esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 2 Literature Review
### Literature Review on Cybersecurity Risk Management
#### 2.2.2 Quantitative Approaches to Cybersecurity Risk Management  
This section reviews a few studies that focus on quantitative approaches to cybersecurity risk management. The quantitative approaches tend to narrow down the scope of the studies to the cyber risk assessment. A Bayesian decision network (BDN) was applied to a framework for network security risk management [56]. The framework consists of several essential processes: risk assessment, risk mitigation, and risk validation and monitoring, which should be done accurately to improve the security level of a network. BDN models information needed for managing security risks, such as information about vulnerabilities, risk-reducing countermeasures, and the effects of implementing them on vulnerabilities. During the risk mitigation process, a cost-benefit analysis of the risk mitigation is conducted with modified Bayesian inference algorithms. Their experiments show that their framework helps improve network security significantly due to the accurate risk assessment and appropriate risk mitigation.  
Another risk assessment framework, called AVARCIDER, extends the specific parameters of ISO 27005 [57]. The implementation of the framework follows a series of activities: launch the risk assessment, identify and assess assets, identify cybersecurity threats, assess the damage level for the vulnerability-asset (dimension) tuples, measure the risk, and perform countermeasures. The whole process is not unique compared to ISO 27005. However, more detailed activities and practical guides are provided. A case study was used to validate the implementation of the framework.  
Realizing the NIST Cybersecurity Framework lacks a financial method useful for the justification of cybersecurity projects, a cost-benefit analysis was integrated into the framework [58]. The study demonstrates how cost-benefit analysis can help organizations select the most appropriate NIST Implementation Tier level and illustrates the relationship between the NIST Implementation Tier levels and a firm's appropriate level of spending on cybersecurity activities. However, this study does not provide operational details for the spending decisions nor explains how financial resources can be allocated among multiple cybersecurity projects.
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 2 Related Work
### Cluster-Range Loss  
Different from other variants of triplet loss, the cluster-range loss [9] focuses on the cluster distribution of positives and negatives. Shrinking the distribution range of positives, whilst extending the distance on the boundary of positive distribution and its nearest negative distribution are the goals of cluster-range loss.  
The requirement of cluster-range loss can be described as below:  
\[D^{n}>D^{p}+\alpha,\quad\forall(n,p)\in\mathcal{C} \tag{6}\]  
\[S^{p}>S^{n}+\alpha,\quad\forall(n,p)\in\mathcal{C} \tag{7}\]  
where Formula (6) is in the form of the Euclidean distance metric and Formula (7) is in the form of the cosine similarity metric. \(D^{n}\) denotes the minimum inter-class distance, and \(D^{p}\) denotes the maximum intra-class distance. Correspondingly, \(S^{p}\) means the minimum intra-class similarity, and \(S^{n}\) means the maximum inter-class similarity. Following the experience in [41], CRL is formed mainly by the cosine similarity metric.  
The exemplar mining method of cluster-range loss is illustrated as Figure 1. For each batch, K speakers are randomly selected with M utterances stochastically picked for each speaker. The hard positive and hard negative for each utterance are selected by virtue of the similarity matrix by the min and max operations. Formally,  
\[h^{p}_{i,j}=\min_{k=1,M}s_{i,j,k} \tag{8}\]  
\[h^{n}_{i,j}=\max_{k=M,\,K\times M}s_{i,j,k} \tag{9}\]  
where \(h^{p}_{i,j}\) and \(h^{n}_{i,j}\) respectively denote hard positive and hard negative in the given batch for the \(j\)th utterance of the \(i\)th speaker.  
Figure 1: Diagram of the online exemplar mining method for cluster-range loss.  
The exemplars are further obtained by the formulas below:  
\[e_{i}^{p}=\min_{l=1\ldots M}h_{i,l}^{p} \tag{10}\]  
\[e_{i}^{n}=\max_{j=1\ldots M}h_{i,j}^{n} \tag{11}\]  
where \(e_{i}^{p}\) and \(e_{i}^{n}\) represent positive and negative exemplars in the given batch for the \(i\)th speaker, respectively.  
