esa-sceva/satcom-chunk-collection huggingface dataset:
# Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
## 2 Background
### Datasets  
There are several datasets used in the IDS literature, such as the KDD-CUP-99, the UNSW-NB15, the CIC-DoS-2017, and the CSE-CIC-IDS [10]. Nonetheless, a primary challenge of applied machine learning in IDS is the lack of publicly available and realistic datasets that can be used for model training and evaluation [10,14]. Organisations are not willing or allowed to disclose network traffic data as they can reveal sensitive information about the network configuration and raise privacy concerns [30].  
Moreover, several datasets have been used to study adversarial attacks in the relevant literature, such as the KDD-CUP-99, the UNSW-NB15, the CIC-DoS-2017 and the CSE-CIC-IDS [31]. The KDD-CUP-99 dataset is the most widely researched dataset for the evaluation of IDS models [15], containing seven weeks of network traffic [8]. However, the KDD-CUP-99 dataset is heavily criticised by researchers stating that the data is inadequate for machine learning models due to the high number of redundant rows [9]. The UNSW-NB15 [32] collected data using various tools such as IXIA PerfectStorm, Tcpdump, Argus, Bro-IDS, in order to identify nine attack factors Fuzzers, analysis, backdoors, DoS, exploits, generic, reconnaissance, shellcode and worms [32]. The CIC-DoS-2017 [33], is focused on DoS attacks due to the recent increase in these attacks [33]. The CSE-CIC-IDS [34] consists of seven attack categories: Brute-Force, Heartbleed, Botnet, DoS, Distributed Denial of Service (DDoS), Web Attacks and Insider Attacks.  
Until recently, there have been no specific IoT datasets proposed for machine learning IDS evaluation [15]. The Bot-IoT dataset is the first to focus on an IoT network architecture and includes five different attacks; DDoS, DoS, operating system and service scans, key-logging, and data exfiltration [14]. The Bot-IoT dataset makes use of the Message Queuing Telemetry Transport (MQTT) protocol--a lightweight messaging protocol used in IoT architectures [15]. There are over 72 million rows of data in the complete dataset; therefore, a 5% version has also been released for more manageable use. The dataset released with 46 features, and the authors proposed a top-10 feature list using the Pearson Correlation Matrix and entropy values [14]. There have been works demonstrating the efficacy of both traditional and deep learning techniques with this dataset [5].
# A High-Performance Thin-Film Sensor in 6G for Remote Sensing of the Sea Surface
## 3 Results and Discussion
The stability of the samples was tested. After two months of exposure to complete air in an environment with a humidity of 70-80% and a temperature of 30 \({}^{\circ}\)C, there was no significant change in the light and dark currents of the device. Therefore, crystalline Te  
Figure 4: Detection performance for the 100 \({}^{\circ}\)C annealing temperature, 200 \({}^{\circ}\)C annealing temperature, and no annealing temperature Te films at 0.1 THz. (**a**) Detector total noise (_Vnt_); (**b**) photoresponsivity (\(R_{A}\)); (**c**) noise equivalent power (_NEP_); (**d**) detectivity (\(D^{*}\)). Typical calculated (lines) and measured (dots). Figure 5: Response times of different Te films. films are well suited for use in real humid environments and are very suitable for use as detectors for ocean remote sensing. In addition, we compared the main performances of THz detectors in recent years, as shown in Table 1. Due to the limited application of ocean remote sensing technology, it must be employed in humid environments with water vapor and a large effective detection area, at least at the millimeter level. Furthermore, its photoresponsivity and NEP have certain advantages. Considering the criteria of mass production, room temperature stability, and use in humid environments, our Te thin-film detector is more appropriate for environments in which marine remote sensing technology is used. Therefore, the simple crystalline Te thin film is an ideal sensor for 6G ocean remote sensing technology.
