esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We demonstrate a Contactless Vital Sign Monitoring (CVSM) system and road-test the system for in-cabin driver monitoring using a near-infrared indirect Time-of-Flight (ToF) camera. The CVSM measures both heart rate (HR) and respiration rate (RR) by leveraging the simultaneously measured grayscale and depth information from a ToF camera. For a camera-based driver monitoring system (DMS), key challenges from varying background illumination and motion-induced artifacts need to be addressed. In this study, active illumination and depth-based motion compensation are used to mitigate these two challenges. For HR measurements, active illumination allows the system to work under various lighting conditions, while our depth-based motion compensation has the advantage of directly measuring the motion of the driver without making prior assumptions about the motion artifacts. In addition, we can extract RR directly from the chest wall motion, circumventing the challenge of acquiring RR from the near-infrared photoplethysmography (PPG) signal of low signal quality. We investigate the system's performance in various scenarios, including monitoring both drivers and passengers while driving on highways and local roads. Our results show that our CVSM system is ambient light agnostic, and the success rates of HR measurements on the highway near 82% and 71.9% for the passenger and driver, respectively. At the same time, we show that the system can measure RR on users driving on a highway with a mean deviation of \(-1.4\) breaths per minute (BPM). With reliable HR and RR measurement in the vehicle, the CVSM system could one day be a key enabler to sudden sickness or drowsiness detection in DMS.  
Time-of-Flight; contactless physiological measurement; heart rate monitoring; motion artifacts compensation; driver monitoring system +
Footnote †: journal: Applied Sciences
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 2 Cybersecurity Goals
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Property** & **Examples** \\ \hline VIRUS [30] & Copies itself to other files; needs a host file to replicate and execute. & CIH, VIRUT, Redlof, peacomm. \\ \hline WORMS [31] & Exploits the vulnerabilities that are present and can self-propagate over the network. & Code Red, Netsky, Sasser, no\_virus. \\ \hline LOGIC BOMB [32] & Triggers a specific code on stipulated conditions as per the logic programmed by its author. & Michelangelo \\ \hline BACKDOOR [33] & It is an alternative entrance into a system. They are used to detour the existing security mechanisms built into the systems. & Xhaker, sub7, Beast, Ginwui, Rexob, Hupigon. \\ \hline TROJAN [34] & A fraudulent program that hoaxes a harmless or useful program, but stores some other malware. & Torpig, Gozi, Pidief, Limbo/NetHell. \\ \hline SPYWARE [35] & Software used to spy on victim’s activities and steal sensitive data. & WhenUSave, PuritySCAN, SecurityToolbar, Virtumonde. \\ \hline ROOTKIT [36] & Set of programs that alter the OS utilities to hide themselves. & LRK, AFX, SInAR, Rustock, Mebroot. \\ \hline BOT/BOTNET [37] & Program that does the work on behalf of its handler. The handler may control millions of such bots and can use them for malicious activities. & Agobot, Slackbot, Mybot, Rbot, SdBot, poebot, IRCBot. \\ \hline APT [38] & A covert attack on a system where the attacker remains undetected for a significant period and maintains unauthorized access. & GhostNet, Stuxnet, APT28, Sykipot APT. \\ \hline KEYLOGGERS [39] & Any hardware-software combination that records every keystroke a user makes. & Zedlog, Simple Perl Keylogger, Symple Python Keylogger, \\ \hline ADWARE [40] & Unwanted software designed to put up advertisements up on the user’s screen, most often within a web browser. & Appearch, DollarRevenue, Fireball, Gator, DeskAd. \\ \hline RATs [41] & Allows a hacker to take control of the computer and conduct activities such as exploring files, financial transactions, harvesting login credentials. & Slashtop, GoToMyPC, RemotePC, AnyDesk, Zoho Assist, Connectwise control. \\ \hline RANSOMWARE [42] & Software designed to restrict access to a computer until a certain amount of money is paid; otherwise, the data are either deleted or leaked on the dark net. & AIDS Trojan, WannaCry, CryptoLocker, Petya, Bad Rabbit, TeslaCrypt. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of different types of malware.
