esa-sceva/satcom-chunk-collection huggingface dataset:
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 7 Simulation and Results
### Simulation Results
From Figure 9(b), it can be seen that: (1) the query time increases with \(k\) in all schemes; (2) when \(10\leq k\leq 30\), the query time of our SELS is at least 1/3 of that of other schemes, i.e., the efficiency of our scheme is at least 3 times better than other solutions due to the same reason as above. In addition, it also can be concluded that the effect of \(k\) on the query time is quadratic in all schemes. The reason is that when the number of pseudo-IDs for each user changes, the number of effective users and the number of users (including friends and dummies) within the issuing user's query distance both changes, and the above two kinds of users changes in multiples. While in other schemes, different \(k\) only leads to the change in the number of users (including friends and dummies) within the issuing user's query distance. Figure 9: The simulation results for scenario-2. Figure 11 shows the results under scenario-4. From Figure 10(a), it can be seen that the number of effective users is stable with different \(n\) in all schemes. The reason is that when other parameters are fixed, the increasing \(n\) can not affect the number of effective users since the number of the issuing user's friends does not change. Thus, the number of effective users that needs to be sent to \(LS\) has no change. However, the number of effective users in our SELS is smaller than other solutions since \(LS\) in our scheme only needs to run the distance comparison function for the friends are located in the issuing user's associated grids. From Figure 10(b), it can be seen that: (1) the query time increases with \(n\) in all schemes; (2) when the total number of users \(n\) changes from 1000 to 2500, the query time of our SELS is at least 1/3 of that of other schemes, i.e., the efficiency of our scheme is at least 3 times better than other solutions since a smaller number of effective users need less computational cost. In addition, it can be concluded that the effect of \(n\) on the query time is linear in all schemes. The reason is that when other parameters are fixed, the only factor that affects the query time is a linearly increasing number \(n\), which increases the number of dummies within the issuing user's query distance.
# A Test Platform for Managing School Stress Using a Virtual Reality Group Chatbot Counseling System
## 2 Literature Review
### Immersive Technology Applied as a Psychological Treatment  
Some studies have demonstrated the advantages of immersive virtual environment technology in the field of psychological treatment. In [PERSON]'s research, the Scopus database was used to conduct a literature search and the results show that immersive technology has more advantages than traditional treatments including easier use of experimental control and higher realism to users [15]. Common immersive technologies, such as VR and augmented reality (AR) environments, have been applied in healthcare research.  
For an AR environment applied in healthcare research, an iPad can be connected with Kinect sensors to form a highly convenient clinical consulting system [16]. Patients can seek medical consultation as long as they are at home. Such low-cost and portable technology has greatly benefited patients and allows physicians to visually explain complex medical conditions to patients.  
For a VR environment applied in healthcare research, prior research used VR for exposure therapy and designed seven VR scenarios for driving phobias in terms of the driving situations, such as road, mountain road, and highway driving during the day or night. The experiments collected the physiological feedback of subjects using the biosensors. The results show that the subjects have a higher evaluation of the immersive experience after the system is optimized [17]. In addition, the results of the meta-analysis demonstrate that VR can be used as an effective intervention to distract patients with needle-related surgery and relieve pain in children [18].  
With the aging of the population in many countries, the emphasis on the healthcare of the elderly has become a new research area. Elderly people often have the possibility of falling, which can damage their body and mind. Many researchers have extended the application of VR in health care to fall prevention and remote rehabilitation of the elderly population. According to the analysis results [19], VR rehabilitation training can be used as an auxiliary system for physical therapy, and it is expected to become a means to implement remote home rehabilitation.  
However, most studies still have the problem of insufficient interactivity, overemphasizing sensory stimulation, and ignoring the importance of interaction. Therefore, virtual reality exposure therapy (VRET) should apply active learning methods and increase interactive experiences [20]. Next, the analysis of patents regarding VR and counseling services are reviewed to describe the future trends of psychological therapy.
# Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses
## 3 Materials, Methods and Field Tests in a Greenhouse
### Communication Technology  
Given the large area of the greenhouses in PCH ranging from 500 m\({}^{2}\) to 1000 m\({}^{2}\), we wanted a mutually reinforcing LPWAN technology to balance out distance coverage with optimized low power usage. LoRaWAN [14] is a proprietary long-range wireless technology using Chirp Spread Spectrum (CSS) modulation technique named LoRa. In Europe, LoRa operates in the license-free 868 MHz band. Symbols are encoded in the form of chirps over a wider channel bandwidth. This technique curtails the inherit challenge of greenhouse that is reducing interference, multipath and fading effect. The spreading factor (SF) ranges from 7 to 12, providing a balance between data rate and range. Higher SF achieves longer distance with the data rate ranging from 0.3 kbps to 27 kbps. We have multiple channel support in LoRaWAN with a payload of a maximum size between 51 B and 222 B, depending on the SF. The duty cycle is not a primary concern while deploying LoRaWAN-based application in greenhouse [48]. The LoRaWAN module chipset, microcontroller, and LoRa-based configuration used for the deployment is given in Table 3.
## 4 Conclusions  
In this study, a FeS soft coating was prepared on the AISI 5140 steel by chemical bath deposition (CBD) coating technique at different temperatures from 30 to 90 \({}^{\circ}\)C in order to improve lubricity during friction. The influence of reaction temperature on microstructure, microcracks, and tribological properties was characterized and studied. The main conclusions are as follows:  
1. The optimum CBD temperature is between 50 and 70 \({}^{\circ}\)C. Because the coating is mainly composed of the soft self-lubricating active ingredients FexSy (FeS, FeS2, and Fe\({}_{1-\infty}\)S) in this temperature range. When the temperature is lower than this temperature range, the FexSy content/thickness in the coating does not reach a peak, and above this temperature range oxidation occurs in the coating.
2. The FeS coating has a chrysanthemum-shaped (CS) morphology composed of the FeS crystals. The CS particles nucleate at about 30 \({}^{\circ}\)C, rapidly grow and reach a peak thickness at about 50-70 \({}^{\circ}\)C, and finally disappear and are replaced by disordered thick petals at 90 \({}^{\circ}\)C. Additionally, in CBD treatment at high temperature (90 \({}^{\circ}\)C), the FeS coating peels off in a partial area because of the rapid evaporation of moisture from the chemical solution bath.
3. The wear resistance of the steel was improved obviously after CBD treatment, because of which the treated coatings can provide better lubrication and improve the wear resistance. The steel treated at a medium temperature (about 50 \({}^{\circ}\)C in this study) has a moderate coating thickness and a coating density, corresponding to the best wear resistance.  
Figure 7: Friction coefficient plotted as a function of sliding friction time of various samples.  
**Author Contributions:** Conceptualization, J.H. and H.X.; methodology, C.H. and H.L.; software, N.G.; validation, X.Y., and J.H.; formal analysis, J.H. and N.G.; investigation, J.H. and N.G.; resources, J.H. and H.X.; data curation, J.H. and N.G.; writing--original draft preparation, J.H., C.H., and N.G.; writing--review and editing, N.G.; supervision, H.X. and N.G.; project administration, J.H. and H.X.; funding acquisition, J.H., H.X. and N.G.  
**Funding:** This work was co-supported by the Natural Science Foundation of China (51575073 and 51501158). Scientific and Technological Research Program of Chongqing (cstc2017 cyjBX0031), the Graduate scientific research innovation project of Chongqing (CYS18308), and Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDG20162017). N.G. special acknowledges the Fundamental Research Funds for the Central Universities of China (No. XDJK2019B066).