esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methods
### Optimization Methods
#### 4.4.2 Thread Binding and Affinity  
The operating system is able to move a thread around from its initial position while executing the code. This may cause performance degradation by losing both locality and data in caches. It is possible to prevent the system from completing such an operation by using the strategy of thread binding; it consists of binding threads to a specific core processor, a hardware thread, or a socket. OpenMP offers two environment variables to bind threads:  
OMP_PLACES: The variable specifies on which CPUs the thread should be placed. This operation can be conducted either by using an abstract name, such as _threads, cores, sockets_, or by an explicit list of the places. With _threads_, each place is a single hardware thread, and, with _cores_, each place is a single-core processor, which may correspond to one or more hardware threads. With _sockets_, the places correspond to a single socket in a multi-socket system. OMP_PROC_BIND: This variable indicates if a thread may be moved between CPUs. It can be set to _false, true, primary, master, close_, and _spread_. If set to _true_, the OpenMP thread should not be moved from its initial position. With _primary_ and _master_, the worker threads are in the same partition as the master thread. With _close_, they are kept close to the partition of the master thread, in contiguous place partitions, which may be beneficial for data sharing and reuse. With _spread_, a sparse distribution within the place partitions is used, which may offer higher bandwidth since we use cores from both sockets.  
For GRICS, controlling the number of OpenMP threads is conducted at the compile stage, with dedicated compilation flags. When it comes to thread binding, two lines were added before the execution of the code:  
* export OMP_PLACES = threads, cores, or sockets.
* export OMP_PROC_BIND = spread or close.  
Hence, these optimizations do not require a source-code modification.
# An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
## 4 Experimental Results and Discussions
### Comparison with other Existing Models Using the Same Dataset  
To emphasize how crucial, it is to use feature selection on the dataset before applying classification models, the baseline and other existing techniques were utilized to compare the proposed model to them. Table 13 compares the outcomes obtained from IDS models proposed with other existing state-of-the-art models based on ToN-IoT datasets. Each row of Table 13 shows a group of various ML-based models from some other notable studies. The use of the ToN-IoT dataset is quite recent in the study of IDSs. Hence, the number of studies associated with network security is very few in this dataset. Worthy of being mentioned is the work of [3], which is the baseline study, where eight (8) ML models were considered. Still, LSTM performed better on four (4) of the datasets. CART performed best on two (2) datasets, and k-NN performed best on one (1) dataset based on accuracy metrics. In [36], the authors used six different ML-based models to classify the dataset, and the study results revealed that the RF classifier performed better on five (5) datasets.  
In contrast, DT performed best on two (2) datasets. The authors in [37] make use of two ML-based models for the classification of the datasets, and the results show that the VC classifier performed better in four (4) datasets, while RF did very well in two (2) datasets. In a similar work by authors in [38], they used six (6) ensemble classifiers for IDS detection. The CB classifier has the best accuracy across all the types of datasets in the TON_IoT Telemetry Dataset used to test the performance of the classifiers used.  
Therefore, the comparison in Table 13 revealed that the proposed model performs reasonably better in terms of accuracy when compared with other existing classifiers and the baseline model. As a result, the model performs best when applied to a real-world IIoT ecosystem that contains vast amounts of unstructured and unlabeled datasets. Furthermore, feature selection considerably reduces the computational time needed to process the dataset compared to the baseline. Consequently, data dimensionality is automatically reduced, and high-level functioning is examined efficiently and precisely. Although Table 13 shows that our results appear to be comparable to other research in the field, our suggested approach has been examined on a more pertinent dataset using feature selection to see how the dataset will respond to the model. So, compared to other relevant research, our results would be more trustworthy.
## 4 Case Study  
Let us now illustrate the proposed algorithm using a real-life example of a concurrent system. The case study shown in this paper presents a Home Area Network (HAN) that was initially proposed by one of the authors in a study [56]. The system is designed to manage digital devices that are commonly used in the home [57; 58; 59]. The HAN system enables smart management of digital devices typically found in homes, such as lights, alarms, and appliances, with the main objectives of ensuring resident safety, improving convenience, and reducing electricity demand on the grid. It controls various sensors and devices in a small apartment, including hall, bedroom, bathroom, living room (with an open kitchen), and balcony. Let us describe the system in more detail, and then we will move on to the Petri net model and its verification.
