esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Evaluating the Integrity of Isolated RNA from FFPE and FF Tumor Tissues  
In order to determine RNA integrity, we analyzed the RNA integrity number (RIN), which evaluates _18 SrRNA_ (1869 bp; e.g., NR_003286) and _28 SrRNA_ (5070 bp; e.g., NR_003287) regions compared to the total area under the curve, including the height of the _28 SrRNA_ peak [34]. Analyzing RNA from two patient matched FFPE and FF MIBC tissues (#126, #128) using all five isolation methods, with or without MB, RINs ranged between 2.2 and 3.5, without any significant improvement using MB (Table 1, Figure S2A-E). Due to the findings that archival tissues mostly result in inadequate RINs, we tested the same RNAs as above using a novel RNA integrity analysis called DV200 [35]. The DV200 determines the percentage of RNA fragments over 200 nucleotides as developed by Agilent ([[http://urx.red/OB4Y](http://urx.red/OB4Y)]([http://urx.red/OB4Y](http://urx.red/OB4Y))) (accessed on 8 October 2021) [35]. However, no significant differences were found comparing FFPE and FF using the different isolation methods in the presence or absence of MB (Table 1, Figure S2A-E).  
In order to further address the integrity of the different isolated RNAs, we implemented a new reference approach to detect RNA in the amplification range of approximately \(\sim\)100 bp for qPCR. Routinely, qPCR primers for reference and target genes are designed to amplify regions between 90 and 150 bp. Uniquely, our designed primers detected the constitutively expressed _5 SrRNA_ (121 bp; e.g., X51545.1) and _5.8 SrRNA_ (156 bp; e.g., NR_146120.1). Like _18 SrRNA_, _5.8 SrRNA_ is transcribed by RNA-Polymerase I, whereas _5 SrRNA_ is transcribed by RNA-Polymerase III [36]. In contrast to all other reference and target genes, the advantage of assessing _5S_ and _5.8 SrRNAs_ using our primer design shows that amplification for each gene can only occur, if these rRNAs are full length fragments. Therefore, amplification of both rRNAs could reflect a quality qPCR reference for smaller RNAs below 200 bp. GPCR results showed that the addition of MB significantly improved _5S_ and _5.8 SrRNA_ expression similar to _18 SrRNA_ (Figure 3A,B). In addition to implementing both rRNAs as reference genes, we also examined the ratios of _5 SrRNA_ to _5.8 SrRNA_ gene expression of FFPE and FF MIBC tissues for all RNA isolation methods and from five normal placenta tissues (Figure 3C). Interestingly, the FFPE _5 SrRNA_ to _5.8 SrRNA_ ratios (1.01 to 1.28) were generally higher compared to FF tissues (0.94 to 1.23) and human placenta (1.07). Especially for FF tissue isolation with Method C, MB improved the overall ratios (+MB: 0.98-1.13; -MB: 0.63-0.95).
## 2 System Model
### Sensing Result Classification
These values can be obtained by considering different parameters of the environment, including the distance between SU nodes, the distance of the SU transmitter from the PU nodes, etc. However, this consideration is outside the scope of current study. In this work, the fixed values of transmission power for different classes are used with respect to energy consumption packets in the simulations. The transmission power can be obtained as the ratio of the energy consumed to the transmission time, \(P_{T\bar{i}}=e_{i}^{con}/\tau_{r}\), where \(\tau_{r}\) is the transmission duration, obtained as \(\tau_{r}=T-\tau_{s}\) for \(T\) and \(\tau_{s}\) being the total duration of one time slot and the sensing time duration, respectively. The capacity of the SU link for the \(i\)th class is obtained as  
\[c_{i}=W\text{log}_{2}\left(1+\gamma_{si}\right), \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Residual Energy Level** & \(reg_{5}\) & \(reg_{4}\) & \(reg_{3}\) & \(reg_{2}\) & \(reg_{1}\) \\ \hline \(L_{1}\) & \(C_{0}\) & \(C_{0}\) & \(C_{0}\) & \(C_{0}\) & \(C_{1}\) \\ \(L_{2}\) & \(C_{0}\) & \(C_{0}\) & \(C_{0}\) & \(C_{1}\) & \(C_{2}\) \\where \(\gamma_{si}=P_{Ti}/N_{0}\) is the SNR of the SU channel, \(W\) is the bandwidth and \(N_{0}\) is noise power. The throughput obtained in the \(i\)th class classification can be given as  
\[R_{i}=\frac{T-\tau_{s}}{T}c_{i}. \tag{9}\]
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 3 Gibbs Sampling for MIMO Detection
### The Stalling Effect on GS Performance  
The performance of the GS method shall now be evaluated. To this purpose, set the maximum number of block iterations \(t_{\max}=500\) and the \"temperature\" parameter equal to the noise variance, that is \(\alpha^{2}=1\). For a symmetric system with \(n=20\) and a 4-QAM constellation, the corresponding BER plot is shown in Figure 1.  