## 4 Experiments Analysis and Discussion
### Performance in Real Industry Scenarios  
In addition to the conventional publicly available benchmark datasets, we further evaluated the effectiveness of TSNN in real-world scenarios in Figure 6. The experimental data, specifically the EAR5 meteorological dataset, was provided by our collaborators and includes wind speed and temperature data from 3850 stations over a period of 2 years.  
The experimental results also indicate that our approach effectively utilizes short-term dependencies to correct long-term prediction biases, thereby successfully overcoming the predictive limitations of the benchmark iTransformer model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**ECL**} & \multicolumn{2}{c}{**Exchange**} & \multicolumn{2}{c}{**Exchange**} \\  & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline iTransformer & 0.192 & 0.280 & 0.338 & 0.422 & 0.283 & 0.300 \\ TSNN-ESTD w/o Ensemble & 0.189 & 0.279 & 0.332 & 0.417 & 0.275 & 0.295 \\ TSNN-ESTD w/o LSTM & 0.171 & 0.260 & 0.228 & 0.348 & 0.218 & 0.261 \\ TSNN-ESTD w/o MLP & 0.170 & 0.261 & 0.211 & 0.341 & 0.215 & 0.260 \\ TSNN-ESTD & 0.164 & 0.259 & 0.173 & 0.314 & 0.203 & 0.255 \\ \hline \end{tabular}
\end{table}
Table 4: Ablation study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & **ITransformer** & **TSNN-ESTD** & **Paired Difference** & **t** & \(p\) & **Cohen’s d** \\  & **(MSE)** & **(MSE)** & **(MSE)** & **(MSE)** & **(MSE)** & **(MSE)** \\ \hline ECL & 0.185 \(\pm\) 0.005 & 0.162 \(\pm\) 0.004 & 0.024 \(\pm\) 0.001 & 65.691 & \(p\) \(<\) 0.001 & 9.29 \\ Exchange & 0.326 \(\pm\) 0.004 & 0.166 \(\pm\) 0.002 & 0.161 \(\pm\) 0.002 & 449.098 & \(p\) \(<\) 0.001 & 63.512 \\ Weather & 0.319 \(\pm\) 0.078 & 0.228 \(\pm\) 0.047 & 0.091 \(\pm\) 0.030 & 21.034 & \(p\) \(<\) 0.001 & 2.975 \\ \hline \end{tabular}
\end{table}
Table 5: Statistical significance testing.
## 3 Results and Discussion
### Exposure Assessment
#### 3.3.1 Health Effect #01

Table 4: Estimated daily intake of active substances for two recall periods, scenario “as is”, and ’health effect #1’. our study for both recall methods are higher, indicating lower risk for 'health effect #1'. However, it must be noted that hypothyroidism is now considered a common diseases worldwide [40] related to the thyroid gland and low thyroid hormone level, affecting quality of life in many ways [41]. In young adults, this disease can lead to lower IQ, affect memory, attention and behavior, cause hearing loss and cardiovascular problems [42]. The comparison between the scenarios for the mean values of the FFQ recall shows that the values for \"as is\" and \"1.0 MRL\" MOET are close to each other. However, the \"0.7 MRL\" scenario increases the MOET by over 40%, while the \"zero\" scenario doubles the MOET value. The results for 24 h recall show an even more pronounced increase in the MOET value (increase in MOET by 50% for \"0.7 MRL\" and almost a tripling of the value for \"zero\"). Calculated MOET Values for scenario \"Max\" are as follows: FFQ recall (mean--117.9; 95 th percentile--27.2; 99 th percentile--13.6); 24 h recall (mean--194.8; 95 th percentile--40.2; 99 th percentile--9.1). It is considered that the MOET value below 100 at the 99.9 th percentile of the exposure distribution is a threshold regulatory consideration [17]. For children and toddlers, the threshold was between 100 and 199, and for adults, it was between 264 and 314. In our case, for the 'Max' scenario, values were below 100. In Figure 2, a Pareto distribution illustrates the primary sources contributing to exposure. In all scenarios with active substances (\"as is\", \"1.0 MRL\" and \"0.7 MRL\") and using both recall methods, cyprodinil is the predominant contributor, accounting for 41.3-63.5% of the total depending on the recall methods and scenario. Cyprodinil is mainly associated with green salads and strawberries. Fluopyram follows with a share of 8.8%-19.6%, mainly from blueberries and strawberries. In the \"zero\" scenario, fluopyram and fluxapyroxad are the main active substances of concern.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL
