\]  
Even such a simple method allows us to study player movements with arbitrary precision and analyze their basic traits. In particular, we were able to distinguish real teams from teams comprised of rule-based AI bots by analyzing probability distributions of player movement directions in different zones of the game field [25].  
However, it should be mentioned that in general, the analysis of player movements is a more complicated process. In raw data captured by player tracking systems, jitter is inevitable, so certain smoothing algorithms are required. The choice of these algorithms, in turn, is not a trivial process, since sudden changes in speed and direction are very common in soccer, so smoothing may cause undesirable distortions [26]. One way of removing them is to use the Gaussian smoothing kernel [27], where \(x\) and \(y\) components of the trajectory are processed separately and treated as one-dimensional time-dependent signals, according to Equations (3) and (4):  
\[x^{\prime}(t)=\frac{1}{2N_{F}+1}\sum_{i=-N_{F}}^{N_{F}}x(t+i)\cdot G(i) \tag{3}\]  
\[y^{\prime}(t)=\frac{1}{2N_{F}+1}\sum_{i=-N_{F}}^{N_{F}}y(t+i)\cdot G(i). \tag{4}\]  
Here \(2N_{F}+1\) denotes the width of the kernel, \(x^{\prime}\) and \(y^{\prime}\) are the smoothed components of the trajectory, and \(x\) and \(y\) are the components of the raw trajectory. \(G\) is the set of Gaussian coefficients defining the shape of the kernel.
## 3 Materials and Methods
### A Computational Framework  
In this paper, a computational framework was built (Figure 2), which consists of two parts, described as follows: (1) an Excel spreadsheet was used to develop the order planning model, and calculate the response variables, i.e., total cost and customer service level. Then, (2) Minitab was used to find the feasible values of the optimal setting for those responses using RSM, CCD, and BBD. Besides, a VDG was applied to validate these experimental designs. Three independent variables were being considered to have effects on the response variables in this experiment, consisting of the review period (T, month), order quantity (Q, unit), and safety stock (SS, unit). The combination in the total cost (TC, USD) that includes the holding cost, shortage cost, over-storage cost, transportation cost, and customer service level (CSL, %) were chosen as response variables of the order planning model.
## 4 Smart5 Grid NetApp Use Cases
### UC4--Real-Time Wide Area Monitoring  
#### 4.4.1 Description  
The scope of UC4 is the real-time monitoring of a geographically wide area where cross-border power exchanges (or between two TSOs) take place. UC4 addresses the energy reliability and security domain of the broad energy vertical. Specifically, in the context of this UC, the interconnection flow between Greece and Bulgaria is monitored, leveraging the advantages provided by the 5G telecommunication infrastructure. This function can be executed from the newly established regional security center (RSC) in Thessaloniki, Greece. The role of the RSC is to promote regional cooperation and to support the strengthening of the neighboring power systems and market operations in the region. To achieve the enhancement of the interconnected power system operation, live monitoring of the power flows between the countries under its area of interest is of vital importance. Hence, this UC can be considered the development of an additional element that increases the live monitoring capability of the RSC. PMUs located at the HV network of northern Greece and Bulgaria, monitoring the interconnection area with Bulgaria, can be used as an input in the monitoring process of the RSC. By incorporating time-stamped synchronized PMU measurements with high data granularity (50 to 60 times per second), including positive, negative, and zero sequence phasors of voltage and currents can be achieved.  
A virtual phasor data concentrator (vPDC) will be developed for the data-gathering process. The utilization of 5G in UC4 contributes to the connectivity between the PMUs and the vPDC, offering the low latency and high reliability needed due to the criticality of the UC. In addition, 5G provides a low-cost, last-mile connection to the PMUs in remote areas where optical fiber has a high installation cost. Hence, the NetApp envisioned to be developed in the context of this UC, in order to fulfill the above-mentioned objectives, will cover three types of services--_vPDC service_, _WAM service_, _and advisory service_ to the involved TSOs.  