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Overvoltage of a Battery
### Problem of Low Cell Balancing Current
#### 2.1.1 In the Case of Having Cell Balancing and in the Case of Not Having It
\begin{split}\text{V}_{\text{CV}}=\text{V}_{\text{batt}}+\text{V}_{ \text{bat2}}+\text{V}_{\text{bat3}}+\text{V}_{\text{bat4}}\\ \text{V}_{\text{CV}}=(1/4\times\text{V}_{\text{CV}}+\text{I}_{ \text{CV}}\times\text{R}_{\text{batt}1}/\text{R}_{\text{total}}-1/4\times \text{I}_{\text{CV}}\times\text{R}_{\text{total}})+\\ (1/4\times\text{V}_{\text{CV}}+\text{I}_{\text{CV}}\times\text{R}_ {\text{bat2}}/\text{R}_{\text{total}}-1/4\times\text{I}_{\text{CV}}\times\text{ R}_{\text{total}})+\\ (1/4\times\text{V}_{\text{CV}}+\text{I}_{\text{CV}}\times\text{R}_ {\text{bat4}}/\text{R}_{\text{total}}-1/4\times\text{I}_{\text{CV}}\times\text{ R}_{\text{total}}),\\ \text{V}_{\text{batt1}}-1/4\times\text{V}_{\text{CV}}=(\text{I}_{ \text{CV}}\times\text{R}_{\text{batt1}}/\text{R}_{\text{total}}-1/4\times \text{I}_{\text{CV}}\times\text{R}_{\text{total}})\\ =\text{I}_{\text{CV}}\times(\text{R}_{\text{batt1}}-1/4\times \text{R}_{\text{total}}{}^{2})/\text{R}_{\text{total}},\\ \text{V}_{\text{bat2}}-1/4\times\text{V}_{\text{CV}}=\text{I}_{ \text{CV}}\times(\text{R}_{\text{bat2}}-1/4\times\text{R}_{\text{total}}{}^{2}) /\text{R}_{\text{total}},\\ \text{V}_{\text{bat3}}-1/4\times\text{V}_{\text{CV}}=\text{I}_{ \text{CV}}\times(\text{R}_{\text{bat3}}-1/4\times\text{R}_{\text{total}}{}^{2}) /\text{R}_{\text{total}},\\ \text{V}_{\text{batt4}}-1/4\times\text{V}_{\text{CV}}=\text{I}_{ \text{CV}}\times(\text{R}_{\text{batt4}}-1/4\times\text{R}_{\text{total}}{}^{2}) /\text{R}_{\text{total}}\\ \text{V}_{\text{CV}}\text{: charger's target charging voltage;}\\ \text{I}_{\text{CV}}\text{: changing current in the constant voltage region;}\\ \text{R}_{\text{total}}\text{: the total DCIR of series-connected batteries.}\end{split}  \tag{3}\]  
Table 1 summarizes the simulation results of Figure 3. Although our goal was to charge the four batteries to the same voltage, voltage distribution occurred due to the capacity variation among the batteries. At the end of charging the series-connected batteries, the highest voltage was observed in the relatively smaller Bat #3, and the lowest voltage was observed in the relatively larger Bat #2, with a voltage difference of 50 mV between the batteries. In the next section, the authors investigate the impact of different charging conditions on aging.
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 3 Proposed LSTM-Based Speech/Music Classification  
In this paper, an improved LSTM-based speech/music classification algorithm applicable to the framework of a speech/music classifier is proposed. LSTMs are sequence-based models of key importance for speech processing, natural language understanding, natural language generation, and many other areas. Because speech/music signals are highly correlated in time, LSTM is an appropriate method to classify speech/music. As shown in Figure 1, speech/music classification is performed using LSTM when it makes a decision as active speech in the EVS codec, compared with the conventional EVS codec and RNN-based algorithm. The feature vectors for speech/music classification are limited to 12 feature vectors used in the conventional EVS.  
Figure 1: (**a**) Block diagram of the conventional EVS speech/music classification. (**b**) Block diagram of the recurrent neural network based speech/music classification. (**c**) Block diagram of the proposed speech/music classification.  
The LSTM unit consists of an input activation function, a single memory cell, and three gates (input \(i_{t}\), forget \(f_{t}\), and output \(o_{t}\)), as shown in Figure 2. \(i_{t}\) permits the input signal to change or block the memory cell state. \(f_{t}\) controls what to remember and what to forget in the cell, and avoids vanishing gradients. Finally, \(o_{t}\) allows the memory cell state to have an influence on other neurons, or prevent this influence. With the addition of a memory cell, the LSTM can overcome the gradation problem of capturing and disappearing very complex and long-term dynamics. According to the LSTM unit, for an input \(x_{t}\), the LSTM calculates a hidden/control state \(h_{t}\), a block input \(g_{t}\), and a state of memory cell \(c_{t}\), which is an encoding of everything the cell has recognized until time \(t\):  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+b_{i}) \tag{6}\]  
\[f_{t}=\sigma\Big{(}W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}\Big{)} \tag{7}\]  
\[o_{t}=\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+b_{o}) \tag{8}\]  
\[g_{t}=\phi\Big{(}W_{xg}x_{t}+W_{hg}h_{t-1}+b_{g}\Big{)} \tag{9}\]  
\[c_{t}=f_{t}\bigodot c_{t-1}+i_{t}\bigodot g_{t} \tag{10}\]  
\[h_{t}=o_{t}\bigodot\phi(c_{t}) \tag{11}\]  
where \(W_{ij}\) are the weight matrices, \(\bigodot\) is the point-wise product with the gate value, \(b_{j}\) is the bias, \(\phi(x)\) is the activation function, and \(\sigma(x)\) is the logistic sigmoid. As shown in Figure 3, LSTM units are gathered together to form layers and are connected at each time step.  