### End-to-End Delay Optimization
#### 3.3.2 Waiting Time for Multi-Path Transmission Nodes  
The delay of end-to-end transmission is mainly based on the waiting time of a data packet in the bottleneck nodes. As Figure 5 shows, the bottleneck nodes' traffic is the ratio of traffic that node A sent to its three parent nodes individually, multiplied by the ratio of traffic that is transmitted to the bottleneck, and then added. Hence, the number of data received by the bottleneck node in the multi-path case is:  
\[S_{r}(a,c)=\alpha_{a,c}\cdot Tat(a,c) \tag{15}\]  
Thus, its waiting time \(D\) is:  
\[D=T_{a,c}=\frac{L}{S_{r}(a,c)(1-p)}-\frac{1}{D_{p}T_{p}N_{task}} \tag{16}\]
## 1 Introduction
As shown in Figure 1, climate data, namely, temperature and rainfall, are automatically collected from IoT-based sensors (other climate data, namely, storm activity and fog and hail occurrence, are gathered from datasets made available by local weather stations), whereas crop yield data, namely, hectares planted, hectares harvested and actual crop production volumes, are provided by the peasant farmer though a mobile application. Furthermore, notice that the interactions among the components that represent the core of  
Figure 1: Research Idea Overview. the system architecture: IoT message hub, data storage service, IoT data analytics service and ML models depict the main steps of the proposed data-mining method. Unlike most recent related works, our work proposes a data-mining process for the prediction of the volume of crop production that integrates two heterogeneous sources of crop production data and climate data as well as the architectural design of a smart farming system specially designed for peasant farming. The remainder of this paper is organized as follows: in the first section, some recent proposals that are relevant to our work are described and compared; in the second section, our proposal is presented in detail; in the third section, the results of an initial validation of our proposal are presented and discussed; finally, in the last section, the concluding remarks of our work are summarized and the future lines of research are outlined.
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 3 Description of Our Method  
This section provides a detailed explanation of the design of the method proposed in this paper. Firstly, the vibration signal is preprocessed to eliminate noise and normalize it. Then, different modules in the feature extraction stage are described in detail. Finally, the pedestrian speed vector is regressed to obtain position information. The entire process is illustrated in Figure 3.
## 3 Experiments  
### Dataset  
In this paper, the vibration response signals were collected through an eight-layer steel frame structure; each layer was 35 cm long and 25 cm wide, with a height of 20 cm between the two layers. Anchor bolts were used to fasten the bottom of the frame to the ground, while double-row bolts were used to join the beam to the columns. The diagram of the frame is shown in Figure 3. White noise excitation was applied at the third layer of the frame, and eight acceleration sensors were installed at each level along the direction of external excitation to record the acceleration response. The white noise generator model was RIGOL DG-1022, an electromagnetic exciter was used as the actuator, and the type of white noise was pre-defined (Ex1-Ex10). The model structure's steel material had an elasticity modulus of \(E=2.0\times 10^{11}\) Pa and a density of \(\rho=7.8\times 10^{3}\) kg/m\({}^{3}\). Each column member was made of a \(200\times 30\times 3\) mm steel plate in a undamaged condition. The damage to the steel frame structure was achieved by reducing the stiffness of the steel plates (i.e., replacing the current plate with thinner ones: \(200\times 30\times 2.5\) mm). In the vibration experiments, the duration of each recording was 32 s, the sampling frequency was 128 Hz, and a record contained 4096 data points for one sensor. Ten damage states were set up for the experiments, and the damage locations and severity for the ten damage cases are shown in Table 1.  