Cluster-range loss consists of a hard loss and a normal loss, which could be describe as Formulas (12) and (13). By constructing a mass of positive and negative pairs, the normal loss serves to stabilize the training process. On the contrary, the hard loss centers on the hard positives and negatives and leads to more efficient convergence. \(m\) is a hyperparameter that is larger than one, and it makes normal loss dominate the training process in the initial stage.  
\[\begin{split} L_{\text{CRL}}^{\text{hard}}&=\frac{ 1}{K\times M}\sum_{i=1}^{K}\sum_{j=1}^{M}\left[e_{i}^{n}-h_{i,j}^{p}+\alpha \right]_{+}\\ &+\frac{1}{K\times M}\sum_{i=1}^{K}\sum_{j=1}^{M}\left[h_{i,j}^{ n}-e_{i}^{p}+\alpha\right]_{+}\end{split} \tag{12}\]  
\[\begin{split} L_{\text{CRL}}^{\text{normal}}&=\frac{ 1}{K\times(K-1)\times M^{2}\times(M-1)}\\ \times\sum_{i=1}^{K}\sum_{a=1}^{M}\sum_{p=1}^{M-1}\sum_{n=1}^{(K- 1)\times M}\left[s_{i,a,n}-s_{i,a,p}+\alpha\right]_{+}\\ L_{\text{CRL}}&=L_{\text{CRL}}^{\text{hard}}+m \times L_{\text{CRL}}^{\text{normal}}\end{split} \tag{13}\]
## 3 Demand Forecasting Models and Power Theft Detection Strategy  
In this section, the proposed architecture for the energy demand forecasting and power theft detection is presented in Figure 1, highlighting the main components and functionalities. In particular, the conventional forecasting models for demand forecasting in domestic household are the focus of this section. Existing models, as well as the proposed hybrid RNN-GBRT forecasting strategy, are presented and analyzed. The considered dataset for models training and testing is taken from a household in Sceaux (Paris) and includes a total of 727 daily energy consumption values from 16 December 2008 to 20 December 2017. Moreover, a new strategy is presented to provide an effective solution to the power theft detection problem.  
Figure 1: Architecture of the energy demand forecasting system.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Selective Applications  
Similar to QCM sensors, MOS including ZnO, SiO\({}_{2}\), TiO\({}_{2}\), Co\({}_{3}\)O\({}_{4}\), WO\({}_{3}\), and other combinations [7; 172], have been used extensively for SAW devices and most commonly for selective VOCs detection. For example, ethanol detection has been achieved at 300 \({}^{\circ}\)C using a YX LiTaO\({}_{3}\)-based SAW sensor modified with a ZnO intermediate layer of 1.2 \(\upmu\)m and a WO\({}_{3}\) sensing layer of 150 nm [177]; porous ZnO-SiO\({}_{2}\) bilayer nano-films have been used for NH\({}_{3}\) detection, with SAW sensor sensitivity being better than the one for single layers and was found to be dependent on bi-film conductivity [178]. In the latter system, it was observed that increased ZnO film thickness led to larger absolute sensor response but also to larger response and recovery times; the study concluded that 60 nm of ZnO led to the best sensing performance for 30 ppm NH\({}_{3}\)[178]. The detection of NH\({}_{3}\) concentration levels that were lower than 1 ppm has been also attempted by the same group in the presence of H\({}_{2}\), CO, CH\({}_{4}\), H\({}_{2}\)S, and ethanol using SiO\({}_{2}\)-TiO\({}_{2}\) films of 200 nm that exhibited remarkable system selectivity, stability, and reproducibility [179].
## 5 Results
### Fuzzy Multimodel Control with Two Models  
This approach involves obtaining a controller that provides a global control signal from the local 50% and 60% PWM models. When the speed reference is below 50% of PWM according to the values in Table 2, the local controller obtained for the 50% model is used, when the speed reference is above 60 %, the local controller developed for the 60% model is used. However, when the speed reference is at intermediate values between 50% and 60%, the fuzzy logic calculates the output based on the weights of the membership functions and fuzzy rules. Figure 19 presents the result of the dynamics of the right and left wheels of the robot and the control signal for changes in the reference considering the positive direction, and Figure 20 shows changes in the reference considering the negative direction.  