For comparison purposes, performance of linear MMSE filtering and also the dual-element-based lattice-reduction shortest-longest-basis with sorted-variance successive-interference-cancellation with MMSE (D-ELR-SLB-SV-SIC-MMSE), proposed in [14], are also depicted. This latter detector remains as one of the best low-complexity detection techniques for large symmetric MIMO.  
As can be inferred for low SNR, the performance is very close to optimal. However, in the medium to high SNR, the BER did not improve any further. This problem is referred in the literature as the \"stalling problem\" [33]. This effect can be explained as follows: consider a case where for a candidate vector \(\mathbf{x}^{(t)}\), the norm \(||\mathbf{y}-\overline{\mathbf{H}}\mathbf{x}^{(t)}||\) is significantly smaller than \(||\mathbf{y}-\overline{\mathbf{H}}\mathbf{x}^{(t)}_{|\partial|}||\), where \(\mathbf{x}^{(t)}_{j|\partial|}\) is the same \(\mathbf{x}^{(t)}\) with \(j\)th position changed with \(\tilde{\omega}\). In this situation and, especially when SNR is high, one may find that after sampling \(P(\mathbf{x}^{(t+1)}=\mathbf{x}^{(t)}|\mathbf{y},\mathbf{H})\approx 1\) and \(P(\mathbf{x}^{(t+1)}=\mathbf{x}^{(t)}_{j|\partial|}|\mathbf{y},\mathbf{H})\approx 0\)[33]. Thus, the conventional Gibbs sampler may remain stuck in the same state for a very long time and that effect consequence is depicted in Figure 1, as the number of iterations was fixed to \(t_{\max}=500\). All in all and  
Figure 1: BER performance for conventional Gibbs sampling detection algorithm for \(M=N=10\) and a 4-QAM constellation. A simulated performance of the D-ELR-SLB-SV-SIC-MMSE [14] is also plotted for benchmarking.  
even though the chain is guaranteed to converge asymptotically when \(t\to\infty\)[31], stalling may occur. A possible solution to alleviate this problem is studied next.
# Q-Learning Approach Applied to Network Security
## 3 Markov Decision Process (MDP) and Q-Learning  
Reinforcement learning is a machine learning method that allows a particular decision entity, called an agent, to interact with the unknown environment, learn from it after receiving appropriate feedback (reward), and take actions that ultimately lead to optimal decisions. The goal agent observes and analyzes the situation and selects the best possible action based on an expected maximized return. The connection with the environment can be described as a Markov Decision Process (MDP) [14], which is based on the tuple \((S,A,P,R,\gamma)\), where \(S\) stands for the set of all possible states; A denotes a set of actions; \(P\) is a transitional probability going from one state to another; \(R\) is  
Setting the discount factor to a high value of around one implies that we prefer future rewards to immediate ones. The state-value function under policy \(\pi\) is  
\[V^{\pi}(s)=E_{\pi}[\sum_{k=0}^{\infty}\gamma^{k}R_{t+1+k}|S_{t}=s] \tag{2}\]  
It represents the cumulative discounted expected return for each episode \(k\) at a time \(t\), conditional on the states \(S_{t}=s\) that the agent needs to explore and the possible corresponding actions he has to take. The goal is to obtain the function's maximum across all policies. This could be achieved by employing the Bellman optimality equation for the state-value function \(V^{*}\) or the Bellman optimality equation for the action-value function \(Q^{*}\).  
\[Q^{*}(s,a)=\sum_{s^{\prime},r}P(s^{\prime},r|s,a)[R+\gamma*\max_{a^{\prime}}q( s^{\prime},a^{\prime})] \tag{3}\]  
The Bellman optimality equation for the state-value function \(V^{*}\) is  
\[V^{*}(s)=\max_{a}\sum_{s^{\prime},r}P(s^{\prime},r|s,a)[R+\gamma*V^{*}(s^{ \prime})] \tag{4}\]  
The Q-learning off-policy method [15] is the most popular RL off-policy procedure employed in the ID domain. It is found in the idea for value iteration, where the agent estimates the action-value function to update all states \(s\) and actions \(a\) for every iteration. The goal is to optimize the Bellman equation by taking higher rewards R actions. The equation below denotes the Q-value where \(\alpha\) is a learning rate, and a constant \(0<\alpha<1\) and \(\gamma\) is again a discount factor \(0<\gamma<1\).  
\[Q^{\prime}(S_{t},A_{t})\leftarrow(1-\alpha)Q^{\prime}(S_{t},A_{t})+\alpha[R_{ t}+\gamma\max(S^{\prime}_{t},A^{\prime}_{t})] \tag{5}\]  
The motivation for employing Q-learning as one of the top RL methods applied to ID is that it is model-free. The researchers can use the rewards as a controlling tool. Last but not least, as we mentioned before, it is an off-policy approach because it learns the model without following a particular policy [16].
