esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 2 Materials and Methods
### Experimental Protocol  
On the one hand, the reference system was developed using the MATLAB Statistics and Machine Learning toolbox. The optimal hyperparameters were obtained by minimizing a cross validation loss by means of a Bayesian optimizer.  
On the other hand, all the LSTM-based systems were implemented with the Python's packages Tensorflow [43] and Keras [44]. In all cases, the architectures were trained during a maximum of 50 epochs using the Adam optimizer with an initial learning rate of 0.0002, a batch size of 64 and a binary cross-entropy loss function.  
Regarding the experimental protocol, a subject-wise three-fold cross validation was used, according to [12]. Specifically, the database was split into three balanced groups. In each fold, two groups were used for training, and the remaining group was divided into two parts, one was kept for validation and the other one for test. The experiments were repeated three times rotating the training, validation and test sets, averaging the results afterwards. In all cases, training, validation and test sets were disjoint, in such a way that all gaze or speech recordings from the same subject were included in only one of these sets. We adopted this subject-independent configuration in order to prevent the system to learn the participant's identity instead of his/her honest or deceptive behaviour.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 5 Evaluation
### Write Performance Test  
The focus of this paper is the research of erasure code technology for cold data in big data systems; therefore, the data writing performance is a key factor. Cold data are usually large quantities of data written sequentially at one time and rarely change; so, we focused on the sequential write performance of the massive data erasure code in the cold backup strategy. The random write function will be implemented in future work. The writing performance tests were also divided into two categories: the IOPS and the throughput.  
The result of the IOPS in sequential writing is shown in Figure 13. From the figure we can see that the trend for the sequential writing was quite different from that for the sequential reading.  
Compared to the IOPS of the sequential reading operation, the IOPS of the sequential writing operation was significantly smaller than that in the replica mode. This is because the writing operation required more metadata and disk operations relative to the read operation. More importantly, in the case of a triple replica, the writing operation generated two times more network bandwidth than the quantity of data written.  
Figure 14 shows the throughput of a sequential write operation. We can see that the quantity of data written became larger as the number of data blocks increased. However, once the data block was larger than 64 K, the growth tended to be flat. Unlike the read operation, the throughput of the erasure code write operation was larger than the throughput of the copy write operation.  
Figure 12: The throughput in the random reading.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 6 Secure Homomorphic K-Means Clustering Protocol
### Secure Cluster Assignment (SCA) Protocol  
Algorithm 3 describes how to securely assign data points to clusters in the K-means algorithm using the Paillier cryptosystem. The input to the algorithm is the encrypted squared distances between data points and cluster centers, and the output is the encrypted cluster assignments.  
Set \(U=\{E(d_{ij}^{2})\}_{j=1}^{K}\), representing the set of encrypted distances from all data points to the cluster centers. Here, \(E(d_{ij}^{2})\) denotes the encrypted squared distance from data point \(i\) to cluster center \(j\). Repeat the following steps until \(K=1\): Pair the encrypted distances into \(s=\frac{K}{2}\) pairs, such as \((E(d_{1}^{2}),E(d_{2}^{2})),\ldots,(E(d_{IK-1}^{2}),E(d_{IK}^{2}))\). Use the secure minimum selection (SMMS) protocol to find the smaller encrypted distance in each pair. Update set \(U\) with the smaller encrypted distance from each pair and set \(K=s\). When \(K=1\), output the minimum encrypted distance \(E(d_{ij}^{2})_{\min}\) and its corresponding index \(E(\lambda_{i})\).  
```
0: Encrypted distances \(\{E(d_{ij}^{2})\}_{j=1}^{K}\)
0: Encrypted cluster assignments \(\{E(C_{\lambda_{i}})\}\)
1:For the CS and CSP:
2:Set \(U=\{E(d_{ij}^{2})\}_{j=1}^{K}\);
3:repeat
4: The CS groups \(\{E(d_{i1}^{2}),E(d_{i2}^{2}),\ldots,E(d_{IK}^{2})\}\) to \(s=\frac{K}{2}\) pairs as \((E(d_{11}^{2}),E(d_{i2}^{2})),...,(E(d_{IK-2}^{2}),E(d_{IK-1}^{2}))\);
5: Run the SMMS protocol to find the small ciphertext in each pair;
6: The cloud server sets \(K=s\);
7: All the small ciphertexts are assigned to the set \(U=\{E(d_{ij}^{2})\}_{j=1}^{\frac{k-1}{2}}\).