## 3 Metrics for Jamming Attack Detection
### Standard Deviation in Received Signal Strength (SDRSS)  
[PERSON] _et al._[4] have suggested a system where the node samples its received legitimate signal, called the clean signal, over a period of time and finds its standard deviation (\(\sigma\)) during the period. It then samples the abnormal signal, called the jammed signal, and finds its mean deviation (d) from the clean signal over the same period of time. The calculation of \(\sigma\) and d are done as per formulae [4]. If d \(\leq\sigma\), then there is no jamming; else, there is jamming. Although we will discuss SDRSS subsequently under the method suggested by [PERSON] _et al._[4], we do not find it suitable for the WSN due to: (1) it cannot work if the jammer is transmitting at a power level equal to the normal transmitted power level of the nodes, as it would do during many types of jamming attacks, like deceptive jamming, as discussed above, (2) it involves sampling at the node level, and (3) it is computationally taxing for a WSN node.
## 2 The Proposed Pedestrian Attributes Recognition Method
### Integration of Pedestrian Attribute Labels  
In this paper, the pedestrian's original attributes labels are integrated to form a vector. In this way, multiple attributes labels for each pedestrian image will be replaced by a vector.  
Suppose there are N images to be converted, and L attributes corresponding to each image, including gender, age range, hair length, clothing color, clothing species, and so on.  
Each sample is represented b \(x_{i},i\in[1,2\cdots,N]\), and the corresponding attribute vector is \(y_{i}\). The attribute value corresponding to \(y_{i}\) is \(y_{i,l}\), \(y_{i,l}\in[0,1],l\in[1,2\cdots,L]\). According to the original annotated label, if the pedestrian sample contains this attribute, then \(y_{i,l}=1\); otherwise \(y_{i,l}=0\).  
In the PETA dataset, there are 61 binary attributes and four multi-class attributes. After combining the attributes of the pedestrian image, a label vector is generated, in which four multi-class attribute labels are also labeled in the form of binarization, and each multi-class attribute can be further divided into 11 binary attributes. Thus, each pedestrian image sample corresponds to a \(61+11\times 4=105\) dimensional label vector.  
In the RAP dataset, there are 69 binary attributes and three multi-class attributes. The same processing method is adopted, and each image corresponds to a 92 dimensional label vector. In the process of integration, the position of each attribute in the label vector is fixed.  
An example is shown to introduce how to integrate the attributes labels. A pedestrian sample in the PETA dataset is shown in Figure 2. The original labels are: upperBodyWhite, lowerBodyBlack, hairBlack, footwearWhite, lowerBodyCasual, lowerBodyTrousers, personalLess30, personalMale, upperBodyCasual, upperBodyLongSleeve, hairShort, footwearSneakers, carryingBackpack and accessoryMuffler. After integration, a 105-dimensional label vector is obtained, which is specifically shown as:
# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 3 The Proposed Algorithm
### Population Size Reduction Scheme  
For the overall performance of the algorithm, the population reduction scheme is effective and practical, and it is a simple scheme. Commonly, a population size reduction scheme can achieve a balance between the diversity of trial vectors and the number of generations. Accordingly, in this paper, we employed a simply linear population size reduction strategy to improve the performance of the proposed algorithm. After each generation \(G\), the population size in the next generation \(N_{G+1}\) is computed according to Equation (11):  
\[N_{G+1}=round\bigg{[}\bigg{(}\frac{N^{\min}-N^{init}}{MAX\_NFE}\bigg{)}\times NFE +N^{\text{init}}\bigg{]} \tag{11}\]  
The population size of the first generation is \(N^{init}\), the population at the end of the run is \(N^{\min}\), \(N^{\min}\) set as the minimum possible value, \(NFE\) is the current number of fitness evaluations. \(MAX\_NFE\) is the maximum number of fitness evaluations. Whenever \(N_{G+1}\)\(<N_{G}\), the (\(N_{G}-N_{G+1}\)) are the worst ranked individuals which will be removed from the population.  
To show the improved QUATRE algorithm more intuitively, the following Algorithm 1 is the pseudo code of the improved QUATRE algorithm.  