Figure 12: Site architecture of UC3.
# A Sub-50 nW Implantable Body Temperature Sensor and Its Application to a Rat
## 2 System Design
### Body Temperature Measurement Channel (CH_T) Design
At this time, the output \(V_{mm}\) of the BRG block is sampled and held in the SMP\({}_{\text{N}}\) block. The current flows through the thermistor (Rth) only during phase PHASE_P and phase PHASE_N, and during other measurement periods no current flows. For example, phase PHASE_P or phase PHASE_N is about 1 ms and the measurement period is 4 s. Therefore, the average current flowing through the thermistor Rth is 1/2000 of the peak current I\({}_{\text{Wth\_peak}}\). At a normal body temperature of 37 \({}^{\circ}\)C, the thermistor resistance is 57.6 k\(\Omega\) and the voltage applied to the voltage divider consisting of the thermistor and the trim resistor is 1.8 V. Therefore, the peak current flowing through the thermistor resistance Rth is 15.6 \(\upmu\)A, so the average current consumed by the BRG block is 7.8 nA. The common mode voltage of the BRG block, which uses 1.8 V as the operating voltage, is 0.9 V, but the common mode voltage of the VCO and TDC blocks, which use 0.6 V as the operating voltage, is 0.3 V, so the common mode voltage level of the input signal of the SMP block needs to be down-shifted by 0.6 V. In Figure 3b, at the time \(t_{2}\) (or \(t_{6}\)) when the phase PHASE_P ends, the output \(V_{mp}\) of the BRG is down-shifted by the amplitude of \(V_{LS}\) (0.6 V) at the moment of holding and is output as the output \(V_{CP}\) of the SMP block. Similarly, at the time \(t_{4}\) (or \(t_{8}\)) when the phase PHASE_N ends, the output \(V_{mm}\) of the BRG is down-shifted by the size of \(V_{LS}\) (0.6 V) at the moment of holding and is output as the output \(V_{CN}\) of the SMP block. The frequency of the output clock according to the input control voltage in the VCO is proportional but nonlinear. To improve the nonlinearity of the VCO, a differential  
Figure 3: Working principle of BRG and SMP blocks: (**a**) BRG configuration according to operation phase, (**b**) output signals of SMP blocks. configuration was used. In the BRG block, the signal \(V_{CP}\) obtained in PHASE_P controls VCO\({}_{\text{P}}\) to produce \(CLK_{\text{P}}\), and the signal \(V_{CN}\) obtained in PHASE_N controls VCO\({}_{\text{N}}\) to produce \(CLK_{\text{N}}\). The TDC block calculates the frequency difference between the two signals, and the nonlinearity common to VCO\({}_{\text{P}}\) and VCO\({}_{\text{N}}\) is canceled. Detailed circuit design and simulation results at the MOS level can be found in reference [13].
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
## 5 Analysis of Computational Results
### Settlement Comparison  
Four reference points were selected at the same locations as settlement monitoring points C32, C33, C34, C35, and C36. The simulated settlement values one year after the opening of subway train operation were compared with the measured settlement values one year after the opening of the uphill tunnel with a curve radius of 350 m. The comparison is shown in the following Figure 25.  
Figure 24: Comparison of vertical deformation peak of single loading at different speeds.  
Figure 25: Comparison between measured and simulated settlement values on the upward track of the tunnel.  
It can be observed that the settlement values gradually increase along the subway route, and the change trend is generally consistent. The absolute average relative error between the simulated settlement values and the measured settlement values is 8.20%, which is within a reasonable range, thus verifying the reliability of the model. However, the magnitude of settlement variation in the numerical simulation results is slightly smaller. This is attributed to the analysis of simulated settlement calculations from the perspective of engineering precision, assuming that soil consolidation has been completed and neglecting consolidation settlement caused by dissipation of pore water pressure. In the actual operational process, apart from cumulative plastic deformation caused by train vibrations, there is also a small amount of consolidation settlement. It is also possible that the discrepancy arises from the fact that the actual scenario involves a dual-tube single-track tunnel structure, while the numerical model assumes a single-tube single-track tunnel structure.