# Modeling Intruder Reconnaissance Behavior through State Diagrams to Support Defensive Deception
## 4 Modeling Reconnaissance Activity
### Reconnaissance Activity Representation  
As mentioned in Section 3, the goal of an intruder during the reconnaissance phase of an attack is to identify the hosts of a network and to detect the open ports of each host. The proposed approach aims at modeling such activities of the intruder in order to enable the accurate detection of such events and to facilitate the optimal operation of deception mechanisms. Specifically, we used a multilayered approach that defines different state diagrams at different detail layers, as depicted in Figure 3. The activity represented by a state diagram in a layer can be further analyzed by a state diagram in a lower layer. Specifically, the state diagram of the \"Reconnaissance Type\" layer can be analyzed in two  
Figure 2: Sample State Diagram Level 2.  
state diagrams in the \"Host Discovery and Port Scanning\" layer. Furthermore, these state diagrams can be analyzed further in \"ICMP Host Discovery\", \"TCP Host Discovery\", \"UDP Host Discovery\" and \"TCP Port Scanning\", \"UDP Port Scanning\", respectively, and constitute the state diagrams of the \"Host Discovery and Port Scanning Options\" layer. Finally, in the \"Network Traffic\" layer, the \"ICMP Host Discovery\" is analyzed in \"ICMP Echo Request\", \"ICMP Information Request\", \"ICMP Timestamp Request\" and \"ICMP Address Mask request\", \"TCP Host Discovery\" is broken down into \"TCP SYN Ping\" and \"TCP ACK Ping\". \"UDP Host Discovery\" is analyzed in \"UDP Ping\". As regards \"TCP Port Scanning\", it is analyzed in \"TCP SYN Scan\", \"TCP Connect Scan\", \"TCP NULL Scan\", \"TCP FIN Scan\", \"TCP Xmas Tree Scan\", \"TCP ACK Scan\", and \"TCP Window Scan\". Regarding \"UDP Port Scanning\", it is analyzed in \"UDP Scan\".
# The New Paradigm of Deepfake Detection at the Text Level
## 4 Results
### CDFTM Tests Result  
The schema presented in Figure 11 outlines the logical flow of the training and testing process, including how data are prepared, how the model is trained with specified hyperparameters, and how predictions are generated and evaluated. The first phase begins with data preparation, by extracting the corresponding features--labels. The features are concatenated into a single vector and mapped with categorical labels to numeric keys for training.  
The training classifier employs a one-versus-all strategy to approach the decomposition of multiclass problems into multiple binary classification problems, one for each class. The binary classifier used in each subproblem is a FastTree model. This model is a gradient-boosted decision tree algorithm optimized for binary classification. After training, the model is deployed locally for the testing phase.  
In the testing phase, the CDFTM and the unseen dataset are loaded into the prediction process. Based on unseen features, the label is predicted, along with a confidence score. Next, the predicted value is compared with the real label extracted from the unseen dataset. The comparison with the true label generates the model's performance.  
After evaluating the unseen dataset, the confusion matrix and performance metrics are calculated. The confusion matrix is depicted in Figure 12. The tests conducted on the unseen dataset of 100 records led to 45 texts being correctly identified as human and 49 correctly identified as deepfakes. Six examples of texts were incorrectly identified. The accuracy of CDFTM is 98%, and the precision is 97.83%. The recall is 90%, as the system identifies human texts 90% of the time. The F\({}_{1}\)-Score shows that the model detects human texts with a score of 93.75%.  
The results show that the model is superior in identifying deepfake texts. The ADFTM uses this model output to establish whether the input text or document is associated with a specific person.  
Figure 11: Pipeline conversion from initial dataset into model evaluation.