Figure 3: Eight-layer steel frame diagram.  
Figure 2: The feature vectors obtained by wavelet packet decomposition under the different damage cases. The 4 damage case are represented by D1–D4.  
In Table 1, UD is the undamaged state, D1-D6 are the cases of single-layer damage, and D7-D9 are the cases of two-layer damage. Ten different white noise excitations (Ex1-Ex10) were applied for each damage case. For each noise effect, one record was gathered. A total of 100 data were collected under 10 kinds of damage and 10 kinds of noise. Each record contains the vibration response of eight sensors, with a sampling time of 32 s and a sampling frequency of 128 Hz, for a total of 32,768 data points. Figure 4 shows the structural vibration response for the UD and D1 cases.
# A Comparative Performance Analysis of Four Wind Turbines with Counter-Rotating Electric Generators
## 3 Closed-Form Modeling  
The kinematic and static models of the _WSCGs_ illustrated in Figure 1 can be derived based on the block diagrams from Figure 2. The following correlations [55] can be written by decomposing the complex transmissions into gear sets I and II, according to the block diagrams in Figure 2:  
* Planetary gear set I: \[\begin{cases}\begin{array}{l}i_{32}^{H_{1}}=i_{01}=\frac{\omega_{3H_{1}}}{ \omega_{2H_{1}}}=-\frac{z_{1^{\prime}}}{z_{3}}\frac{z_{2}}{z_{1^{\prime\prime} }}\\ \omega_{3}=\omega_{2}i_{01}-\omega_{H_{1}}(1-i_{01})\\ T_{3}i_{01}\pi_{01}^{w}+T_{2^{\prime}}=0\\ T_{3}+T_{2^{\prime}}+T_{H_{1}}=0\end{array}&\Rightarrow&i_{32}=i_{01}+\frac{ \omega_{H_{1}}}{\omega_{2}}(1-i_{01})\\ \end{array}\] (1) where \(x=\pm 1\), depending on the direction of the power flow transmission through the fixed axis mechanisms associated with the planetary gear set, obtained by reversing the motion with respect to \(H_{1}\)[55]. For Case A (Figure 1a) and Case B (Figure 1b), the sign of \(x\) depends on the ratio \(k_{t}=-T_{R3}/T_{R1}\): for \(k_{t}>1\), \(x=+1\), and for \(k_{t}<1\), \(x=-1\). For Cases C (Figure 1c) and D (Figure 1d), \(x=-1\).
* Planetary gear set II: \[\begin{cases}\begin{array}{l}i_{64}^{H_{2}}=i_{02}=\frac{\omega_{6H_{2}}}{ \omega_{4H_{2}}}=-\frac{z_{4}}{z_{6}}\\ \omega_{6}=\omega_{4}i_{02}+\omega_{H2}(1-i_{02})\\ T_{6}i_{02}\pi_{02}^{w}+T_{4}=0\\ T_{6}+T_{4}+T_{H_{2}}=0\end{array}&\Rightarrow&i_{64}=i_{02}+\frac{\omega_{H _{2}}}{\omega_{4}}(1-i_{02})\\ \end{cases}\] (2) where the sign of the exponent \(w\) is obtained as follows [55]: \[w=\text{sgn}\big{(}\omega_{6H_{2}}T_{6}\big{)}=\text{sgn}\bigg{(}\frac{i_{02} }{1-i_{02}}\bigg{)}=-1\] (3) where sgn is the signum function.  
The kinematic and static modeling in the general case (Case A) is further presented considering Equations (1) and (2), to which the kinematic and static correlations related to the connections between the planetary gear sets, the wind rotors and the electric generator are added. The relations for the other three functional cases (B, C and D) are obtained by customizing the analytical archetype model obtained in Case A.  
Figure 2: Block diagrams of the transmissions from Figure 1: (**a**) Case A (Figure 1a); (**b**) Case B (Figure 1b); (**c**) Case C (Figure 1c); (**d**) Case D (Figure 1d).