## 3 Experimental Results
### Performance Metrics for MF Class Using Imbalanced Dataset without Feature Normalization  
After the data preprocessing operation, the dataset is imbalanced since it comprises a total of 9973 rows which include 9643 non-failure and 330 machine-failure-labeled data. Firstly, the classification studies for the machine failure label were performed on the imbalanced dataset without any normalization. Performance results of the classification with the imbalanced dataset without normalization are given separately in Table 4. For all experiments, the classification performance scores from different types of SML algorithms (DTs, SVMs, and k-NNs) and DL techniques (1D-LeNet, 1D-AlexNet, and 1D-VGG16) are shown in the tables. The overall accuracy values are high in both the shallow and deep machine learning algorithms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Types** & **Dataset Type** & **\# of Failures** & **\# of Non-Failures** \\ \hline MF & Imbalanced & 330 & 9643 \\ MF & Balanced & 330 & 402 \\ HDF & Balanced & 115 & 120 \\ OSF & Balanced & 98 & 100 \\ PWF & Balanced & 95 & 100 \\ TWF & Balanced & 46 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The details of datasets used in our experiments. When the classes are imbalanced (e.g., the majority in our dataset are in the non-failure label), the classification model performance seems good if it is evaluated according to the accuracy score. In other words, as the bias in the class distributions are high in imbalanced datasets, accuracy metrics can become unreliable [46; 47]. Hence, it would be more useful to choose the F1-score (also known as F-measure), which is the most popular metric when evaluating the performance of classification in an imbalanced dataset. F1-score provides strong/reliable results for the imbalanced dataset and evaluates both the recall and precision ability of the model. According to the results that are shown in Table 4, the general F1-score values are below 54% in each shallow and deep learning classification algorithm. In discussing other metrics, DT and SVM models demonstrate higher recall and precision compared to the other models, with DTs achieving a recall of 18.79% and precision of 87.32%, and SVM achieving a recall of 20.91% and precision of 88.46%. On the other hand, the k-NN model shows significantly higher recall (50%) but lower precision (56.70%) compared to DTs and SVMs. The 1D-LeNet, 1D-AlexNet, and 1D-VGG16 models, which are deep learning architectures, display relatively lower recall and precision values compared to DTs and SVMs. Among these, 1D-AlexNet demonstrates the highest precision at 73.68%, while 1D-LeNet has the lowest recall at 6.67%. All models show high specificity values. This is primarily based on the imbalance in the dataset, where the normal class (non-failure) overbalances the failure class. Therefore, the models tend to accurately identify the normal class (not fault class), resulting in high specificity scores. Figure 6 shows the comparison of fault classification performance metrics for SML and DL models on an imbalanced dataset without normalization.
# Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
## 3 Experimental Results and Discussion
### Discussion
#### 3.4.1 Discussion  
It is necessary to have a discussion on the improvement of the fault diagnosis performance of the different optimization modules of the proposed model.  
Similarly, we recorded the average of the experimental accuracy with five repetitions as the experimental results, and the results of the ablation experiment are shown in Figure 15.  
Figure 15: The ablation experiment’s results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Names of the Models**} & \multicolumn{2}{c}{**Optimization Techniques 1**} \\ \cline{2-3}  & **Embedding CBAM** & **Using Meta ACON-C** \\ \hline Base & \(\times\) & \(\times\) \\ \hline Base + A & \(\surd\) & \(\times\) \\ \hline Base +B & \(\times\) & \(\surd\) \\ \hline Base + A + B (proposed model) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}  
1 Mark “\(\surd\)” means that the optimization techniques is applied. Mark “\(\times\)” means that the optimization techniques is not applied.  
\end{table}
Table 9: The details of differentiating ablation comparison models.  
From Figure 15, embedding the CBAM attention mechanism and using the Meta ACON-C activation function significantly improves the model performance, and the average accuracy of the model fault diagnosis improves by 4.03% compared to Base when the SNR of 2 dB. In the 4 dB result, Base + A improves by 1.72% and 1.22% compared to Base and Base + B, respectively. In the 6 dB result, Base + B seems to have no boost to Base, however, after embedding CBAM, Base + A + B to Base improves by 1.48%. The diagnostic results of all four models exceed 99% under the effect of slight noise of 8 dB and 10 dB.  