## 5 Discussion
(2019) [9] reported that KNN was among the worst performing models in terms of AUROC score. Furthermore, it is worthwhile mentioning that this is the first study that reports the training times of the models within the context of LOS prediction using ML. As expected, and in line with the literature, we found that all models performed best when using features derived from HRV. In contrast, the models performed worse than those trained on PFI features. For a training interval around \(t_{i}=-48\), we found that ensemble models trained on SpO2 features also performed relatively well. However, when looking at the feature importance (i.e., Figure 6), we found that features derived from SpO2 were considered least important. Therefore, to overcome this ambiguity, future research is needed to discover the true effectiveness of the usage of features derived from SpO2 when predicting LOS. Interestingly, no papers have investigated the added value of features derived from SpO2 yet in the context of LOS prediction using ML. In addition, we found that while all separate vital parameter feature sets performed the worst around \(t_{i}=-6\), RF (respiratory frequency) performed best around this time interval. This suggests that just before a sepsis event, RF might be a promising parameter to implement when predicting the risk of LOS shortly before the episode. In line with this finding, we found that adding features from RF to the HRV feature set added the largest increase in AUROC score for most models as in Table 4 and Figure 14. In contrast, adding PFI to the dataset effectuated the smallest increase in AUROC. Lastly, contradictory to the findings of [PERSON] et al. (2020) [8], we found that all models performed best on the feature set without any dimension reduction techniques applied (i.e., using PCA analysis). One reason for this finding might be that PCA, although it does remove noise from the dataset, does not necessarily lead to the selection of the most informative features [33].
# Peri-Implantitis Causal Therapy with and Without Doxycycline: Retrospective Cohort Clinical Study
## 2 Materials and Methods
### Inclusion Criteria  
Patients were eligible for inclusion if they met the following criteria:  
* Age \(\geq\) 18 years;
* Presence of at least one osseointegrated implant diagnosed with peri-implantitis based on radiographic and clinical criteria, according to the 2018 Classification of Periodontal and Peri-Implant Diseases and Conditions (2018);
* Subjected to exclusively screw-retained full-ceramic implant-proshetic rehabilitation, regardless of implant design;
* Availability of complete clinical records, including baseline (T0) and follow-up (T1) evaluations;
* Patients with compensated systemic conditions (e.g., controlled diabetes or cardiovascular disease);
* Smokers were not excluded to reflect real-world clinical conditions and assess the impact of smoking on treatment outcomes.
* Exclusion criteria
* Patients were excluded if they met any of the following conditions:
* Lack of complete clinical documentation, including missing baseline or follow-up data;
* Previous history of surgical peri-implant therapy within 12 months prior to study inclusion;
* Use of local or systemic antibiotics for peri-implant treatment within the 3 months preceding non-surgical therapy;
* Presence of autoimmune diseases or uncompensated systemic conditions (e.g., uncontrolled diabetes, immunosuppression) that could interfere with tissue healing and disease progression after treatment;
* Inability to comply with the scheduled follow-up protocol, due to either non-compliance or logistical constraints;
* Participation in concurrent clinical trials evaluating peri-implant disease management during the study period.  
According to eligibility criteria, a total of 209 patients, of whom 97 were enrolled in CG and 112 in TG, were considered for the study.
## 3 Problem Formulation and Proposed Solution
### Problem Formulation  
For the RIS-aided parallel MIMO VLC system, the asymptotic capacity in high signal-to-noise ratio (SNR) with peak-constrained inputs is derived as [8]  
\[R(\mathcal{A},\mathbf{F})=\lim_{\mathcal{A}\rightarrow\infty}\sum_{n=1}^{N} \frac{1}{2}\log(\frac{h_{n}^{2}\mathcal{A}^{2}}{2\pi exp(1)}), \tag{15}\]  
where \(\mathcal{A}\) represents the peak constraint on the input signal due to the IM/DD modulated scheme, \(e\) is the natural logarithm, \(h_{n}\) is the \(n\)-th channel gain of the RIS-aided parallel MIMO VLC system (containing the LoS and NLoS channel gain), and \(\mathbf{F}\) is the assignment matrix and influences the value of \(h_{n}\). Consequently, the primary optimization problem is formulated as  
\[\mathbf{P}:\max_{\mathbf{F}} R(\mathcal{A},\mathbf{F}) \tag{16}\] \[s.t. f_{n,k}\in\{0,1\},\] \[\sum_{n=1}^{N}f_{n,k}\in\{0,1\},\] \[\sum_{k=1}^{K}f_{n,k}=1,\]Considering that the assignment matrix can only have an influence on the channel gain, we can transform the problem (16) into a more tractable form, which is characterized by  
\[\textbf{P1}:\max_{\textbf{F}} h_{1}h_{2}\cdots h_{N}\] \[s.t. f_{n,k}\in\{0,1\}, \tag{17}\] \[\sum_{n=1}^{N}f_{n,k}\leq 1,\] \[\sum_{k=1}^{K}f_{n,k}=1\]  
**Theorem 1**: _The equivalence problem **P1** is equivalent to the primary problem **P**._  
The optimization of RIS can influence the channel gain \(h_{n}\) in (15), and consequently, \(\mathcal{A}\) can be ignored in our process of optimization. Meanwhile, we can obtain the relationship that \(\lim\limits_{\mathcal{A}\rightarrow\infty}\sum_{n=1}^{N}\frac{1}{2}\log(\frac{ h_{n}^{2}\mathcal{A}^{2}}{2\pi e})=\sum_{n=1}^{N}\log h_{n}+N\log\mathcal{A}-N\log(2 \pi e)=\log h_{1}h_{2}\cdots h_{N}+N\log\mathcal{A}-N\log(2\pi e)\). Consequently, the objective is transformed into maximizing \(h_{1}h_{2}\cdots h_{N}\). The constraint in (13) is equivalent to the constraint \(\sum_{n=1}^{N}f_{n,k}\leq 1\) since the value of \(\sum_{n=1}^{N}f_{n,k}\) can only be 0 or 1 under the constraint of (12). This completes the proof.