From Figure 19, just after 6 s, the fuzzy inference between the local controllers is noticeable, as the output dynamics present different dynamic behavior, as observed in the speed curves for the (a) left wheel and (c) right wheel. Additionally, around 6.5 s, a switch to the 60% PWM controller is noticed for both wheels. As this technique uses only two models, the controllers may not be well-adjusted to reference speeds far from their operating points, causing the engine system to present the desired dynamic behavior.  
Figure 18: Gain scheduling for negative direction. Tracking signal in black and system response in blue for (**a**) left wheel and (**c**) right wheel. Control signal applied to the motors for (**b**) left wheel and (**d**) right wheel.  
In Figure 20, there is a fuzzy inference happening between the local controllers at a time close to 6 s. The behavior is the same when compared to the positive direction: that is, for operating points far from the local controllers used, there is a greater number of overshoots and unwanted dynamic behavior. It is important to highlight that even when the controllers are not well-adjusted for speeds far from their operating points, tracking of the input reference is maintained.
## 3 Model Results and Discussion
### Performance on Testing Set  
The prediction performance of the proposed methods can be tested after the best hyperparameters have been identified. The performance metrics of the models are reported in Table 6. The prediction findings for the testing set are shown in Figure 9, with the \(x\) and \(y\) axes representing the experimental and predicted torsional capacity, respectively.  
Figure 8: Learning curves: (**a**) DT, (**b**) BME, (**c**) FRT, (**d**) AB and (**e**) GTB.  
Overall, the difference between the predicted and actual torsional capacity (_MAE_) is low, with an average between 6.9 and 10.7 kNm. The low values obtained for _RMSE_, _cvRMSE_, _MAPE_ and _RMSE_, and the high values obtained for _R\({}^{2}\)_ and _VAF_ are very acceptable  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & _MAE_ (kNm) & _RMSE_ & _cvRMSE_ (\%) & _R\({}^{2}\)_ & _MAPE_ (\%) & _RRSE_ & _VAF_ \\ \hline DT & 10.713 & 17.749 & 16.78 & 0.973 & 12.18 & 0.163 & 0.975 \\ BME & 7.616 & 12.064 & 11.41 & 0.988 & 8.83 & 0.111 & 0.989 \\ FRT & 7.434 & 11.461 & 10.83 & 0.989 & 8.72 & 0.106 & 0.990 \\ AB & 9.901 & 14.725 & 13.92 & 0.982 & 10.83 & 0.136 & 0.983 \\ GTB & 6.856 & 10.618 & 10.04 & 0.990 & 7.21 & 0.098 & 0.991 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance metrics for the testing results.  
Figure 9: Predictions results: (**a**) DT, (**b**) BME, (**c**) FRT, (**d**) AB and (**e**) GTB.  
and show that the used models based on Ensembles of Trees can be considered effective in estimating the torsional capacity of RC beams. From Table 6, the best models in terms of the \(cvRMSE\) are the BME, FRT and GTB models, with values of 11.41%, 10.83% and 10.04%, respectively.  
Figure 10 presents the histograms of the predicted torsional capacity \(T_{R\_Exp}\) from all the models compared with the actual torsional capacity \(T_{R\_Pred}\). It also presents its normal distribution, for which the mean value and standard deviation are presented in Table 7. The normal distribution shows that the error is dispersed randomly.  
Figure 10: Histogram for \(T_{R\_Exp}/T_{R\_Pred}\) for testing results: (**a**) DT, (**b**) BME, (**c**) FRT, (**d**) AB and (**e**) GTB.