The effectiveness of the different optimization modules used in the proposed model is verified by ablation experiments, using either of these two optimization methods can improve Base's fault diagnosis performance in the presence of noise interference, and where the contribution of the embedded CBAM attention mechanism is greater.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 3 Methodology
### Fuel Burn  
It was assumed that the turboelectric system would have the K-60 TP turboprop running at full throttle during cruise (as shown later in the results section for power data). The turboprop is most fuel efficient near its maximum throttle [18]. An estimate of endurance can be calculated using the maximum fuel burn rate from the test bench [15]. Assuming 0.25 kg of reserve fuel and a single 2 min start time, the total flight endurance of the test vehicle was estimated to be around 16 min. This is a short flight time for this size vehicle, but it should be noted that aircraft weight could be reduced by removing the extra sensors and other similar components. The aircraft was designed explicitly to be a functional technology demonstrator, and a future vehicle designed for a particular mission would require a gas turbine engine with higher thermal efficiency than the inexpensive hobby-grade engine utilized in this study.
## 6 Methodology
### System Overview
#### 6.1.3 Device Provisioning  
The entire attestation process depends on the device being pre-provisioned with the necessary key material, secure application components, and firmware images, as illustrated in Figure 6. The secure provisioning phase involves setting up the secure boot chain by defining the flash layout, specifying the locations of the SM and application images, and determining the keys used to verify and decrypt these images. It also includes configuring boot options to enable TrustZone and activate SiRoT boot.  
The key material and application data are stored in the internal trusted storage (ITS), which is created using STM's ITSBuilder tool. The ITS file system is encrypted and can only be decrypted by the SM-Core.  
Application provisioning involves the ML training pipeline and model conversion to TFLite, where the compressed ECC secp256k1 point (H(w)G) of the model is stored in the internal ITS. The ML-EAT token is generated off the device, with only the fields that need to be computed at runtime being populated during the attestation request.  
The non-secure application is compiled and encrypted using the pre-provisioned keys on the device. Flashing is performed at a specific address, as defined by the flash layout. This ensures that only authorized, pre-configured software can execute on the device, thereby supporting a secure boot process and maintaining overall system integrity.  
The STuROT supports a dual-slot boot interface. During a firmware update, the new image is placed in the download slot. Upon the next power cycle, if the image is authenticated and its integrity is verified, it is copied to the active slot and executed. This mechanism enables over-the-air updates for the ML model, as illustrated in Figure 1.  
Figure 5: Software architecture of the proposed dual attestation mechanism.
## 2 Theoretical background  
During diffuse wave propagation in a non-homogeneous material, the energy \(I(S,R,t)\) of a diffuse wave in a defined frequency for a source \(I_{0}\) can be described by the diffusion equation [32]:  
\[\frac{\partial l(S,R,t)}{\partial t}-D\Delta I(S,R,t)+kl(S,R,t)=I_{0}, \tag{1}\]  
where \(D\) is the wave diffusivity, \(k\) is the dissipation parameter, \(t\) is the time, \(I(S,R,t)\) is the energy propagated from a source \(S\) to a receiver \(R\). Equation (1) describes the spatio-temporal variation of the diffuse wave field. A more accurate description can be obtained from the radiative transfer equation [33].  
Supposing the waveforms \(\varphi_{A}(S,R,t)\) and \(\varphi_{B}(S,R,t)\) can be obtained before and after a defect appearance during experiments, they are related with the energy through Equation (2) [24; 34]:  
\[\langle\varphi_{A}(S,R,t)\varphi_{B}(S,R,t)\rangle=I(S,R,t)-\frac{c\sigma}{2} \int_{0}^{t}I(S,x,u)I(x,R,t-u)du, \tag{2}\]  
in which \(c\) is the wave velocity, \(\sigma\) is the scattering cross-section, and \(x\) is the defect location. It can be normalized into Equation (3):  
\[\frac{\langle\varphi_{A}(S,R,t)\varphi_{B}(S,R,t)\rangle}{\sqrt{\langle\varphi _{A}2^{2}(S,R,t)\rangle\langle\varphi_{B}2^{2}(S,R,t)\rangle}}=CC(S,R,x,t)=1- DC(S,R,x,t)=1-\frac{c\sigma}{2}\frac{\int_{0}^{t}I(S,x,u)I(x,R,t-u)du}{I(S,R,t)}, \tag{3}\]  
in which \(CC(S,R,x,t)\) is the correlation coefficient and \(DC(S,R,x,t)\) is the decorrelation coefficient. They all depend on the time and the positions of the source, the receiver, and the defect location. \(DC(S,R,x,t)=0\) when no defect presents (\(\sigma=0\)) and \(DC(S,R,x,t)=1\) when the two waveforms are absolutely different (e.g., with large cracks). The decorrelation coefficient is related to the defect sensitivity kernel function \(K(S,R,x,t)\) using Equations (4) and (5) [35]:  
\[DC(S,R,x,t) = \frac{c\sigma}{2}K(S,R,x,t), \tag{4}\]  
in which  
\[K(S,R,x,t) = \frac{\int_{0}^{t}I(S,x,u)I(x,R,t-u)du}{I(S,R,t)} \tag{5}\]  
Equation (5) does not have an analytical form in general and should be calculated numerically [33].  