```
Input: Bound constraints [\(R_{D,\min}\),\(R_{D,\max}\)], the fixed maximum number of function evolution \(n\#\emph{e}_{max}\); Output: Best individual \(X_{\text{gbst}}\), Best fitness value \(f(X_{\text{gbst}})\), number of function evaluation \(n\#\emph{e}\);
1. Initialize the population size \(p\)s, scale factor \(F\), inheritance probability \(P\), all individuals \(X=\{X_{1},X_{2},\ldots,X_{p\text{s}}\}\), \(A=\emptyset\), \(r^{n\pi}=1.6\), \(p_{\max}=0.2\), \(p_{\min}=0.05\), \(G=1\);
2.while\(n\#\emph{e}\leq n\#\emph{e}_{\max}\)do
3.for\(i=1\); \(i\leq p\)s; \(i++\)do
4. Generate \(X_{\text{best},G^{\prime}}^{p}\), \(X_{r_{1},G}\) and \(\widehat{X}_{r_{2},G}\);
5.ifG > 2 then
6. Adjust the population size;
7. Adjust the size of storage \(A\);
8.endif
9. Generate \(F_{i}\) for ith individual;
10. Readjust \(F_{i}\) into the bound constraints if necessary;
11.endfor
12. Generate evolution matrix \(M\) according to the adopt scheme;
13. Generate \(B\) according to Equation (6);
14. Generate trial candidates by employing evolution matrix \(M\);
15. Calculate fitness values of all \(U_{i,G}\);
16.\(n\#\emph{e}=n\#\emph{e}+p\)s;
17.for\(i=1\); \(i\leq p\)s; \(i++\)do
18.if(\(U_{i,G}\)) \(\leq f(X_{i,G})\)then
19.\(X_{i,G+1}=U_{i,G}\);
20.else
21.\(X_{i,G+1}=X_{i,G}\);
22.endif
23.endfor
24.if\(S_{F}\
eq\emptyset\)then
25. Update \(X_{\text{F}}\);
26. Update \(P\) (\(A=k\)) (\(k=1\),\(2\), \(\ldots,D\));
27.endif
28. G = G + 1;
29. Update storage \(A\);
30.endwhile
31.\(f(X_{\text{gbst}})=f(X_{\text{gbst},G})\), \(X_{\text{gbst}}=X_{\text{gbst},G}\);
32. Return \(X_{\text{gbst}}\), and \(f(X_{\text{gbst}})\);
```  
**Algorithm 1** Pseudo Code of the L-EQUATRE Algorithm.
## 4 Analysis of Energy Storage System
### SOC Estimation
We evaluated the relationship between battery voltage and battery capacity from \(-50\)\({}^{\circ}\)C to \(0\)\({}^{\circ}\mathrm{C}\). However, the values at the non-measured battery voltage and battery capacity could be predicted by interpolation. Equation (11) can be described as follows. \[ \begin{array}{l}U(T_{i})=a(T_{i})SOC^{6}(T_{i})+b(T_{i})SOC^{5}(T_{i})+c(T_ {i})SOC^{4}(T_{i})\\ +d(T_{i})SOC^{3}(T_{i})+e(T_{i})SOC^{2}(T_{i})+f(T_{i})SOC(T_{i})+g(T_{i}) \end{array}  \tag{12}\]  
where \(U(T_{i})\) is the battery voltages at different temperatures from \(-50\)\({}^{\circ}\mathrm{C}\) to \(0\)\({}^{\circ}\mathrm{C}\); \(SOC(T_{i})\) is the battery capacity at different temperatures from \(-50\)\({}^{\circ}\mathrm{C}\) to \(0\)\({}^{\circ}\mathrm{C}\); \(a(T_{i})\), \(b(T_{i})\), \(c(T_{i})\), \(d(T_{i})\), \(e(T_{i})\), \(f(T_{i})\), and \(g(T_{i})\) are the coefficients of the Equation (12), which have dependences of low temperatures. The coefficients \(a(T_{i})\), \(b(T_{i})\), \(c(T_{i})\), \(d(T_{i})\), \(e(T_{i})\), \(f(T_{i})\) and \(g(T_{i})\) from \(-50\)\({}^{\circ}\mathrm{C}\) to \(0\)\({}^{\circ}\mathrm{C}\) are shown in Figure 6.  
