esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 LEO Satellite Network Model
### LEO Satellite Network Resources  
In the LEO satellite network, each node is a communication satellite that can provide network services such as data forwarding, routing calculation, and data caching based on the services, with the corresponding onboard network resources being bandwidth, computing, and storage resources. There are variations in the scale of onboard resources among different satellites considering the differences in their payload capabilities. Therefore, to better characterize the three types of onboard network resources, the maximum available resources for each satellite node are set at the initial state, specifically \(\mathrm{R}_{i}^{\mathrm{max}}=[\mathrm{B}_{i}^{\mathrm{max}},\mathrm{C}_{i}^ {\mathrm{max}},\mathrm{S}_{i}^{\mathrm{max}}]\). Then, a corresponding resource status matrix is generated for each satellite, reflecting the remaining status of onboard resources. Specifically, the remaining bandwidth, computing, and storage resources of the \(i\)-th satellite at time \(\mathrm{t}_{j}\) can be represented as \(\mathrm{R}_{i}\big{(}\mathrm{t}_{j}\big{)}=[\mathrm{B}_{i}\big{(}\mathrm{t}_ {j}\big{)},\mathrm{C}_{i}\big{(}\mathrm{t}_{j}\big{)},\mathrm{S}_{i}\big{(} \mathrm{t}_{j}\big{)}]\). Therefore, there exists a network resource status matrix at time \(\mathrm{t}_{j}\) for all nodes in the entire network:  
\[\mathrm{R}\big{(}\mathrm{t}_{j}\big{)}=\begin{bmatrix}\mathrm{B}_{1}\big{(} \mathrm{t}_{j}\big{)}&\mathrm{B}_{2}\big{(}\mathrm{t}_{j}\big{)}&\ldots& \mathrm{B}_{N}\big{(}\mathrm{t}_{j}\big{)}\\ \mathrm{C}_{1}\big{(}\mathrm{t}_{j}\big{)}&\mathrm{C}_{2}\big{(}\mathrm{t}_{j} \big{)}&\ldots&\mathrm{C}_{N}\big{(}\mathrm{t}_{j}\big{)}\\ \mathrm{S}_{1}\big{(}\mathrm{t}_{j}\big{)}&\mathrm{S}_{2}\big{(}\mathrm{t}_{j} \big{)}&\ldots&\mathrm{S}_{N}\big{(}\mathrm{t}_{j}\big{)}\end{bmatrix}_{3 \times N}, \tag{3}\]
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 2 Background Knowledge  
### Digital Signature Algorithm  
Digital signatures are effective in providing the authentication, integrity, and non-repudiation of messages. The digital signature algorithm (DSA) was adopted as a digital signature standard (DSS) by NIST from 1994. The DSA algorithms include three main stages. The first one is the key-generation stage, where the sender generates a public key from a random number. The generated key is then sent with the message and used to verify the signature. The second one is the signature-generation stage, where the sender generates the signature for the message based on a secret key and the hash of the message. The message is then digitally signed with this signature. The last one is the verification stage. In this stage, the receiver verifies the validity of the message by using the public key and the signature. The message is considered valid if the recovered signature matches the received signature. Figure 1 illustrates the three stages of DSA.
###### Abstract  
This study investigated the biomedical waste collection, transportation, and treatment activities in the city of Kocaeli, Turkey. As an alternative to incineration technology, a steam autoclave was used to sterilize the biomedical waste. Information regarding the collection, transportation, treatment and associated greenhouse gas emissions (GHG) were also investigated. Prior to sterilization, biological indicator vials containing _Bacillus stearothermophilus_ were placed in the center of the load to ensure that the pathogens were destroyed. GHG emissions were calculated based on the fuel consumed by the biomedical waste collection vehicles and the electricity/natural gas used at the sterilization plant. Results of this work revealed that the total biomedical waste generated per year increased from 1362 tons in 2009 to 2375 tons in 2019. The amount of biomedical waste generated per hospital bed was determined as 1.19 kg.bed\({}^{-1}\).day\({}^{-1}\). Results show that for efficient sterilization of biomedical wastes, the steam treatment system process should be operated at a contact time of 45 min, a temperature of 150 \({}^{\circ}\)C, and at a steam pressure of 5 bar. Biological indicator tests showed that the number of living _Bacillus stearothermophilus_ decreased significantly, with removal rates greater than 6 log\({}_{10}\). Finally, it was determined that the biomedical waste management activities generated a total of GHG emissions of 5573 ton CO\({}_{2}\) equivalency (tCO\({}_{2}\)-e) from 2009 to 2019. Furthermore, the average global warming factor (GWF) was calculated to be 0.269 tCO\({}_{2}\)-e per ton of biomedical waste generated. This study showed that the sterilization process is very effective in destroying the pathogens and the management of biomedical waste generates considerable amounts of GHG emissions.  