Figure 3: Block diagram of LSTM layers.  
Figure 2: Detailed schematic of the LSTM unit, containing three gates, a block input, and a block output.
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 2 Related Work
[20] identified a relationship between these gradient-based attention maps and the activation maps generated by the encoder. Building on this insight, they proposed AMCons, which uses these activation maps to detect anomalous featureswhile incorporating an entropy loss to enforce an evenly distributed attention across all normal features. In turn, self-supervised methods generate synthetic abnormal samples by randomly adding noise [13; 16; 17; 22] or inserting image patches [21; 24; 32] into normal images, followed by either restoring the original image or segmenting the abnormal region. The denoising autoencoder (DAE) assumes that abnormalities can be simulated by adding noise to the query image, which is subsequently denoised using a U-Net architecture [16; 17]. Another approach for generating anomalies involves inserting image patches at random locations, as seen in Poisson image interpolation (PII) [24] and CutPaste [32]. Unlike CutPaste, PII employs Poisson interpolation to seamlessly blend patch edges into the target image. A representative example of a one-class semi-supervised AD method is the dual-distribution discrepancy for anomaly detection (DDAD) method [23]. This method trains an ensemble of AEs using only normal images, learning a normality distribution that allows reconstruction of normal structures. Simultaneously, a second ensemble of AEs is trained on unlabeled images, learning a broader distribution capable of reconstructing both normal and abnormal features. The intra- and inter-discrepancy between the reconstructions of these two AE ensembles is then leveraged to generate an anomaly map, which is further refined by a second-stage network trained through self-supervision [23]. Finally, DRA and BGAD are two examples of open-set supervised AD methods [27; 33]. DRA employs a multi-head architecture with four classification heads, where three are dedicated to detecting abnormality patterns--two using unsupervised techniques and one via supervised learning--while the fourth head, trained with supervision, assigns a normality score to the query image [27]. Conversely, BGAD extracts features using a pre-trained network and utilizes normalizing flows to model the normality distribution. In this method, abnormal samples and corresponding features are used to refine the normality distribution through a boundary optimization loss [33].
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 2 The Nonlinear PMSM Electromagnetic Model Based on Differential Inductance
### PMSM Equations in the ABC Frame
Therefore, the torque equation can be obtained from the \(\mathrm{d}\theta_{e}\) term:  
\[T_{e}=n_{p}\left(\mathbf{i}_{abc}^{T}\frac{\partial\Psi_{abc}}{\partial\theta _{e}}-\frac{\partial W_{m}}{\partial\theta_{e}}\right) \tag{10}\]  
The magnetic energy equation can be obtained by making the coefficient of the \(\mathbf{di}_{abc}\) term of (9) equal to zero:  
\[W_{m}(\mathbf{i}_{abc})\
abla^{T}=\mathbf{i}_{abc}^{T}\mathbf{L}_{abc} \tag{11}\]  
Under no-load conditions, the motor's armature contributes no magnetic field energy, with the system's entire magnetic field energy originating exclusively from the permanent magnets. When the motor is loaded, variations in magnetic field energy arise solely from current excitation. Consequently, the aforementioned energy relationship can be expressed in integral form, where the initial condition represents the permanent magnets' magnetic field energy:  
\[W_{m}|_{\theta_{e}}=\left(W_{m,\text{PM}}+\int_{0}^{i_{abc}}\mathbf{i}_{abc}^ {T}\mathbf{L}_{abc}\mathrm{d}\mathbf{i}_{abc}\right)|_{\theta_{e}} \tag{12}\]  
For further analysis, define the magnetic co-energy:  
\[W_{c}=\mathbf{i}_{abc}^{T}\Psi_{abc}-W_{m} \tag{13}\]  
Thus, the torque Equation (9) can be simplified as:  
\[T=n_{p}\frac{\partial W_{c}}{\partial\theta_{e}} \tag{14}\]  