Figure 5: (**a**–**f**) show the relationship between battery voltage and battery capacity at \(-50\)\({}^{\circ}\)C, \(-40\)\({}^{\circ}\)C, \(-30\)\({}^{\circ}\)C, \(-20\)\({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, \(0\)\({}^{\circ}\)C, respectively. Figure 6: (a–g) show the coefficients \(a(T_{i})\), \(b(T_{i})\), \(c(T_{i})\), \(d(T_{i})\), \(e(T_{i})\), \(f(T_{i})\) and \(g(T_{i})\) from \(-50\)\({}^{\circ}\)C to \(0\)\({}^{\circ}\)C, respectively. As shown in Figure 6, the temperature dependence of the coefficients of \(a(T_{i})\), \(b(T_{i})\), \(c(T_{i})\), \(d(T_{i})\), \(e(T_{i})\), \(f(T_{i})\) and \(g(T_{i})\) is generally in the form of a cubic functions. The coefficients \(a(T_{i})\), \(b(T_{i})\), \(c(T_{i})\), \(d(T_{i})\), \(e(T_{i})\), \(f(T_{i})\) and \(g(T_{i})\) in Equation (12) at different temperatures could be predicted as follows. \[\left\{ \begin{array}{l}a(T_{i})=-9.96\times 10^{-13}T_{i}^{3}-4.8\times 10^{-11}T _{i}^{2}-9.28\times 10^{-10}T_{i}+4.27\times 10^{-9}\\ b(T_{i})=4.52\times 10^{-10}T_{i}^{3}+2.19\times 10^{-8}T_{i}^{2}+4.28\times 10^{-7 }T_{i}-1.96\times 10^{-6}\\ c(T_{i})=-8.46\times 10^{-8}T_{i}^{3}-4.11\times 10^{-6}T_{i}^{2}-8.17\times 10^{- 5}T_{i}+3.69\times 10^{-4}\\ d(T_{i})=8.41\times 10^{-6}T_{i}^{3}+4.1\times 10^{-4}T_{i}^{2}+8.25\times 10^{- 3}T_{i}-0.037\\ e(T_{i})=-4.66\times 10^{-4}T_{i}^{3}-0.023T_{i}^{2}-0.4657T_{i}+2.066\\ f(T_{i})=0.0137T_{i}^{3}+0.675T_{i}^{2}+13.8716T_{i}-60.908\\ g(T_{i})=-0.1654T_{i}^{3}-8.2046T_{i}^{2}-170.3108T_{i}+741.803\end{array} \right. \tag{13}\]
## 5 Models for the Effective SNR  
The capacity of CA has usually been investigated in the literature by characterising the capacity of each CC individually as an independent channel and then adding up the individual capacity of each CC. This approach may be suitable to calculate numerically the aggregate sum-rate in optimisation-based studies (e.g., [30; 31]); however, the involved mathematical expressions provide little insight into how the total capacity depends on each relevant parameter. In order to investigate the capacity of CA as a diversity technique, the analysis carried out in this work considers a different approach based on the concept of _effective SNR_, which is defined in this study as the equivalent SNR at which a single channel with bandwidth \(B\) achieves the same performance as the considered CA scenario with a total aggregated bandwidth \(B\) divided across \(N\) CCs and instantaneous channel quality conditions represented by the set \(\{\gamma_{n}\}_{n=1}^{N}\) of SNR values in each CC.  
The introduction of this definition of effective SNR provides a convenient and useful new tool for the analysis of CA in general, and as a diversity technique in particular. However, this concept also raises the question of how to map a set \(\{\gamma_{n}\}_{n=1}^{N}\) of \(N\) SNR values in each CC into a single effective SNR \(\gamma_{\text{CA}}\) that results in the same performance in the case of a single channel. This is an extremely challenging problem given the broad range of processes at the MAC and PHY layers that determine the performance of CA (packet scheduler, HARQ retransmission process, transmission power control, dynamic adaptation of the modulation and coding scheme, etc.) and the many different algorithms that can be implemented for each such MAC/PHY process. For this reason, an exact mapping of the individual SNR in each CC into a single effective SNR, while highly desirable, seems unfeasible. To overcome this limitation, this work considers two models for the effective SNR, which are presented below. These models are sufficient for the purposes of this study, and the development of more accurate models is proposed as future work.
## 3 Synthetic Examples
### The Double Linear Chirped Signal  
Linear frequency-modulated (FM) signal is a generally accepted model that is used to test the performance of time-frequency distribution. The double linear chirped signal shown in Figure 1 is composed of two crossing linear chirps whose main frequencies are linearly increasing and decreasing with time, respectively.  
The CWT, GST, SSCWT, and SSGST methods are applied to this signal, and the relative results analysis are shown in Figure 2. As can be seen, although all energy centers around the true instantaneous frequencies of the signal, the energy of the CWT and GST results smears heavily (Figure 2a,b). In contrast, due to the effect of the \"squeezing,\" SSCWT and SSGST squeeze all time-frequency coefficients into the time-frequency trajectory, which makes the spectra more energy-concentrated, as displayed in Figure 2c,d. In other words, SSCWT and SSGST show higher time-frequency resolution than CWT and GST. Furthermore, compared with the result of SSCWT, SSGST shows that the signal energy is much more concentrated around the ridge and that the time-frequency resolution performs better. The arrows show that there is no distinct \"steps\" in the SSGST result compared with the result of SSCWT.  