infectious waste; sterilization; biomedical waste; greenhouse gas; _Bacillus stearothermophilus_
# Gauge-Invariant Formulation of Time-Dependent Configuration Interaction Singles Method
## Theory
### Review of Channel Orbital-Based TDCIS with Fixed Orbitals  
As mentioned in Section 1, an interesting reformulation of the above-described TDCIS method has been proposed in Ref. [2], which introduces the time-dependent channel orbitals \(|\chi_{i}\rangle\) that collects all the single excitations originating from an occupied orbital \(|\phi_{i}\rangle\),  
\[|\chi_{i}\rangle=\sum_{a}|\phi_{a}\rangle C_{ia}(t), \tag{27}\]  
and rewrites the EOMs in terms of \(C_{0}\) and \(\{|\chi_{i}\rangle\}\) as  
\[i\partial_{t}C_{0} = \sqrt{2}\epsilon\cdot\sum_{j}\langle\phi_{j}|\mathbf{r}|\chi_{j}\rangle, \tag{28a}\] \[i\partial_{t}|\chi_{i}\rangle = \hat{P}\{(\hat{F}_{i}+\mathbf{E}\cdot\hat{\mathbf{r}})|\chi_{i}\rangle+ \sqrt{2}\epsilon\cdot\hat{\mathbf{r}}|\phi_{i}\rangle C_{0}\}-\sum_{j}|\chi_{j} \rangle\langle\phi_{j}|\mathbf{E}\cdot\hat{\mathbf{r}}|\phi_{i}\rangle, \tag{28b}\]  
where \(\hat{P}=\hat{1}-\sum_{j}|\phi_{j}\rangle\langle\phi_{j}|\). According to these EOMs and the initial conditions [\(C_{0}(t\rightarrow-\infty)=1\), and \(\{C_{ia}(t\rightarrow-\infty)=0\}\Longleftrightarrow\{\chi_{i}(t\rightarrow- \infty)\equiv 0\}\)], the channel orbitals \(|\chi_{i}\rangle\) get gradually populated along with the laser-electron interaction, measuring an excitation of an electron out of \(|\phi_{i}\rangle\). See Ref. [2] for interesting properties of the channel orbitals.  
It is also possible to formulate the channel orbital-based scheme based on the velocity gauge TDCIS using fixed orbitals, though not previously considered. We, therefore, introduce the analogous quantity  
\[|\eta_{i}\rangle=\sum_{a}|\phi_{a}\rangle D_{ia}(t), \tag{29}\]  
and rewrite Equation (26) as  
\[i\partial_{t}D_{0} = \sqrt{2}\mathbf{A}\cdot\sum_{j}\langle\phi_{j}|\hat{\mathbf{p}}|\eta_{j}\rangle, \tag{30a}\] \[i\partial_{t}|\eta_{i}\rangle = \hat{P}\{(\hat{F}_{i}+\mathbf{A}\cdot\hat{\mathbf{p}})|\eta_{i}\rangle+ \sqrt{2}\mathbf{A}\cdot\hat{\mathbf{p}}|\phi_{i}\rangle D_{0}\}-\sum_{j}|\eta_{j} \rangle\langle\phi_{j}|\mathbf{A}\cdot\hat{\mathbf{p}}|\phi_{i}\rangle. \tag{30b}\]  
Hereafter, we refer to the method based on Equation (28), i.e., the channel orbital-based TDCIS in the length gauge with fixed orbitals, simply as LG method, and that based on Equation (30), i.e., the channel orbital-based TDCIS in the velocity gauge with fixed orbitals, as VG method, for notational brevity.
## 3 Architecture of the Proposed Solution
### _Steal Time Monitoring Code_  
Steal time monitoring data can be collected using bash scripts. Here, we discuss the code used in our monitoring solution. The code listed below is divided into subparts, and each metric is discussed separately. The following are the code strategies we used in the data collection.  