8:until k = 1
9:output \(E(d_{ij})_{min}\) and get its index \(E(\lambda_{i})\).
10: Initialize empty encrypted clusters: \(\{E(C_{j})\}_{j=1}^{K}\)
11:for each data point \(i\)do
12: Assign the encrypted data point \(E(x_{i})\) to the corresponding encrypted cluster based on the encrypted index \(E(\lambda_{i})\): \(E(C_{\lambda_{i}})=E(C_{\lambda_{i}})\cup\{E(x_{i})\}\)
13:endfor
14:return Encrypted cluster assignments \(\{E(C_{\lambda_{i}})\}\)
```  
**Algorithm 3** Secure Cluster Assignment (SCA) Protocol using Paillier Cryptosystem
# Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices
## 5 Discussion  
As a result of our modeling, by using simple examples, it was demonstrated that the connection of the sensory part and neurons based on memristors without analog-to-digital and digital-to-analog conversions makes it possible to implement analog processing of visual information in hardware using formal and spiking ANN models. This architecture can be scaled to the size of modern matrices in photo and video recording devices and used as a hardware accelerator for ANN models currently used to work with images, and can also be used as a platform for the further development of this direction in accordance with the developed roadmap (Figure 11).  
The operation of a formal ANN is demonstrated on one dense layer. Such layers can be combined together to form multilayer feed-forward ANNs. Since the main mathematical operation for most formal ANN architectures (convolutional ANNs, LSTMs, etc.) is matrix multiplication, the results of the demonstration can be extended to these--one part of the crossbars will perform the functions of memristor-based neurons, and the other part, for example, can perform the functions of core convolutions based on memristors [61]. From a mathematical point of view, this is equivalent.  
To demonstrate the operation of a spiking ANN, only one special case of a self-learning option is proposed, inspired by article [58]. Currently, there are other approaches to train spiking ANNs [37,77,78], and in many of them, as in the example considered above, information is encoded according to spike frequency. This means that the developed concept can be further developed and improved by developing new designs of analog neuromorphic computer vision systems based on memristive devices.  
In any case, the proposed architecture is at its initial stage of development and is associated with a large number of related tasks. One of the subjects of further research could be replacing the \"photodiode-memristor\" circuit with a single device--a memristor--that changes its resistance depending on the light intensity. Currently, such devices are being developed based on MIS (metal-insulator-semiconductor) structures, for example, based on ZrO\({}_{2}\)(Y) films with self-organized Ge nanoislands [79]. There are also tasks associated with the integration of the developed class of systems with the existing infrastructure, which are made possible with the improvement of component manufacturing technologies and the development of models and algorithms. As a result of the generalization and systematization of tasks, a road map for the development of this area was formed, presented in Figure 11.  
Figure 11: Roadmap for further development in this area (DVS—dynamic vision sensor; IR—infrared).
## 3 Methodology
### Diabetes Prediction Models
#### 3.5.1 Convolutional Neural Network  
Here, in this section, we explained the role of the CNN by explaining the functionality of the different layers for the prediction of Type 2 diabetes mellitus. Initially, CNN was used mainly for image classification, but today, CNN can be applied in various domains.  
