esa-sceva/satcom-chunk-collection huggingface dataset:
# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
## 3 Adopted Approaches for Room Classification Problem
### Error Correcting Output Code (ECOC)  
In practice, a single classifier for multiple room classes may not be sufficient to assist the robot in making the right decision, due to high similarities among the rooms. Thus, it is very important to find a way to improve the robot's best decision, even when applying a CNN classifier with high accuracy. We suggest adopting a decoding approach to address this problem [36]. The purpose of this technique is to improve the practical performance by designing a new classifier that combines multi-binary classifiers through an algorithm. This approach is also known in literature as decomposition [37] or plug-in classification technique (PICT) [38]. There were two main reasons for adopting this technique for this project. The first reason was to take advantage of higher accuracy with binary classes. The second reason was that designing multi-binary classifiers was feasible in the room classification problem, as the number of room classes in houses is limited.  
One of the most popular decomposition techniques is error correcting output code (ECOC). It was proposed by [PERSON] and [PERSON] to address multiclass learning problems [9]. The main concept of this algorithm is to create a binary matrix code that represents multi-binary classifiers of two super groups. Each group consists of many classes, in order to alleviate the overall error in order to obtain the right classification. As shown in Figure 2, the algorithm consists of two stages. The first stage is to create and train multi-binary classifiers. It starts with creating a binary matrix code, in which the number of rows represents the number of classes and the number of columns represents the number of binary models. The data is then classified into two super groups based on zeros and ones in respective columns, where all classes with zeros are assembled in the first group, and the rest of classes are collected in the second group. The last step in this stage is to train all binary classifiers, i.e., the best CNN architecture for this problem, based on columns of the matrix using their super groups. The second stage is to predict a new image using all trained models. Each model gives a probability of predicting one of the super groups. After getting all probabilities of all models, we calculate the distance between all predictions \(p\) and each row in the matrix code. The smallest distance will be considered the correct class of that input image.  
Figure 2: _Cont._The alternative way to get the overall classification is _Regression-ECOC_, which is using the least squares instead of Euclidian distance. Thus, the correct class will be the maximum value of the following equation:  
\[\hat{\mathfrak{q}}=\left(MM^{T}\right)^{-1}M\,\hat{\mathfrak{p}} \tag{3}\]
## 2 Theory and Simulation
### Straddling Oil and Gas Pipeline Dynamics Theory
#### 2.1.1 Fluid Control Equation  
When the fluid flows in the pipe, it needs to follow the basic conservation laws, namely the law of conservation of mass, the law of conservation of momentum, and the law of conservation of energy.  
(1) Mass conservation equation  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{x})}{\partial x}+\frac {\partial(\rho u_{y})}{\partial y}+\frac{\partial(\rho u_{z})}{\partial z}=0 \tag{1}\]  
where: \(\rho\) is the density, \(t\) is the time, and \(u_{x}\), \(u_{y}\), and \(u_{z}\) are the velocity components in the \(x\), \(y\), and \(z\) directions, respectively.  
(2) Momentum conservation equation  
\[\begin{cases}\frac{\partial(\rho u_{x})}{\partial t}+\
abla\left(\rho u_{x} \overrightarrow{u}\right)=-\frac{\partial P}{\partial x}+\frac{\partial x_{ xx}}{\partial x}+\frac{\partial x_{yy}}{\partial y}+\frac{\partial x_{xx}}{ \partial z}+\rho f_{x}\\ \frac{\partial(\rho u_{y})}{\partial t}+\
abla\left(\rho u_{y}\overrightarrow{ u}\right)=-\frac{\partial P}{\partial x}+\frac{\partial x_{xx}}{\partial x}+\frac{ \partial x_{yy}}{\partial y}+\frac{\partial x_{yy}}{\partial z}+\rho f_{y}\\ \frac{\partial(\rho u_{z})}{\partial t}+\
abla\left(\rho u_{z}\overrightarrow{ u}\right)=-\frac{\partial P}{\partial x}+\frac{\partial x_{xx}}{\partial x}+\frac{ \partial x_{yy}}{\partial y}+\frac{\partial x_{yy}}{\partial z}+\rho f_{z}\end{cases} \tag{2}\]where: \(\
abla=i(\partial/\partial x)+j(\partial/\partial y)+k(\partial/\partial z)\), \(\
abla\) is the Hamiltonian differential operator, \(P\) is the pressure on the surface of the fluid element, \(f_{x}\), \(f_{y}\), and \(f_{z}\) are the unit mass forces in the three directions, respectively, and \(\tau_{xx}\), \(\tau_{xy}\), and \(\tau_{zx}\) are the components of the viscous stress, \(\tau\), on the surface of the element.  