First, we have to figure out the process ID of the VM for which we are calculating the steal time information. Listing 1 shows how the PID is collected from the procfs folder.  
**Listing 1**: PID.  
\[\text{PID}=\$(\text{pgrep qemu}\mid\text{head}-1\mid\text{tail}-1)\]  
When this line of code is executed on the VM, we obtain the 'PID' of this VM and then have to collect the CPU steal time for the VM. Listing 2 shows the code to obtain the CPU steal time information of the VM. This information is stored in the variable called'steal time'.  
**Listing 2**: CPU Steal time.  
\[\text{stealtime}=\$(\text{top -b -n 2\mid\text{head -n 3\mid\text{tail -n 1\mid\text{awk '{print \$16}$'}}}})\]  
Now, we collect the CPU steal time of each VM. In the last part of our code, we have to set some threshold values for the monitoring data display results.  
We developed some cases in which the first case shows the result 'OK' if the CPU steal time is between 0 and 2. A 'WARNING' is displayed if it is 3 to 7; from 7 onwards, it will be in a 'CRITICAL' state. While conducting the experiments, there are some factors related to the display of these results; when the steal time percentage is between 0 and 2, it seems that everything runs smoothly without any delay; with a percentage increase between 3 and 7, the execution seems a bit delayed; so, it generates a WARNING display. Above seven percent, there is a high jitter in the execution; so, it is a CRITICAL situation. Listing 3 shows the CPU steal time case output, and a steal time status line is displayed.  
Our proposed solution collects the monitoring data on the VM using the procfs, kernel pseudo-file system. All the virtual machines run on the compute node and work as the process of the cloud compute node. The information about the steal time of the VMs is mapped with the PID because each VM runs as a process in a cloud compute node. The information about the steal time is stored in the /proc folder. Our monitoring solution explores the /proc folders and fetches the steal time information. And to the best of our knowledge, the monitoring of the steal time of the VMs from the /**proc** folder has not yet been conducted.
# Cybersecurity Comparison of Brain-Based Automotive Electrical and Electronic Architectures
## 7 Recommended Security Controls for the Architectures
### Security Controls for the Ethernet  
* **MACSec:** MACsec is commonly used in IT network, however, it is one of the new security controls for in-vehicle communication. One of the characteristics of the MACsec is that it protects data communication in the lower level (Ethernet) of the protocol stack. However, MACsec alone cannot ensure end-to-end security. Several reasons against deploying MACsec for the in-vehicle communication are higher cost, performance degradation, and very few providers offer MACsec features in their SoC (System on Chip).
* **IPSec:** It is an approach to secure data communication which uses IP protocols and is able to offer end-to-end (IP-to-IP) security. IPSec was originally designed for IPv6, but it is also used for IPv4. Some relevant references of IPSec are RFC3686, RFC4302, RFC4303, and RFC5996. IPSec is able to ensure authenticity, confidentiality, integrity of the messages between two ECUs having unique IP addresses. Virtual Private Network (VPN) uses IPSec protocols.
* **Transport Layer Security (TLS):** It is the successor of the secure socket layer (SSL) and this protocol is supported by the AUTOSAR architecture. TLS supports application-to-application (Port-to-Port) security. TLS is able to ensure authenticity, confidentiality, integrity of the messages between two applications running on two ECUs having unique port numbers. It is recommended to use the latest version of TLS protocol which is version 3 in 2022.
* **Virtual Local Area Network (VLAN):** It allows to create multiple logical networks in the in-vehicle architecture to separate one network from another, thereby, the service provider such as OEM and Tier1/Tier2 supplier is able to implement individual security policies in each of those networks.
## 7 Conclusions and Future Works  
In summary, the paper proposed two algorithms, LAC-KHM and CLAC-KHM, for solving the CPP in an SDN. LAC-KHM improved the LAC algorithm by incorporating the KHM method to avoid the sensitivity to the initialization of the algorithm. CLAC-KHM customized LAC-KHM for solving the CPP by considering the three main metrics, which were the distance between the controllers, the distance between controllers and switches,  
Figure 4: Objective functions for all algorithms on Iris and Aarnet topologies.  