# Robust Template-Based Watermarking for DIBR 3D Images
###### Abstract  
Several depth image-based rendering (DIBR) watermarking methods have been proposed, but they have various drawbacks, such as non-blindness, low imperceptibility and vulnerability to signal or geometric distortion. This paper proposes a template-based DIBR watermarking method that overcomes the drawbacks of previous methods. The proposed method exploits two properties to resist DIBR attacks: the pixel is only moved horizontally by DIBR, and the smaller block is not distorted by DIBR. The one-dimensional (1D) discrete cosine transform (DCT) and curvelet domains are adopted to utilize these two properties. A template is inserted in the curvelet domain to restore the synchronization error caused by geometric distortion. A watermark is inserted in the 1D DCT domain to insert and detect a message from the DIBR image. Experimental results of the proposed method show high imperceptibility and robustness to various attacks, such as signal and geometric distortions. The proposed method is also robust to DIBR distortion and DIBR configuration adjustment, such as depth image preprocessing and baseline distance adjustment.  
depth-image-based rendering (DIBR); 3D content; curvelet transform; 1D-discrete cosine transform (1D-DCT); template watermark; DIBR watermarking +
Footnote †: journal: Applied Sciences
# LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
## 4 LoRaWAN Analysis
### Packets Loss  
In this section we focus on the packet loss in a LoRaWAN. Packet loss in this kind of network can be caused by a variety of factors. In particular, research efforts have been carried out to derive a model for packet collision at the LoRa physical layer [14; 15].  
The majority of the LoRaWAN traffic is \"Unconfirmed\", i.e., without requiring an acknowledgment, as it is reported in Table 3. The unconfirmed traffic demands less resources from the LoRaWAN and it is more suitable for devices having a limited energy supply. However, the NS is unaware of lost unconfirmed uplinks, since acknowledgments and/or retransmissions are not required.  
Our goal is to compute a distribution of the packets lost in the transmission. We split the dataset by DevAddress. For each device, we consider the FCnt counter in the frame header (highlighted in red in Figure 5. FCnt is a 16-bit counter which increases its value by one for each uplink packet sent by an ED. The FCnt is a value reported in the header of the packet and it is sent as plain-text, i.e., one can analyse the ratio of lost packets even without having the decryption keys.  
If any packet is lost, then the observed values of FCnt are continuous. However, if some packet is lost, then we will observe some missing values of FCnt. Precisely, if we observe a packet with FCnt = \(n\) and successively we observe a packet from the same ED with FCnt = \(m\), the number of lost packets is \(m-n-1\). This strategy is schematized in Figure 8.  
In order to compute the distribution of the lost packets, we first isolate the packets of all the devices in the network. For each device, we then compute the ratio of the lost packets using the strategy described previously. By computing the PDF distribution of such values, we obtain the result shown in Figure 9. In Figure 10, the same distribution is reported as a CDF. To increase the robustness of the analysis, we considered only packets belonging to those devices which transmit regularly to the network. By looking at Figure 9, we can observe that the majority of the devices loses a small fraction of packets, while others lose up to 50% of packets.  
To perform this analysis, we considered \(N_{C}\) = 3,374,952 uplink packets correctly received and we computed \(N_{L}\) = 1,333,398 lost packets. The cumulative fraction of lost packets in the whole LoRaWAN is, therefore:  
\[\text{lost}=\frac{N_{L}}{N_{C}+N_{L}}=0.283\]  
Figure 8: Scheme of the adopted strategy to compute the number of lost packets for a given DevAddr.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 5 Transfer Learning Strategies
### Adapter Tuning  
Feature and vanilla finetuning techniques could be more parameter-efficient since they require new network weights for every downstream task. So, these techniques require an entirely new model for every downstream task. To address this issue, Ref. [77] proposed a transfer with the adapter module in which a module is added between the layers of a pretrained network. In each block of the transformer, these adapter layers, which aredense-RELU-dense blocks, are added after the feed-forward networks. Since their output dimensionality matches their input, no structural or parameter changes are required to insert adapter layers. During finetuning, most of the original model is kept fixed, and only the parameters from adapter layers get updated. In adapter tuning, task-specific layers are inserted, with only a few trainable parameters added per task. Also, a high degree of parameter sharing occurs as the original network is kept fixed.  