(3) Energy conservation equation  
\[\frac{\partial(\rho E)}{\partial t}+\
abla\cdot\left[\overset{\rightarrow}{u} (\rho E+P)\right]=\
abla\cdot\left[k_{eff}\
abla T-\sum_{j}h_{j}l_{j}+\left( \tau_{eff}\cdot\overset{\rightarrow}{u}\right)\right]+S_{h} \tag{3}\]  
where: \(E\) is the total energy of the fluid, \(k_{eff}\) is the effective heat conduction coefficient, \(h\) is the enthalpy, \(h_{j}\) is the enthalpy of the component \(j\), \(j_{j}\) is the diffusion flux of the component \(j\), and \(S_{h}\) is the volume heat source term, including the chemical reaction heat and other user-defined terms.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 3
#### 3.3.2 Case 3: Analysis of 1000-10,000 Frequency Range (Hz)  
At mid-low frequencies (1000-10,000 Hz), the dominant factors involve bulk winding movement, mechanical displacement, and clamping pressure loss (Table 7). These effects are attributed to physical shifts in winding structures, which alter the winding-to-core capacitance and affect the transformer's resonant characteristics.  
Figure 18a reveals that significant deviations begin near 4000 Hz, with the \(X_{latost}\) curve progressively diverging from the fingerprint and crossing below the LCL. This pattern suggests that the transformer is experiencing winding drift or loosening of the clamping structure.  
The \(C_{p}\) and \(C_{pk}\) indices, shown in Figure 18b, remain above 1.4, and are still within an acceptable range, but they display a clear downward trend as frequency increases. This confirms that the structural stability of the windings is beginning to degrade.  
Remarks: It should be noted that the transformer is exhibiting progressive winding drift and mechanical stress. While not yet critical, preventative action is required to reinforce clamping mechanisms and prevent worsening structural instability.
## 2 HSI Procedure
### Preprocessing
#### 2.2.2 Standard Normal Variate (SNV)  
Standard Normal Variate normalization is a preprocessing technique that attempts to take those variations into account due to the effects of light scattering as well as particle size. Light scattering can cause shifts in the levels of absorbance that produce curvature and baseline shifts of the spectra [44]. In contrast with MSC, based on a reference spectrum, SNV normalizes each individual spectrum by the subtraction of the mean and division by the residual spectrum. Indeed, this method is effective in countering scattering effects without the need for least squares fitting, and is useful for variables with different units  
Figure 1: The whole HSI system including (**a**) the hypercube diagram, (**b**) the relationship between spatial and spectral dimensions, and (**c**) the main components of the HSI system. Furthermore, (**d**) there are three approaches for generate hyperspectral images, as follows: when the image is acquired point by point, whiskbroom imaging (point scanning) emerges (left); push-broom imaging (line scanning) is carried out where the image is captured line-by-line along one axis (middle); and finally, area imaging (wavelength scanning) is the case where the entire object or area is imaged at a single wavelength at a time (right) [2].  
or measurement ranges [39]. For instance, a study analyzing wolfberry fruit quality using hyperspectral imaging mentions the enhancement of model accuracy when dealing with variables measured across diverse scales [45]. Furthermore, SNV was effective when it was applied to improve classification tasks for hyperspectral data in quality control systems for fruits and vegetables [15]. It helps to adjust the data by centering and scaling, which improves the accuracy of multivariate analysis techniques, ensuring consistency across variables despite their different units.  
SNV has been widely applied to improve the performance of predictive models on various assessments related to food quality. The technique is important, improving the HSI performance in conducting a task such as assessing the quality and safety of foods. SNV applied to predict beef color when combined with PLSR was reported to perform very well. It was also combined with SNV and algorithms like SPA and GS-SVM to produce very high accuracy in classifying the bruises on apples [46]. The effectiveness of SNV in classifying egg freshness was agreed upon, in particular, when combined with wavelet threshold denoising (WTD) and an improved Support Vector Machine (SVM) model [47]. [PERSON] et al. (2023) also applied SNV to authenticate levels of adulteration in minced Atlantic salmon with high prediction accuracy after the application of a CNN model [48]. According to these findings, the potential of SNV preprocessing to assess the quality of other kinds of food, such as seeds and leafy vegetables, can be studied further in order to guarantee the effectiveness of this method in various food products.