**Definition 1**.: _A CNN is a special kind of multi-layer perceptron identical to a traditional neural network where specific inputs are supplied to each neuron. These are self-learned neurons that learn from data with the assistance of weight and bias by conducting such operations as the dot product [23]. CNN is made of layers, namely: a convolutional layer, a maximum pooling layer, a flattening layer, and a fully connected layer. The goal of the convolutional layer is to learn the feature representation for the input data. It is the heart of the network and has local connections and weights for common features. In the first stage, input parameters are passed through the kernel and then outputs are sent via a nonlinear activation function ReLU, which does not activate all the neurons at the same time. It only activates those neurons which are in the range of 0 and 1. Then output neurons are passed through the pooling layer, which may be thought of as a fuzzy filter since it decreases the dimensionality of the features while increasing their robustness. Finally, the fully connected layer receives signals from the preceding layers and delivers them to each neuron in the system. The output layer, which is generally a softmax classifier, then does the classification. As shown in Figure 4, In our case, the PIDD consists of six features as input and one as target output, which consists of two values such as 0 and 1. Input features are described as where input parameters belong to a feature set; here, the outcome variable belongs to a class label, such as 1 specifies a diabetic and 0 specifies a healthier one._  
In our proposed model, these input features were passed through convolutional 1D, where we applied batch normalization (BN) along with ReLU. Here, BN normalizes input features into batches, which minimizes the gradient saturation during covariate shift [45], and the ReLU activation function decreases the redundancy by allowing values that range between 0 and 1 to accelerate the speed. The complete process is mathematically explained in Equation (5) [46]  
\[y(k)=f\left(\sum_{1}^{N}\widetilde{x}j(k)\times Wp(k)+bp(k)\right) \tag{5}\]  
where  
\(W_{p}:\) specifies weight  
\(\widetilde{x}:\): Batch Normalization of input features  
\(f(.):\) represents the activation function ReLU  
Figure 4: Architectural CNN Model for Diabetes Prediction.  
\(p:\) no of filters  
\(b_{p}:\) bias term value ranges from 0-1
## 3 Results and Discussions  
This section provides results based on the optimal models, which result from the random search method with the defined space in Table 2.  
The best R\({}^{2}\), RMSE, and CV for the different configurations due to the number of inputs and outputs for the 10% testing set (80% training set and 10% validation set) are in Table 1. In Table 1, the SVR provides the optimal performance by configuring one historical timestamp to predict the electricity consumption at the sequential timestamp. This capital scenario implies that the resident's living pattern related to electricity usage is highly time-dependent as SVR can use the electricity consumption in this half an hour to predict that in the next half an hour well. Two out of three models in Table 1 depict comparable performance with 14% and 15% CV from the SVR and the XGBoost. The LSTMprocures a slightly bigger CV than the other two. More insights regarding the number of inputs and outputs can be gained from Table A1. Table A1 lists how the SVR responds to the different number of inputs and outputs. It is apparent that the SVR is subjected to a large number of outputs showing less qualified to predict four or more outputs, especially using limited inputs. However, when three outputs are required, the SVR predictor can still at least promise 90% R2 and 19.801% CV.  
Table 2 displays some research in the same area to benchmark this study. They compete in terms of temporal granularity, building type, features, and data size. Compared to [38], which only focused on the cooling system, which relies on fewer features, this study investigates the electricity for the whole building, which can be affected by plenty of factors that make it hard to predict. CV provided by the SVR is slightly higher. However, the absence of weather features leads to no additional sensors being required. The same R2 can be seen from the SVR compared to [16], which also introduced seven weather conditions. It is noted that [16] aimed to predict hourly results, while the SVR model can predict in more detail two slots within one hour with 93% R2. The SVR performs better than [18], which implemented 140 sensors among the three residential buildings. The SVR uses fewer features compared to [22]. The 84-day dataset used in [22] is considered diminutive compared to the 1-year dataset used in this article. The SVR delivered a better CV of 14.25%, while [22] achieved a 14.88% CV for the sub-hourly prediction.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 7 Security Analysis  
In this section, we prove that the proposed privacy-preserving K-means clustering protocol securely realizes the ideal functionality \(\mathcal{F}_{\text{KMeans}}\) in the presence of a semi-honestadversary corrupting either the cloud server (CS) or the computation service provider (CSP), but not both.  
The proof proceeds using the standard simulation paradigm: for each party that may be corrupted, we construct a probabilistic polynomial-time (PPT) simulator that can simulate the party's view using only the allowed leakage. If such a simulator exists, then the real and ideal executions are computationally indistinguishable, and the protocol is secure.