## 3 High-Efficiency Wideband Envelope Amplifier  
One of the main fields of research when talking about polar transmitters or envelope elimination and restoration (EER) architectures is the EA, also sometimes known as a supply modulator. The main challenge presented by EAs is that they must provide enough bandwidth and a slew rate to meet the linearity needs of the envelope of the RF signal to be amplified (which can range from DC to tens of megahertz in some cases). Regarding this, the literature proposes a wide variety of design and implementation ideas [26; 27; 28; 29; 30], ranging from linear to switched to hybrid architectures. However, there are few commercial devices (operational amplifiers or DC-DC converters with a high dynamic response) that allow for the implementation of broadband EAs [31; 32; 33], and this portfolio is considerably reduced in regard to transmitters with high output power (>50 W) requirements. For this reason, although it was not the main goal of this work, it has been necessary to design and implement a wideband EA to verify the performance of the proposed class-D PA under wideband envelope modulation situations when integrated in a polar-mode transmitter. The proposed EA, which is shown in Figure 8, consists of a wideband linear EA (WLEA) powered by a switchmode slow envelope power supply (SEPS) to improve power efficiency.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 2 Heading Estimation Based on an AR Model
### KF Process  
The state equation and measurement equation of a linear discrete KF system can be expressed as  
\[\left\{\begin{array}{l}x_{k}=\Phi_{k,k-1}x_{k-1}+w_{k-1}\\ z_{k}=H_{k}x_{k}+v_{k}\end{array}\right. \tag{1}\]  
where \(x_{k-1}\) and \(x_{k}\) represent the state vector at times \(k-1\) and \(k\), respectively; \(\Phi_{k,k-1}\) is the state transition matrix; \(z_{k}\) is the measurement vector; \(H_{k}\) is the measurement matrix; and \(w_{k-1}\) and \(v_{k}\) are zero-mean white noise, whose variances can be expressed as \(Q_{k-1}\) and \(R_{k}\), respectively.
## 5 Experiment Results and Analysis  
To assess the performance of the Proposed platform, an Ethereum private blockchain has been set on a machine having 4 cores CPU: Intel(R) Core(TM) i5-9300H CPU @ 2.40 GHz and a GPU: NVIDIA GeForce GTX 1650 with Max-Q Design. It is worthy to mention that the purpose of the experiment is not to validate the functioning of the platform, since it has been already accomplished during the development stage on Remix IDE. We decided to put the focus on evaluating its performance when increasing subscribing consumers as well as their consumption data sending rate. Accordingly, blockchain nodes are created running on different ports and are connected to each other as peers. The created nodes represent, as a whole, subscribing consumers uploading their electricity consumption data on the provider's smart contract. The evaluated metric is transactions latency, which consists of time in seconds between the transaction being issued by the node calling the smart contract and when being committed to the ledger. As this is illustrated in Figure 21, transaction latency increases with the increase of the sending rate. This is due to the block mining  
Figure 19: Pathloss exponent for device 1 deduced using best fitting curve.  
Figure 20: Pathloss exponent for device 2 deduced using best fitting curve.  
time, as well as to the fact that block size in Ethereum blockchain is limited to a maximum of 20 million gas and thus to a limited number of transactions in a single block. The less gas a transaction consumes, the more transaction can be contained in a single block and consequently, the less transaction latency. The slope of latency increase varies according to the block mining time. Figures 22 and 23 show gas consumption for each function in both smart contracts.  
Figure 21: Transaction’s latency with increasing send rates for different numbers of consumer node.  
Figure 22: Gas consumption for Provider Smart Contract.