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 2 Literature Review
### Spectrum Handoff  
#### 2.2.1 Proactive Handoff  
In [46], authors anticipated a proactive handoff approach which considered Short Time Backup Channel (STBC), the BC for CRU communication is selected prior to the PU arrival. The handoff judgment in STBC is dependent upon the QoS of the ongoing channel. In this scheme, a backup channel achieves the usage of the bandwidth improvement for a short period whereas, in the complete backup approaches, the BC is reserved continually with the ongoing channel. In the STBC the lowest stage of the handoff wait can be attained as compared to without backup schemes. The STBC chooses the marked channel in the handoff prior to the occurrence of the trigger event. A proactive Fuzzy Logic (FL) based Spectrum Handoff approach is proposed in [47] and presented the major principles of FL to manage troubles professionally. Two FL controllers are used; first controller determines the space among CRU and PU. It examines the control of CRU communication (without causing any influence on the communication of the nearest PU). The second controller measures the waiting of the CRU in the ongoing channel. A handoff is started if the QoS of the CRU is not suitable and the high intervention caused by the CRU on the nearest PU's communication. In [14], authors projected a methodology established upon an increased probability Spectrum Handoff having Cumulative Probability (SHCP) to decrease the handoffs to achieve an improved quality of service. In this method, the PU decides whether to wait or stay on its ongoing operating channel or do a handoff. It depends upon an algorithm of probability estimation however maintaining a backup reserve channel. In [48], authors anticipated a proactive handoff scheme based on a probabilistic and predictive approach. It is slightly mandatory due to the indefinite behavior of PUs. It was planned to decrease the latency and loss of facts, and figure out through spectrum handoff. Proactive handoff gives an excessive option to the SUs to continue their half-finished broadcast on the target station. In [49], authors presented a distributed CRN scheme based on the multi-armed bandit method. Authors examined the blind spectrum choice problem of SUs by considering a fixed handoff stay whose detecting aptitude of Cognitive Radio (CR) is inadequate and the channel statistics are not known in advance. In this scenario, SUs have made the choice of either (i) waiting at ongoing spectrum with low accessibility or (ii) handing off to another spectrum with higher accessibility.
# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 6 Conclusions  
This experimental research introduced a distributed boundary detection algorithm EEBD. Under the same boundary detection probability, the proposed algorithm can reduce the number of nodes to be deployed compared to the two benchmark algorithms in monitoring a sea area. The boundary width can be customized to not only reduce the communication consumption of reporting but also to satisfy the detection accuracy. The simulation results demonstrate that the EEBD performs better in terms of accuracy with both low and high node density. The following two-fold directions are recommended for next study.  
1. To further decrease the sensor nodes' energy consumption and extend network lifespan under the condition of ensuring the accuracy of event boundary detection, the energy consumption model and the event reporting routing protocol need further research.
2. A complete event dataset is necessary before the boundary detection algorithm is executed. Due to the adverse conditions of the sea, the collected marine big data always experience a serious data loss phenomenon in WSNs [22]. To further improve the accuracy of event boundary detection, the missing data recovery of WSNs is an important research topic.
3. Real marine event boundary detection experiments and event boundary dynamic tracking are also key research directions for the future.  
All the authors have contributed equally to this research work.  
This work was supported by the National Natural Science Foundation of China [Grant No. 51579143, 51709167, 61701299]; the Shanghai Committee of Science and Technology, China [Grant No. 18040501700]; the Major Special Science and Technology Project of Hainan Province [Grant No. ZDKJ2017012]; the Graduate Innovation Foundation of Shanghai Maritime University [Grant No. 2016 ycx042, 2017 ycx030].  
The authors would like to show their deepest gratitude for the data and technical support provided by Maritime Stereo Search and Rescue Center of Shanghai Maritime University.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of Operating Conditions
#### 3.2.4 Effect of Rectisol Operating Pressure  
The effects of Rectisol pressure on the syngas composition, SN, and methanol synthesis yield are presented in Figures 8 and 9. CO\({}_{2}\) content continuously decreases from 11.13 kmol to 0 kmol when the Rectisol pressure raises from 1 bar to 80 bar. As a result, the SN increases from 0.87 to 2.5 while the Rectisol pressure increases. When the pressure is around 40 bar, the SN value is close to 2 where the methanol yield reaches the maximum of 8.02 kmol. When the Rectisol pressure increases from 40 bar to 60 bar, the solubility of methanol to absorb CO\({}_{2}\) continues to increase, producing a significant drop in the CO\({}_{2}\) content in the syngas. As a result, the methanol yield reaches the minimum of 0.01 kmol. This is because there is not enough CO\({}_{2}\) in the syngas to react with H\({}_{2}\) to produce methanol.  