## 4 Experimental Results and Discussion
### Throughput  
The suggested technique is compared with a cross-layer-based adaptive threshold and cross-layer fuzzy logic techniques in Table 5.  
Figures 7 and 8, and Table 5, show the evaluation of the proposed technique and the conventional algorithms with respect to end-to-end delay and throughput. The E-CERP approach's throughput was high (0.99 Mbps) in 100 nodes compared to the conventional algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Techniques** & \multicolumn{4}{c}{**Average Energy Consumed**} & \multicolumn{4}{c}{**Network Lifespan**} \\ \hline
**Number of Nodes** & **100** & **200** & **300** & **400** & **500** & **100** & **200** & **300** & **400** & **500** \\ \hline Proposed CERP & 1.97 & 1.10 & 5.13 & 4.80 & 4.19 & 5908 & 5711 & 5514 & 5218 & 5022 \\ \hline CLAT & 7.75 & 5.51 & 7.91 & 7.45 & 7.55 & 5395 & 5100 & 4904 & 4708 & 4610 \\ \hline CLFL & 8.43 & 10.36 & 9.53 & 11.23 & 10.37 & 4904 & 4708 & 4610 & 4218 & 4021 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy consumption and network lifespan.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & \multicolumn{4}{c}{**Throughput**} & \multicolumn{4}{c}{**End-to-End Delay**} \\ \hline
**Number of Nodes** & **100** & **200** & **300** & **400** & **500** & **100** & **200** & **300** & **400** & **500** \\ \hline Proposed CERP & 0.99 & 0.98 & 0.96 & 0.95 & 0.94 & 0.0492 & 0.0689 & 1.9692 & 3.9384 & 5.9076 \\ \hline CLAT & 0.96 & 0.93 & 0.93 & 0.93 & 0.91 & 1.9617 & 3.6785 & 4.1686 & 5.1494 & 6.1302 \\ \hline CLFL & 0.95 & 0.91 & 0.90 & 0.90 & 0.88 & 2.9425 & 4.9042 & 5.8851 & 7.3563 & 8.8276 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Throughput and the end-to-end delay comparison.  
Figure 7: Throughput comparison.  
From Table 5, it is seen that the number of nodes increases as the throughput decreases. The throughputs obtained by the conventional cross-layer-based adaptive threshold technique and cross-layer fuzzy logic approach are 0.99 Mbps and 0.96 Mbps, respectively, for 100 nodes.
## 3 System Overview  
### Application Scenario Overview  
In time banking applications, we need to investigate the service-exchange process the system executed. Currently, the critical business of a TimeBank is to provide services in exchange for time credits, where one can use time credits to exchange for other people's services. Before introducing the service-exchange process of a TimeBank, four conventions need to be addressed first:  
* \"Service\" represents a collective term of different useful and desirable skills for disabled or aged people, such as cleaning a house, repairing a machine, driving to a hospital, and so on.
* The time credit value is measured according to the service's time spent, regardless of the service's substances. For example, cleaning a house for one hour gets the same time credit as taking care of an elderly citizen for one hour.
* Members of the system can be divided into two clusters: The Service Providers (SPs) and the Service Receivers (SRs). SPs provide services in their spare time, while SRs seek assistance from others in their required instance.
* The provision of services is not immediate. For example, Uber is a kind of prompt service provider. However, in a TimeBank, if [PERSON] needs someone to help babysit his child on Saturday, [PERSON] has to issue the request a few days before.  
In the proposed TimeBank system, SPs and SRs match the supply-and-demand services on the Fabric blockchain. The service-exchange process of the system consists of the following three main steps:  
1. Step 1: Members need to post their service inquiries to the blockchain, where the service inquiries have two types. SPs post the supply inquiry, and SRs post the demand inquiry. Then, the service inquiry reveals the user's specific content of the supply or demand service, and it will be posted to the blockchain through the client-side application software, as shown in Figure 1.