# Zero-Shot Recommendation AI Models for Efficient Job-Candidate Matching in Recruitment Process
## 2 Materials and Methods
### Comparison of Dot Product and Cosine Similarity
#### 2.15.2 Disadvantages  
Dot Product
* Magnitude Dependence: The sensitivity to the magnitude of vectors can be a drawback when the size difference between vectors (such as document length) should not influence the similarity measure.
* Lack of Normalization: Without normalization, comparisons across different sets or scales may be less meaningful.
* Ignorance of Magnitude: While often an advantage, the disregard for vector magnitude means that cosine similarity cannot distinguish between vectors that are similar in direction but different in scale.
* Computational Complexity: The need to calculate vector norms for normalization can introduce additional computational overhead, especially for large datasets.  
In summary, the choice between dot product and cosine similarity should be guided by the specific requirements of the application. The dot product may be preferable when the magnitude of the vectors carries important information, while cosine similarity is more suitable for cases where only the orientation or the pattern of the data matters, such as in text similarity measures where document length should not affect the similarity score.
## 2 Results
Sample #A in vacuum shows a decay dynamic qualitatively similar to the one of Sample #F, but with decreased lifetimes down to \(\tau_{1}\approx 1.2\) ns, \(\tau_{2}\approx 7.5\) ns, and \(\tau_{3}\approx 65\) ns, and a clearly stronger relative contribution of the FE trapping (relative amplitude 85.7%) and a reduced relative amplitude of FE relaxation (12.9%) and BE relaxation (1.4%). The relaxation dynamics variation of Sample #A when the atmosphere is changed from V to WA is very different from the one of Sample #F (see Figures 4b and 5b), in particular:  
1. The peak intensity increases of about 2 times (see inset of Figure 4b). 2. A clear increase of the two faster decay times is observed (\(\tau_{1}\) from 1.2 to about 2.0 ns and \(\tau_{2}\) from about 7.5 to about 9.8 ns), while the slowest time \(\tau_{3}\) remains basically constant at about 65 ns. 3. All the pre-exponential amplitudes clearly increase of about 1.9, 4.3, and 3.9 times for A\({}_{1}\), A\({}_{2}\), and A\({}_{3}\), respectively. Figure 4: TR-PL traces in V (red line) and WA (black line) of: Sample #F (**a**); and Sample #A (**b**). The green and blue lines are the corresponding best fit curves with a three exponential decay. Insets: Magnification of the peak signal, evidencing that the peak value is basically not affected by the V to WA transition in Sample #F while it clearly increases in Sample #A.  
Figure 5: Pre-exponential amplitudes and corresponding decay times during irradiation in V and WA for: Sample #F (**a**); and Sample #A (**b**).
## 4 Experiments
### Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Input**} & \multicolumn{3}{c}{**Cora**} & \multicolumn{3}{c}{**Citeseer**} & \multicolumn{3}{c}{**Pubmed**} & \multicolumn{3}{c}{**Wiki**} \\ \cline{3-14}  & & **Acc\%** & **NMI\%** & **F1\%** & **Acc\%** & **NMI\%** & **F1\%** & **Acc\%** & **NMI\%** & **F1\%** & **Acc\%** & **NMI\%** & **F1\%** \\ \hline \(k\)-means & Feature & 34.65 & 16.73 & 25.42 & 38.49 & 17.02 & 30.47 & 57.32 & 29.12 & 57.35 & 33.37 & 30.20 & 24.51 \\ \hline Spectral-f & Feature & 36.26 & 15.09 & 