Figure 3: Objective functions for all algorithms on Intellifber.  
and load balancing. The proposed algorithms were evaluated on four different topologies, and the results showed that they outperformed k-means, spectral, and LAC algorithms. However, the proposed algorithms suffered from high computational complexity. This limitation arose due to the CLAC algorithm relying on the KHM algorithm, which entailed calculating the harmonic mean of distances between each data point and all centers for membership and weight functions. Consequently, this approach incurred a substantial computational burden. On the other hand, the CLA algorithm leveraged the k-means algorithm, which assigns data points to clusters based solely on their proximity to the assigned cluster, significantly reducing the computational complexity by avoiding calculations involving all clusters, which needs to be investigated in future works.  
Conceptualization, A.A., M.J. and M.R.M.; software, A.A.; validation, A.A. and M.J.; formal analysis, A.A.; investigation, A.A.; resources, A.A.; data curation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, A.A. and M.J.; visualization, A.A.; supervision, M.J.; project administration, M.J. and M.R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this study is available upon request. Researchers interested in accessing the data can contact [PERSON] ([EMAIL_ADDRESS]). We are committed to promoting transparency and facilitating the reproducibility of our research findings.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SDN & Software-defined network \\ CPP & Controller placement problem \\ LA & Learning automata \\ KHM & K-Harmonics mean \\ DBCP & Density-based controller placement \\ \end{tabular}
## 5 Discussion  
In this study, emotion recognition was performed based on the two-dimensional valence/arousal (VA) model, which divides emotions into four groups: high arousal-high valence (HAHV), high arousal-low valence (HALV), low arousal-low valence (LALV), and low arousal-high valence (LAHV). We used various features in different domains, such as time, frequency, time-frequency, and location. The features, classifiers, and evaluation metrics were selected according to their frequency of use and results found in the RSL. Sixteen input vectors were created for the classifiers, seven were collected for features in each domain (the original, and that selected via applying a correlation matrix), and nine were composed of combinations of features chosen from multi-domains.  
Then, the present study extensively evaluated feature sets across domains and their impact on emotion recognition. Feature selection improved results across the different domains. Additionally, hybrid models combining domain features offered a superior performance. An ANN using an input vector of nine time, seven frequency, two time-frequency, and sixteen location features yields the best results, with a maximum accuracy of 0.96.  
There are multiple proposed methods for EEG-based emotion classification. Still, they have limitations, including the number of emotional states considered. Generally, strategies dealing with more classes and participants tend to decrease their performance. According to [8], the average accuracy for emotion recognition using four quadrants in the two-dimensional valence/arousal space is 76.68%. Therefore, our results are better than those reported in the literature. An important part of our contribution is the anal ysis of the participation of multi-domain features in the classification performance for emotion recognition.  
Our study uses the DEAP dataset for EEG-based emotion recognition. It is important to recognize the role of self-labeling in the data acquisition process and its implications for emotion classification. Participants in the DEAP study used the Self-Assessment Manikin (SAM) to label their emotions. This method relies on visual mannequins to facilitate the self-assessment of emotional responses elicited by various video stimuli. This is an intuitive and accessible approach for participants to convey their emotional states. However, the self-labeling of emotions still introduces a degree of subjectivity into the dataset. This subjectivity arises from individual differences in emotional perception and expression. Individuals may interpret and respond to the same stimuli differently due to personal experiences, cultural backgrounds, and even momentary psychological states. As a result, self-labeled emotional states may not always align consistently with the physiological signals captured by the EEG. This aspect is recognized with the conviction of the potential of machine learning techniques to account for this variability in obtaining their recognition models.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 2 Background and Related Works
### Related Works  
There are three major types of ML algorithms, namely supervised learning, unsupervised learning, and semi-supervised learning, which can be widely applied in various networks including UAV networks to increase network security. These ML techniques are also used in intrusion detection systems. An IDS has several benefits, including attack detection, protection against violations, and recording existing threats to protect satellite networks. Moreover, it acts as high-quality control for safe format and administration and furnishes useful records about intrusions that occur. There are two main approaches to detect intrusions, and they are based on signature and statistical anomaly. The authors in [38] present an exhaustive survey on IDS based on CICIDS-2018 datasets. The CICIDS2018 is the most comprehensive Big Data, publicly available intrusion detection dataset that encompasses a broad range of types of attacks. These authors examined numerous research papers and compared their performance based on their ML models, computing environments and several performance parameter scores such as accuracy, precision, recall, area under curve, etc. The CSE-CIC-IDS-2018 datasets can be a convincing dataset to evaluate ML-based IDS in UAVs [39; 40].