## 3 Laboratory Experiment and Measurement
### Sensor Experiment
#### 3.1.3 Stability  
Two CO\({}_{2}\) samples with concentration levels of 500 ppm and 2000 ppm were used corresponding to the normal CO\({}_{2}\) concentration and suitable CO\({}_{2}\) concentration for plant photosynthesis in a greenhouse. Based on the results shown in Figure 7a, the differential voltage was from 0.682 to 0.688. The peak differential voltage is 0.688, corresponding to a peak measured CO\({}_{2}\) concentration of 520 ppm. Similarly, for the measurement of the 2000 ppm sample, the differential voltage was from 0.104 to 0.126, and the peak voltage and concentration were 1981 ppm and 2022 ppm, as shown in Figure 7b. In summary, considering the noises and interference, the fluctuations of 1.11% (22/2000) and 4% (20/500) of the measurement results on the 2000 ppm and 500 ppm samples, respectively, are acceptable in agriculture application.  
Pure N\({}_{2}\) was used to derive the theoretical LoD and the relationship between system noise and averaging time. The measurement results for 10 h are shown in Figure 8, including the differential voltage in the lower branch and the converted CO\({}_{2}\) concentration in the upper branch using Equation (5). The peak fluctuation value of the converted concentration is \(-9\) ppm, with an average of \(-2.8912\) ppm, and the standard deviation was 2.2322 ppm.  
Figure 6: The measured averaged differential voltage (\(\Delta U\)) versus the CO\({}_{2}\) concentration within 0-50 ppm. The inset shows the recorded differential voltage (\(\Delta U\)) results of a 30 ppm sample for 10 min.  
According to the long-term experiment on the 0 ppm sample (pure N\({}_{2}\)), the Allan variance of this sensor was plotted in Figure 9. When the averaging time is 1 s, the theoretical detection limit is nearly 6 ppm. Based on the relationship between the noise influence and integration time, when the curve slope is nearly \(-1\), the sensor was dominated by the white noise, and this situation ends at nearly 22 s integration time. Under this situation, the theoretical detection limit is 0.5354 ppm. In the intersection point of 1/2 slope, the Allan variance reaches the lowest value corresponding to the theoretical detection limit at the price of 80 s response time. The possible noise comes from temperature drift or certain environmental influences under this situation. In consideration of the special environment in a greenhouse, which is a large delay system, the not stringent measurement requirement in response time indicates that it is appropriate to increase the averaging time to enhance the stability of the sensor.  
Figure 8: Long-term measurement results on a 0-ppm CO\({}_{2}\) sample (pure N\({}_{2}\)) for 10 h with a 1-s sampling period.  
Figure 7: (**a**) Long-term measurement results on a CO\({}_{2}\) standard sample with a concentration level of 500 ppm; (**b**) Long-term measurement results on a CO\({}_{2}\) standard sample with a concentration level of 2000 ppm.
## 1 Introduction  
An underground utility tunnel is an underground structure in which major supply facilities of daily life resources, such as electricity, communication, and water supply, are installed in a downtown area [1]. Underground utility tunnels, which serve as a major supply chain in a downtown area, require safety management to prevent events such as pipeline rupture or fire due to its nature and positioning. However, maintenance and management of these facilities is difficult because of the of the long and narrow characteristics of underground utility tunnels. To address these issues, numerous studies on digital twin technology for efficient monitoring are currently underway. Digital twin technology builds a virtual model of the real world, reflecting the shape and location of objects. A safety-and-disaster digital twin model must reflect the changes in the state of objects and spaces in the real world, such as cracks in structures, fire outbreaks, and fire-extinguishexpication changes. Underground utility tunnel ducts are mainly monitored through closed circuit television (CCTV) cameras, and many studies have been conducted using artificial intelligence (AI) technologies to determine their shapes. However, studies that focus on the location of changed objects on digital twin models are limited. Technologies using stereo and depth cameras are widely used to determine the location of changed objects; however, changing CCTV cameras in underground utility tunnels installed in long extensions is expensive and time-consuming.  
Moreover, a monocular camera image such as a CCTV camera image lacks feature information related to the size of an object. Therefore, various algorithms using deep learning have been proposed, and their accuracy has been sufficiently improved to obtain image depth close to the results obtained from binocular images. The goal of this study is to identify the applicability of these algorithms in actual underground utility tunnels. In this study, the DenseDepth transfer learning method, which uses depth image of light detection and ranging (LiDAR) depth data, and the coordinate system conversion method through floor plan projection were used to estimate image depth, and their results were compared. Therefore, it was possible to derive an appropriate method for estimating distance using a single camera in an actual underground utility tunnel. This study contributes to solving the problem of reflecting the position of an object in the real world to the digital model.