Once \(DC(S,R,x,t)\) and \(K(S,R,x,t)\) are obtained, the defect location can be predicted from different inversion algorithms, e.g., the linear least square inversion method [35] and the Monte Carlo Markov chain method [36]. Here a classical grid search method is used to find the most likely defect position using the cost function below:  
\[e(x)=\sum_{S,R}\frac{DC(S,R,x,t)^{2}}{\varepsilon^{2}}-\frac{\left(\sum_{S,R }DC(S,R,x,t)K(S,R,x,t)\right)^{2}}{\varepsilon^{2}\sum_{S,R}K(S,R,x,t)^{2}}, \tag{6}\]  
The probability density of the defect appearance at \(x\) is defined in Equation (7):  
\[p(x)=\frac{1}{C}exp(-\frac{e(x)}{2\varepsilon^{2}}), \tag{7}\]where \(\varepsilon\) is a fluctuation parameter for the measured decorrelations and \(C\) is a normalization constant.
## 6 M2M Application Protocols
### HTTP and CoAP
CoAP provides two types of application reliability to the delivery of publish messages: a Confirmable (CON) message, where the message is retransmitted if no delivery acknowledgement was received, using a simple stop-and-wait retransmission reliability with exponential back-off for congestion control; and a Non-Confirmable (NON) message, where there is  
Figure 3: Observer Design Pattern. no need to acknowledge the message. There is also a duplicate detection for both Confirmable and Non-Confirmable messages. There are two additional types of messages: Acknowledgement (ACK) or Reset (RST) messages. The ACK message is used to acknowledge CON messages, and the RST message either notifies the other endpoint that a CON message was received but some context is missing, or it is used to cancel subscriptions. Since UDP is used, the use of multicast IP destination addresses is supported, and can be useful in case of notifications. Security can be implemented by the Internet Protocol Security (IPsec) or Datagram Transport Layer Security (DTLS), although the last option shall be preferred. According to [54], DTLS will introduce at most 13 Bytes overhead per packet, not including initializations. Recent work identified this as a potential problem and there is a proposal for reducing the packet size overheads of DTLS by means of 6 LoWPAN header compression saving at least 14% [62]. According to [PERSON] _et al._[51], CoAP enables high scalability and efficiency through a more complex architecture which supports the use of caches and intermediaries (proxies), similarly to HTTP. The protocol supports the caching of responses in order to efficiently fulfil requests. This caching can be provided by a node in an endpoint or an intermediary. Another important mechanism in the protocol is the proxy functionality. Proxying is useful in constrained networks to improve performance or network traffic limiting, since, for example, proxies are not as limited, in bandwidth or battery, as other nodes. A gateway is considered to be a form of proxy or intermediary.
# Service Interface Translation. An Interoperability Approach
## 4 Performance Evaluation
### Use Case Scenario  
A simple use case scenario was selected to test and evaluate the proposed service interface translation. The scenario comprised a weather station that acted as a provider of various services, a service consumer, mandatory Arrowhead core systems [30], and the consumer interface generator system. The weather station included six sensors: indoor and outdoor temperature, humidity, pressure, solar radiation, and wind speed sensors. To retrieve data from each sensor, several services were provided.  
All systems were Arrowhead framework compliant. The systems ran within the frame of a unique local cloud. All interactions between systems were thus intracloud operations. Figure 4 describes the local cloud scenario.  
The scenario was designed to illustrate the interface generator system's behavior and performance. Therefore, interoperability mismatches were introduced between the service consumer and provider. Consumer and provider service contracts differ in various aspects. Interoperability mismatches prevent successful communication, making service consumption impossible without the use of the generator.  