2. Step 2: The supply-and-demand service inquiry keeps looking for candidates who match up with the matching criteria until a matching strategy is pursued. In this work, a market thickness-based dynamic tuning matching strategy is adopted. In a nutshell, each service inquiry decides when to match with others, according to the market's thickness.
3. Step 3: Before SP provides the pre-negotiated service to SR, the TimeBank system will help SP check SR's account balance to ensure it has enough time credit for paying the mandatory service charge. Similarly, the TimeBank system will help SR send the pre-negotiated time credit to SP after SP did complete the service.
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 2 Background on Low-Precision Neural Networks
#### Fixed-Point Representation for Analog Photonic Engines  
Although the third approach (i.e., extreme quantization) is really appealing, binary and ternary representations are too drastic for an optical implementation, since the utilized optical technologies typically allow one to resolve more than two or three levels; moreover, they increase the complexity of the training with ad hoc learning algorithms. Therefore, in this work, we exploit the second approach, i.e., low-precision fixed-point real numbers or integers. This representation is inherently used by analog processors, such as the reported linear optical processors. Indeed, although analog signals can theoretically assume a continuous set of values, the ability to distinguish between them is limited by noise and distortions; this results in a finite and equally spaced set of numerical values of the output signal that can be distinguished.  
In this scenario, the DNN can be trained on a CPU using a fixed-point representation for its weights and activations (but not for the gradients), which can be then utilized in the photonic neural networks. This neural network will then perform the inference at a much faster rate and at a fraction of the power consumption compared to electronic solutions. The problem of training such a network directly in photonics [3] is outside the scope of this work.
## 5 Experiments and Results
### Classifier Evaluation Metrics  
The performance metrics are mainly composed of two parts: (1) the time efficiency of the classifier, and (2) the accuracy of the classifier. The time efficiency of classification refers to the time it takes to train a classifier and the time it takes to predict the category of a sample using the classifier. The correctness of the classifier includes three criteria: accuracy, precision, and recall. Accuracy is used to measure the overall classification effect of the classifier, and precision and recall measure the classification effect of a single protocol category of the classifier. The calculation formula is shown in Formulas (8)-(10):  
\[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}} \tag{8}\]  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
The meanings of TP, TN, FP, and FN are shown in Figure 5.
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
## 4 Experiment Details and Dataset Brief
### Performance Evaluation  
To assess the effectiveness of our approach, we evaluated the accuracy and efficiency of the deep learning models using two popular hyperparameter-optimisation techniques, covering arrays and grid search. We compared the performance of the baseline models against the optimised models, considering both accuracy and efficiency metrics. Specifically, we focused on optimising the selected hyperparameters. To ensure that the models are not overfitted during the tuning process, we employed several techniques, such as early stopping, which stops the training process when the model performance on the validation set starts to deteriorate, and regularisation techniques, such as dropout relay. Additionally, the model's performance was evaluated on a separate test set to ensure that it generalises well to unseen data.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
## 6 Detection and Mitigation
### Detection Mechanisms--Techniques
#### 6.1.7 Vehicle Networks  
With the development of autonomous vehicles, vehicular ad hoc networks (VANETs) have been designed to provide traffic safety by allowing the ad hoc transmission of safety information between vehicles. This additional communication makes VANETs a likely target for malicious attackers. Reference [198] introduces novel attack VANETs and propose a honeypot approach to notify nearby vehicles to ignore messages stemming from vehicles infected by botnets. The paper also proposes the use of localisation mechanisms to limit the exposure to far-away botnets. Reference [12] introduces _Shieldnet_, which employs a set of machine learning algorithms to detect the use of the _GHOST_[81] vehicular botnet. The algorithm detects suspicious activity by searching for outlier data within the Basic Safety Messages (BSM) fields of VANET broadcasts, also isolating known infected hosts using a reputation-based identification system.