## 2 Mathematical Modeling of Class-E Series-Tuned RFPAs  
This section explores the mathematical modeling of Class-E RFPAs. For simplicity, assumptions include a 50% switching duty cycle and zero on-resistance of the transistor.  
The zero voltage switching (ZVS) and zero voltage derivative switching (ZVDS) are given by Equations (1) and (2) [21].  
\[V(\omega t)|_{t=2\pi}=0 \tag{1}\]  
\[\left.\frac{dV(\omega t)}{d(\omega t)}\right|_{\omega t=2\pi}=0 \tag{2}\]  
Based on existing literature, two commonly used topologies are addressed in this work [22]: Topology 1, which assumes an infinite DC feed. It uses a DC choke to provide very high resistance to the RF signal, and Topology 2, which incorporates a finite DC feed. Topology 2 offers an infinite number of potential solutions; however, this work examines two cases, denoted as Topology 2.1 and Topology 2.2, to highlight key design considerations.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Privacy Protection  
In the proposed protocol, \(DID_{i}\) implicitly involves the identity of \(SN_{i}\), \(ID_{i}\), where \(DID_{i}=K_{1}\oplus ID_{i}\). Attackers cannot derive \(ID_{i}\) from \(DID_{i}\) because \(ID_{i}\) is protected by \(K_{1}\) and the security of \(K_{1}\) (\(=T_{mk}\)\(mod\)\(p\)) is based on the ECM-DH problem. Additionally, the group identity \(GID_{im}=(DID_{1}\), \(DID_{2}\),...,\(DID_{i}\),...,\(DID_{n})\) is protected by \(h(K_{1}||G_{im}||T_{s})\) (or \(K_{1}\)). No one can derive \(GID_{im}\) from the revealed message \(HGP_{im}\), where \(HGP_{im}=h(K_{1}||G_{im}||T_{s})\oplus GID_{im}\). Another group member \(SN_{i}\) cannot recognize the group members of \(G_{im}\) to which \(SN_{i}\) does not belong. Thus, the proposed protocol ensures users' privacy protection.
## 1 Introduction  
The net exchange of carbon between ecosystems and the atmosphere can play a key role in regulating the human-induced increase in CO2 and global changes. Carbon use efficiency (CUE), defined as the ratio between net primary production (NPP) and gross primary production (GPP), describes effectiveness of vegetation in storing carbon and is of significance for understanding carbon biosphere-atmosphere exchange dynamics [1,2]. At the ecosystem scale, CUE can be used as a measure, whether the terrestrial ecosystem is a carbon source or sink. In earlier studies, it was considered that CUE does not change [3,4] varies slightly from 0.4 to 0.6 over a wide range of plant functional types and environmental conditions. That is why in many terrestrial C-cycle models, it was assumed that CUE does not change. In recent years, there were obtained many results that argue that CUE is not constant, but varies with: ecosystem types, successional stage and stand age, geographical location, nutrient availability, and environmental conditions.  
Droughts are frequently accompanied by strong heat waves. This combination complicates the understanding of the impact of drought on productive processes and respiration, consequently on CUE. Many studies on the impact of a single abiotic stress condition--such as drought or heat--have provided important information, but often they do not help us understand the effects of a combination of two stresses on plants. The response of plants to a combination of different abiotic stresses is unique and cannot be directly extrapolated from simply studying each of the different stresses applied individually [5].  
In the period 2000-2014, South Eastern Europe was hit by four extreme events -- in 2000, 2003, 2007, and 2012 --characterized by high temperatures and water stress during the growing season. This posed the issue of the impact of extreme events resulting from climate change on the CUE of ecosystems. That is why the objectives of the current study were to: (1) map the carbon use efficiency in South Eastern Europe; (2) study dependents of CUE on extreme drought and temperature/heat waves; (3) study the dependence of CUE on main PFTs/biomes in the region; and (4) examine the CUE elevation profiles.