The indoor temperature service was the service selected for the test and experiments. Table 2 describes the differences between the service consumer and provider for the indoor temperature service.  
Mismatches in the data model and payload are of great relevance, and semantic interoperability is a major barrier in the current technological paradigm [31]. Consequently, the consumer and provider payloads for the indoor temperature service differ in different aspects to show the potential of the proposed solution. As shown in Figures 5 and 6, mismatches introduced between the two payloads can be found in the structure of the message, the encoding, the semantics used in the key values, and the data types. The payload described in Figure 5 makes use of JSON and SenML. The temperature service message includes the name (n), value (v), unit (u), and time (t). Figure 6 payload, on the other hand, includes XML and a natural language to describe the name, localization, and value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic** & **Service Provider** & **Service Consumer** \\ \hline Protocol & CoAP & HTTP \\ \hline Encoding & JSON & XML or CBOR \\ \hline Semantic & SenML & Customized \\ \hline Temperature Data Type & Float & Integer \\ \hline \hline \end{tabular}
\end{table}
Table 2: Interface characteristics.  
Figure 4: A case scenario that included the Davis Vantage Pro2 weather station, the weather station provider and consumer, the mandatory Arrowhead core system, and the consumer interface generator.  
Figure 5: Example of the payload sent by the service provider.  
Figure 6: Example of the payload expected by the consumer.
## 6 Results and Discussion
### Data Confidentiality Violations Detection Results  
Figure 12 shows the number of disclosed agent identities and locations as compared to the total number of registered active accounts throughout the execution period. This time graph represents the agent data confidentiality violation detection results at the individual level. For each agent account, the agent anonymity could be either a real identity or anonymous. At the same time, the agent's presence visibility was either visible (location was disclosed) or invisible (location was protected). At the end of the 24-h execution, out of all the registered agent accounts, about 20% of the agent identities and 20% of agent locations had been disclosed to the public. According to the defined requirements, disclosing the agent identity, location, or both to the public violates agent data confidentiality. These information disclosures to the public were due to agent preferences that revealed the agent identity and made the agent's presence (location) visible to the public during the run-time.  
Next, Figure 13 displays the amount of disclosed social information. This time graph represents the result of the agent data confidentiality violations detected at the social level. For this second data confidentiality level, more than 70% of agents' social information were disclosed to the public via connection to online social networking during the run-time. Disclosing social information to the public is a data confidentiality violation since it risks the disclosure of personal information, such as a real name, address, and relationship to the public [53].  
Figure 12: Total number of active accounts and the number of detected disclosed identities and disclosed locations versus time (hours).  
Finally, Figure 14 presents the amount of disclosed contents. This time graph represents the result of agent data confidentiality violations detected at the content level. At this third data confidentiality level, about 80% of shared contents were disclosed to the public. These content disclosures occurred when agents shared public contents, such as traffic reports. These types of disclosures reveal an agent's account and risk other disclosures, such as their identity and location.
# Rib Design for Improving the Local Stiffness of Gearbox Housing for Agricultural Electric Vehicles
## 4 Parameter Study of the Rib Design
### Parameter Study 1: Width versus Area  
First, the local stiffness results were compared while increasing the width of the rib and decreasing the length and height at the same rate while fixing the mass of the rib (Table 6). The rib designed in the original housing had a width of 4 mm. Therefore, to check the effect of the rib width, the value was increased from 4 mm to 6 mm and 8 mm. According to the results, when the width was increased, the stiffness increase rate gradually decreased from 34.5% to 28.4%, and 24.6%, respectively. In other words, the rib design showed that it was more effective to increase the length or height than to increase the width with the same mass in terms of the stiffness increase rate  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rib Mass, g** & **W, mm** & **L, mm** & **H, mm** & **Freq., Hz** & **Stiff. line, N/mm** & **Stiff. Increase, \%** \\ \hline
15.2 & 4 & 20 & 20 & 1499.6 & \(3.79\times 10^{2}\) & 34.5 \\ \hline
15.2 & 6 & 16.3 & 16.3 & 1485.8 & \(3.62\times 10^{2}\) & 28.4 \\ \hline
15.2 & 8 & 14.1 & 14.1 & 1476.7 & \(3.51\times 10^{2}\) & 24.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of parameter study 1.  
Figure 11: Parameters of the rib design.  
Figure 10: Elastic strain of the 1 st mode.