Figure 1: The double linear chirped signal.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 4 The CHaChA Clustering Approach
### Online Cluster Adaptation
#### 4.4.5 Re-Clustering  
Based on the concepts for subsequent cluster joining, isolation and failure detection, and cluster balancing, it is possible to respond to topology changes and connectivity problems even after the initial clustering phase has been completed. For example, subsequently installed devices can automatically integrate into an existing network, which is capable of independently balancing a resulting imbalance in cluster sizes. Similarly, individual CH failures can be compensated by moving the associated CMs to surrounding clusters.  
Beyond that, however, scenarios are conceivable that require a revision of the overall cluster constellation. One possible reason are severe topology changes that might be triggered, e.g., by the addition of a large number of nodes, frequent CH failures, or isolation of larger sub-networks. There could also be a persistent imbalance in cluster sizes that cannot be addressed by roaming due to an absence of cluster edge nodes.  
Distributed, asynchronous problem handling by individual nodes may not always be able to produce a cluster constellation suitable for the particular use case. A more reliable alternative here is the initiation of re-clustering. In CHaChA, detecting the need for re-clustering as well as its coordinated announcement in the network are conceptually reserved for CHs. Possible detection criteria are the evolution of the total number of nodes (network size \(N\)) or the CM/CH ratio since the last cluster formation.  
No re-clustering mechanisms have yet been implemented and tested in the context of this work, but they are feasible based on the previously described concepts. After detecting a condition for re-clustering, CHs take over its initiation on the base channel (primary interface). An intuitive approach is to send PHASE_0 broadcast messages, analogous to the control of phase transitions by the MCH in the clustering phase sequence. This causes a coordinated switching of all nodes into phase 0, whereupon re-clustering is performed. Even during re-clustering, connectivity in the network is always maintained via the statically configured primary interface on the base channel.
## 2 Materials and Methods
### Datasets Used in the Study
#### 2.1.2 Terrestrial Biosphere Parameters from MODIS  
The satellite observations related to terrestrial biosphere activities and land surface temperature used are obtained from MODIS onboard the Terra or Aqua. These data include MYD13C2-NDVI, MYD13C2-EVI, MCD15A2-LAI, MOD17A2-GPP and MOD11C3-LST. NDVI and EVI are the vegetation indices positively related to the amount of the biomass and strength of vegetation photosynthesis [28], LAI indicates the state of plant growth [30], GPP represents the ability of plants to capture carbon [29], and LST affects the soil and plant respiration rates and ecosystem photosynthesis [33]. It has been shown that, in the tropical areas, the net effect of increasing LST is increasing atmospheric CO\({}_{2}\)[34]. A detailed description of all the used datasets is shown in Table 1.
# Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm
## 3 Preliminaries
### Correlation Electromagnetic Analysis (CEMA)  
CEMA is another classical method which adopts the idea of divide and conquer in electromagnetic analysis. Its main target is to find the correlation between the hypothetical electromagnetic leakage \(H\) and the actual electromagnetic leakage traces \(T\) of a cryptographic device. In our research, the hypothetical electromagnetic leakage \(H\) is assumed to be equal to the predicted subkey intermediate output's Hamming distance (HD) \(W\). Hamming distance from the current state \(h\) to next state \(s\) is given as  
\[W=HD(h,s)=HW(h\oplus s) \tag{5}\]  
where \(HW\) represents Hamming weight, \(\oplus\) is exclusive-OR operation. Usually, \(h\) and \(s\) have a functional relationship  
\[s=f(h,x) \tag{6}\]  
where \(x\) is a byte of the key.  
Pearson correlation coefficient is applied to predict the correlation between \(W\) and \(T\). In a real scenario, a set of \(N\) electromagnetic traces \(T\) and \(N\) associated intermediate output's Hamming distance \(W\), under the uncorrelated noise assumption, the estimated correlation factor \(\rho_{TW}\) is given by the following formula:  
\[\rho_{TW}=\frac{N\sum_{i=1}^{N}T_{i}W_{i}-\sum_{i=1}^{N}T_{i}\sum_{i=1}^{N}W_ {i}}{\sqrt{N\sum_{i=1}^{N}T_{i}-(\sum_{i=1}^{N}T_{i})^{2}}\sqrt{N\sum_{i=1}^{ N}W_{i}-(\sum_{i=1}^{N}W_{i})^{2}}} \tag{7}\]  
where \(W=[W_{1},W_{2},\cdots,W_{N}]\), \(W_{i}\) is the \(i\)th intermediate output's Hamming distance, the summations are taken over the \(N\) traces \(T=[T_{1},T_{2},\cdots,T_{N}]\) and the estimation has to be done at every time slot within the electromagnetic traces \(T_{i}\).  
We can guess the key bytes and calculate \(\rho_{TW}\) for every candidate to find the right key. The value of \(|\rho_{TW}|\) is larger than others when the right key occurs.