25.64 & 46.23 & 21.19 & 33.70 & 59.91 & **32.55** & 58.61 & 41.28 & 43.99 & 25.20 \\ \hline Spectral-g & Graph & 34.19 & 19.49 & 30.17 & 25.91 & 11.84 & 29.48 & 39.74 & 3.46 & 51.97 & 23.58 & 19.28 & 17.21 \\ \hline Deepwalk & Graph & 46.74 & 31.75 & 38.06 & 36.15 & 9.66 & 26.70 & 61.86 & 16.71 & 47.06 & 38.46 & 32.38 & 25.38 \\ \hline DNGR & Graph & 49.24 & 37.29 & 37.29 & 32.59 & 18.02 & 44.19 & 45.35 & 15.38 & 17.90 & 37.58 & 35.85 & 25.38 \\ GAE & Both & 53.25 & 40.69 & 41.97 & 41.26 & 18.34 & 21.93 & 64.08 & 22.97 & 49.26 & 17.33 & 11.93 & 15.35 \\ VGAE & Both & 55.95 & 38.45 & 41.50 & 44.38 & 22.71 & 31.88 & 65.48 & 25.09 & 50.95 & 28.67 & 30.28 & 20.49 \\ MGAE & Both & 63.43 & 45.57 & 38.01 & 63.56 & 39.75 & 39.49 & 43.88 & 8.16 & 41.98 & 50.14 & 47.97 & 39.20 \\ ARGE & Both & 64.00 & 44.90 & 61.90 & 57.30 & 35.00 & 54.60 & 59.12 & 23.17 & 58.41 & 41.40 & 39.50 & 38.27 \\ ARVGE & Both & 63.80 & 45.00 & 62.70 & 54.40 & 26.10 & 52.90 & 58.22 & 20.62 & 23.04 & 41.55 & 40.01 & 37.80 \\ AGC & Both & 68.92 & 53.68 & 65.61 & 67.00 & 41.13 & 62.48 & 69.78 & 31.59 & 68.72 & 47.65 & 45.28 & 40.36 \\ \hlineAGCHK performs very stable on benchmark datasets, and the standard deviations of Acc, NMI, and F1 are 0.18%, 0.35%, and 0.24% on Cora, 0.00%, 0.00%, and 0.00% on Citeseer, 0.01%, 0.01%, and 0.01% on Pubmed, and 0.05%, 0.03%, and 0.02% on Wiki, which are more stable than AGC [7]. We compare the time efficiency of several baseline methods as Table 3, and the best score is in bold, while the second best score is underlined. We can see that the running time of AGCHK and AGC are comparable, while AGCHK is more than three times faster than the other methods. Since AGCHK does not need to train parameters, it is more efficient than the baselines based on graph neural networks.
## 3 Design of Functional Modules
### GSM/SMS Communication  
The GSM/SMS communication model also has send and receive modules, which are illustrated in Figures 6 and 7.  
Figure 5: (**a**) Send process; (**b**) receive process in Ethernet communication.  
Figure 6: The ’send’ process in GSM/SMS communication.  
Figure 7: The ’receive’ process in GSM/SMS communication.  
* The send process in GSM/SMS communication: Step 1: Set the variable. Step 2: Temporarily save the data (Send Buffer) to OutData. Step 3: Transmit the phone number of the recipient. Once data has been received in the serial port, determine whether CMGSByte.IndexOF (\">\") Steps 4-5: \(\geq 0\). If yes, this means data have been sent successfully by the GSM/SMS module, and the workflow moves to Step 6. If not, steps 3-5 are repeated. Step 6: Transmit Send Buffer data to the phone number of the recipient.
* The'receive' process in GSM/SMS communication Step 1: Set the variable. Step 2: Calculate the length of data from the serial port and save to DataLen. Step 3: Calculate the size of DataLen. If DataLen > 0, this means that there are data in the serial port, and the workflow moves to Step 4. If DataLen \(\leq 0\), the workflow ends. Step 4: Save data from the serial port to InData. Step 5: Determine whether InData.IndexOF (\"+CMTI:\") \(\geq 0\). If yes, this indicates that the SMS notification has been received. If not, the workflow is terminated. Step 6: Get the message position (messageNO) from the SIM card. Step 7: Read the SMS data from messageNO. Step 8: Save the serial data to messageRead. Step 9: Transmit the series to the Data Transfer Module.