Unlike the feature-based technique, which reads the inner layer parameters to form the embeddings, adapters write to the inner layers instead, enabling them to reconfigure network features. The main hyperparameter of this approach is the feed-forward network's inner dimensionality 'd' since it determines the number of new parameters that will be added to the model. This approach is a promising technique in the experiments conducted in [25]. Adapter tuning attains comparable performance with finetuning on NLU and NLG benchmarks by using only 2-4% task-specific parameters. Experiments from [77] demonstrated how BERT with adapters added only a few (3:6%) parameters per task to attain near SOTA on the GLUE benchmark.
# A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle
## 4 The Performance Analysis
In the case 50 messages were transferred, the proposed method showed an error rate of 0.4% and the existing method of When messages are transferred to the other protocol, the LI-VEG has lower transmission delay than the existing protocols. * When network traffic becomes lower, the LI-VEG has a lower transmission delay than the existing protocols. However, when network traffic becomes higher, the LI-VEG has almost the same transmission delay as the existing protocols. * When messages are transferred between protocols, the LI_VEG has a lower error rate than the existing protocols.
## 2 Materials and Methods
### Reliability and Reproducibility of the Measurements  
The intra-observer reliability and reproducibility were assessed by calculating the intraclass correlation coefficient (ICC). From a subsample of 12 DICOM files, two calibrated researchers performed the 2D and 3D measurements, with an interval of 1 week between each analysis. The coordinates obtained were transferred to a Microsoft(r) Office 365 Excel spreadsheet (Microsoft Corp) and then exported to IBM(r) SPSS(r) Statistics software, version 25.0 (IBM Corp., Chicago, IL, USA), for statistical evaluation.
# Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model
## 2 Materials and Methods
### Neural Network Architecture  
We implemented a neural network model consisting of 1 input layer, 2 hidden layers and 1 output layer. The first hidden layer consisted of 17 neurons and the second hidden layer of 8 neurons.  
The rectified linear unit (ReLU) was implemented as an activation function at each node of the network [25]. The inputs of the classifier were the key components derived by PCA, while the number of output neurons was the predicted class (cancer or normal). A sigmoid activation function was implemented at the output layer to identify the class to be predicted [26].  
We used the [PERSON] optimization algorithm, a modified version of stochastic gradient descent [27]. It was used to assign the parameters that reduce the loss function (binary cross-entropy) as much as possible.  
In order to avoid overfitting, we used a \"early stopping\" function in Keras ([[https://keras.io/callbacks/#earlystopping](https://keras.io/callbacks/#earlystopping)]([https://keras.io/callbacks/#earlystopping](https://keras.io/callbacks/#earlystopping)) accessed on 1 March 2023 ) that stops the training process when overfitting could be occurring (min_delta = 0.005, patience = 5).  
To decrease the time and memory consumption, the model was trained with a batch size = 8 and run for a maximum of 200 epochs.  
The training set was used to train the neural network and the testing set to evaluate the accuracy, sensitivity, and specificity of the tested model. The performance of the models was also evaluated using a receiver operating characteristic (ROC) curve, and the area under the curve (AUC).  
The training and testing datasets were generated 10 times and the performance measures were summarized as the mean and standard deviation.  
The neural network model program was developed in Python using the keras package (version 2.10) [28].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **GEO ID** & **\# of Cancer Samples** & **\# of Normal Samples** \\ \hline Lung squamous cell carcinoma & GSE33479 & 14 & 27 \\ \hline Lung adenocarcinoma & GSE10072 & 58 & 49 \\ \hline Prostate adenocarcinoma & GSE6919 & 65 & 63 \\ \hline Rectum adenocarcinoma & GSE20842 & 65 & 65 \\ \hline Stomach adenocarcinoma & GSE2685 & 21 & 8 \\ \hline Thyroid carcinoma & GSE33630 & 60 & 45 \\ \hline Uterine Corpus Endometrial Carcinoma & GSE17025 & 79 & 12 \\ \hline TOT & & 749 & 485 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The neural network was compared with a random forest classifier implemented in R with the randomforest package [29].