## 4 Results and Discussion
### Technical Evaluation
Table 3 presents the median and interquartile ranges of the quality indices calculated from the signals displayed in Figure 8. The perfusion index is the gold standard for assessing PPG signal quality, and it is calculated as the ratio of the pulsatile blood flow to the non-pulsatile (DC) [25]. Perfusion indices below 0.4% show weak pulse strength, which is the case for the proximal signals, as it is expected due to the lack of extracerebral perfusion (phantom). The perfusion index was significantly different between detectors as well as it differs between wavelengths. Skewness measures how symmetric the signal's distribution is and kurtosis measures how sharp the peak of the signal's distribution is. A distribution with many outliers will have low values of skewness and kurtosis since its probability distribution will be asymmetric, flatter and will approach zero slower [27]. Hence, there are more erratic peaks on the distal signals than the proximal, with a statistically significant difference. However, the skew and kurtosis on the frequency domain were the same at all levels. Entropy provides a quantitative measure of the uncertainty present in a signal [25]. The lower values (E \(<\) 5) correlate to low noise which is the case for both proximal and distal signals despite the significant difference between the detectors and wavelengths. Another important index on pulsatile signals quality is the zero-crossing rate, which, on PPG signals, is calculated as the rate of sign-changes with the signal mean as the reference. On a window of 5 s (10,000 samples) and simulating a heart rate of 60 bpm, the results showed a zero-crossing rate of 0.0010, as was expected. The latter demonstrated the absence of erratic peaks crossing the mean signal. The signal-to-noise ratio (SNR) is a well-used measure that compares the level of the desired signal to the level of background noise [25]. The higher the ratio, the lower the noise. The resultant median ratios over 22 dB indicate the low proportion of the noise against the PPGs signals. Better ratios were found on the distal signals with a significant difference on at least one of the wavelengths compared to the proximal measurements. Finally, it has been found that most of the energy of the systolic and diastolic waves is concentrated within the 1-2.5 Hz frequency band. The ratio of the power spectral density  
Figure 8: Raw and filtered PPG signals from two photodiodes using the head phantom at a normal ICP of 15 mmHg.
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 5 Conclusions  
The accuracy of dose administration in SRS treatments, delivering precisely-targeted high-dose sessions, is of paramount importance to guarantee both the clinical outcome and the absence of severe toxicities. Although a comprehensive QA program is therefore mandatory, drawbacks and limitations still hinder the use of available commercial detectors.  
The IBA myQA(r) SRS detector analyzed in this study is a device characterized by real time readout, small sensitive volume of the solid-state sensors and pitch-less resolution in an easy-to-use piece of equipment. It can therefore be effectively used for constancy and daily radiosurgery QA tests, replacing point detectors and radiochromic films in a large variety of measurements.  
Its features allow us to effectively measure even very small fields and to assess steep dose gradient regions without losing high spatial frequency details. Provided angular dependence is taken into account, the device is therefore capable of verifying most radio-surgery treatments. In particular, the patient-specific QA Gamma tests can be performed for highly complex treatments such as trigeminal neuralgia and other functional targets (characterized by very high doses delivered to a small brain area of 4 to 5 mm in diameter), for which dosimetry verification is a great challenge.  
Once a confident and accurate measurement baseline is established, myQA(r) SRS features and ease-of-use can make the periodic QA task for robotic radiosurgery safer and lighter.  
Conceptualization, E.D.M. and F.P.; methodology, F.P., E.D.M. and D.A.; software, F.P. and D.A.; validation, E.D.M. and F.P.; data curation, F.P.; writing--original draft preparation, F.P., E.D.M. and D.A.; writing--review and editing, E.D.M. and F.P.; supervision, E.D.M. and L.F.; project administration, E.D.M. and L.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All patients gave written informed consent which included anonymized data usage for research purposes.  
Data in the study will be deposited in the repository of Fondazione IRCCS Istituto Neurologico \"Carlo Besta\" (Milan, Italy), which will be shared by request from any qualified investigator.  
This work was supported by the Italian Ministry of Health (RRC).  
[PERSON] declares that he received payment of the registration fee for the 15 th International Stereotactic Radiosurgery Society Congress by ELSE Solutions S.r.l., Italian vendor of IBA devices. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. Other authors have no conflict of interest to disclose.