Figure 6: Effect of Rectisol temperature on syngas composition (40 bar).  
Figure 7: Effect of Rectisol temperature on SN and methanol yield (40 bar).
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Perfect Forward Secrecy  
Perfect forward secrecy means that when using the secret keys of the server and client, an attacker \(A\) still cannot obtain the previous session keys. In our proposed scheme, the secret key of the server is \(k_{s}\), and that of the client is the data \(\{B_{i},C_{i},m,b,K_{s}\}\) stored in the memory device. With the above assumption, \(A\) can obtain \(T_{1}\), \(T_{2}\), \(A_{i}\), \(R_{c}\), and \(R_{s}\). However, when \(A\) intends to compute the shared session key \(SK=h(J_{i}||T_{1}||T_{2})=h(r_{s}A_{i}R_{c}||T_{1}||T_{2})=h(r_{c}A_{i}R_{s}||T_ {1}||T_{2})\), \(A\) faces difficulties in extracting \(r_{c}\) from \(R_{c}\) or \(r_{s}\) from \(R_{s}\). Therefore, \(A\) cannot obtain the previous session keys. Thus, our proposed scheme provides perfect forward secrecy.
## 2 Description of MCS  
The structural model of the MCS used in this study is shown in Figure 1. Photovoltaic panels (PV) to generate electricity and a power grid to purchase electricity are used to meet the self-consumption and lighting consumption of the air-source heat pump (ASHP) and ground-source heat pump (GSHP), along with other equipment. At the same time, the MCS is equipped with energy storage batteries (ES) to store the rich electricity generated by the PV panels. The ground-source heat pump and air-source heat pump are used to meet the cooling load and heat load of the air conditioning. The plate solar collector (SC), gas boiler (GB), ground-source heat pump, and air-source heat pump are used to meet the domestic hot water load. When the hot water supply is greater than the demand, the remaining hot water is stored in the hot water storage tank (HS). In the process of energy supply, the cascade utilization of energy during system operation is fully considered.  
The energy is input, converted, and output by the system through electrical balance, thermal balance, and cold balance. The energy balance equations can be expressed as [14]:  
Electrical balance:  
\[P_{PV,out}(t)+P_{e}(t)+P_{ES}^{out}(t)=P_{GSHP}(t)X_{GSHP}+P_{ASHP}(t)X_{ASHP }+P_{ES}^{in}(t)+P_{eb}(t) \tag{1}\]  
Thermal balance:  
\[P_{GSHP}(t)\eta_{GSHP}^{h}(t)X_{GSHP}+P_{ASHP}(t)\eta_{ASHP}^{h}(t)X_{ASHP }+P_{GB,in}^{g}(t)\eta_{GB}(t)+P_{SC,out}(t)+P_{HS}^{out}(t)=P_{ah}(t)+P_{wh}( t)+P_{HS}^{in}(t) \tag{2}\]  
Cold balance:  
\[P_{GSHP}(t)\eta_{GSHP}^{c}(t)X_{GSHP}+P_{ASHP}(t)\eta_{ASHP}^{c}(t)X_{ASHP }=P_{ac}(t) \tag{3}\]  
where \(P_{PV,out}(t)\) is the PV forecast output at time \(t\) (kW); \(P_{e}(t)\) is the electricity purchased by the system at time \(t\) (kW); \(P_{ES}^{in}(t)\) and \(P_{ES}^{out}(t)\) are the charging and discharging power of the ES equipment at time \(t\), respectively (kW); \(P_{GSHP}(t)\) is the total input power of the GSHP at time \(t\) (kW); \(X_{GSHP}\) represents the GSHP's running state (0-1, where 1 is the running state and 0 is the shutdown state); \(P_{tb}(t)\) is the terminal power load of the system at time \(t\) (kW); \(\eta_{GSHP}^{h}(t)\) is the heating coefficient of the GSHP at time t; \(\eta_{ASHP}^{h}(t)\) is the heating coefficient of the ASHP at time t; \(P_{GB,in}^{g}(t)\) represents the natural gas power consumed by the GB at time \(t\) (kW); \(\eta_{GB}(t)\) represents the thermal efficiency of the GB at time t; \(P_{SC,out}(t)\) is the SC's output power at time \(t\) (kW); \(P_{HS}^{in}(t)\) and \(P_{HS}^{out}(t)\) are the charging and discharging power of the HS equipment at time t, respectively (kW); \(P_{dh}(t)\) and \(P_{wh}(t)\) are the terminal air conditioning heat load and domestic hot water load of the system at time \(t\) (kW); \(\eta_{GSH}^{c}(t)\) is the refrigeration coefficient of the GSHP at time t; \(\eta_{ASHP}^{c}(t)\) is the refrigeration coefficient of the ASHP at time t; and \(P_{ac}(t)\) is the terminal air conditioning cooling load of the system at time \(t\) (kW).
# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 7 Discussion
To carry out the experimentation, we have used the SAD corpus and we have compared three neural network architectures (LSTM, BiLSTM and CNN) and three models based on transformers (XLM, M-BERT and BETO) to study the performance of the novel transfer learning methods. The results obtained show that the monolingual pre-trained model BETO outperformed M-BERT and XLM. Thus, one of the major observations in this paper is that this model can more accurately modulate the vocabulary of the corpus because it is trained in Spanish, as opposed to models trained on multilingual corpora. Finally, we have observed some limitations during the development of the NLP system and its validation. On the one hand, the SAD corpus contains about 5000 annotated tweets, which is a low number of examples to train neural network-based systems and our results show that the data we currently have is not enough to benefit from the potentials of these architectures. On the other hand, all Transformer-based models, although they obtain top performance, have not been trained using a vocabulary as specific as the one used in social networks, which means that they have not been able to learn from the informal language used in social networks. Therefore, it can be seen that there is still room for improvement in the detection of mental disorder comments from social media by applying state-of-the-art methods.
# Privacy-Preserving Monotonicity of Differential Privacy Mechanisms
## 3 Notation and Problem Statement
### Privacy and Utility Metrics  
To numerical and categorical data, some privacy metrics and utility metrics are necessary for formalizing the model of trade-off. It cannot compare Euclidean distances between categorical data, because the categorical data directly indicate the semantics. Thus, Euclidean distance cannot be used as privacy metric for categorical data. Since differential privacy mechanisms make randomized selection of categorical data, the uncertainty can be used as privacy metric. In this paper, the expected estimation error and entropy are used as privacy metrics. The modulus of characteristic function and the variant \(1-NE\) of normalized entropy are used as utility metrics.  
Let \(s^{{}^{\prime}}_{i}\) be random perturbation value of the original \(s_{i}\). The expected estimation error (\(EEE\)) of query results is  
\[EEE=\sum P(s^{{}^{\prime}}_{i})||s^{{}^{\prime}}_{i}-s_{i}||_{1} \tag{1}\]  
Since the characteristic function of any random variable completely defines its probability distribution, we use the modulus of characteristic function as utility metric. When \(X\) is a continuous random variable and the probability density function of \(X\) is \(p(x)\), the characteristic function of \(X\) is  
\[\phi(t)=\int_{-\infty}^{+\infty}p(x)\exp(itx)dx,-\infty<t<+\infty \tag{2}\]  
Thus, the modulus of \(\phi(t)=a+ib\) is \(|\phi(t)|=\sqrt{a^{2}+b^{2}}\). In this paper, the \(|\phi(t)|\) denotes the data utility of a differential privacy mechanism. When use the modulus \(|\phi(t)|\) of characteristic function \(\phi(t)\) as utility metric, the parameter \(t\) is a preset fixed value.  
By random selection query results of a database, the entropy (\(ENT\)) of output is  
\[ENT=-\sum P(s_{i})\log_{2}P(s_{i}) \tag{3}\]  
We can get the normalized entropy of \(n\) outputs is  
\[NE=-\frac{\sum P(s_{i})\log_{2}P(s_{i})}{\log_{2}n} \tag{4}\]  
Since normalized entropy indicates the uncertainty of output of random selection and \(0\leq NE\leq 1\), we use \(1-NE\) denotes the utility metric of output of random selection.
## 3 Experimental Results  
In the experiments, one hundred photos were taken by our mobile device to evaluate the proposed app's performance in calculating the number of people in each image. The state-of-the-art YOLOv4 (You Only Look Once version 4) network [24] and VJ algorithm are conducted for performance comparisons. The recall rate, precision rate, and _F_-measure are utilized to evaluate the calculated number of people's correctness.