\(W_{c}\) can be decomposed as follows:  
\[\left\{ \begin{array}{ll}W_{c\text{-AM}}=\mathbf{i}_{abc}^{T}\Psi_{abc\text{ -AM}}-W_{m\text{-AM}}\\ W_{c\text{-PM}}=\mathbf{i}_{abc}^{T}\Psi_{abc\text{-PM}}-W_{m\text{-PM}}\\ W_{c}=W_{c\text{-AM}}+W_{c\text{-PM}}\end{array} \right. \tag{15}\]  
Following the definitions and formulations of all motor physical quantities, the subsequent analysis delves into the motor's mathematical model from a field-theoretic perspective. Since the differential inductance \(\mathbf{L}_{abc}\) is the Jacobi matrix of the flux linkage \(\psi_{abc}\), and also \(\mathbf{L}_{abc}\) is a symmetric array, it follows that \(\psi_{abc}\) has zero curl in ABC frame:  
\[\mathbf{rot}(\psi_{abc})=\
abla\times\psi_{abc}=0 \tag{16}\]  
\(\psi_{abc}\) defines a curl-free field, then there must exist a potential function \(S\) for the field such that:  
\[\mathbf{grad}(S)=\
abla_{\mathbf{i}_{40}}S=\psi_{abc} \tag{17}\]  
This can be proved according to (2) (15):  
\[S=W_{c}|S(0)=-W_{m\_{\text{PM}}} \tag{18}\]  
The preceding analysis demonstrates that the magnetic co-energy \(W_{c}\) serves as a scalar potential function for the curl-free component of the flux linkage field under specified initial conditions. Since the permanent magnet flux linkage and initial rotor position are independent of stator current excitation and uniquely determine the integration constants, the total co-energy can be decoupled into a linear current-dependent term and a permanent magnet contribution. Consequently, Equation (18) is reformulated as follows:  
\[\
abla_{\mathbf{i}_{40}}W_{c\_{\text{AM}}}=\psi_{abc\_{\text{AM}}} \tag{19}\]
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 6 Non-Thermal Preservation Techniques
### Pulsed Electric Field  
Pulsed electric field (PEF) is a new non-thermal technology that has recently attracted meat scientists' and technologists' interest because it is capable of modifying the membrane's structure and increasing mass transfers through PEF [68]. Short electrical pulses at high voltages allow thermal effects to be regulated and kept low, distinguishing it from thermal electrical-based approaches such as ohmic heating and moderate electrical field [69]. The PEF technique interrupts biological cells in the food matrix without disrupting the product's attributes [70]. Electric field pulses of short duration (several nanoseconds to several milliseconds) with electric field strengths of 0.1-80 kV/cm are applied to food that is placed between or passed through two electrodes in PEF technology [71; 72]. PEF can modify various qualitative characteristics of meat, such as texture, color and water holding capacity, and improve processes of mass transfer such as curing and brining [70; 71; 73; 72]. PEF processing in meat technology focuses on meat protection, tenderization, super cooling, and rapid brining, among other things, because of its capacity for permeabilization of cell membranes [68]. The temporal flavor profiles of meaty and oxidized flavor attributes were found to be influenced by PEF treatment [74]. During PEF processing, temperature rises from 5 \({}^{\circ}\)C to 30 \({}^{\circ}\)C due to moderate ohmic heating have an impact on meat quality [68]. Browned, juicy, livery, and meaty flavor attributes were found in all PEF-treated samples. Some of the terms associated with \"positive\" attributes include meaty, roast beef, juicy, browned, fatty, and salty. PEF-processed meat at 1.1-2.8 kV cm\({}^{-1}\) showed no significant improvement in the tenderness of meat during storage [75]. PEF treatment results in a significant increase in beef tenderness when treated at 5-10 kV [76]. Beef treated at 5-10 kV at different frequencies 20, 50, and 90 Hz showed up to a 19% reduction in shear force [77].  
In a recent research database, PEF employed for bacterial growth prevention with a 280 mHz frequency of pulse electric field strength with 300 V/m applied, which resultedin the inactivation of the bacteria _Yersinia Enterocolitica_ present in beef and prolongs the storage period to 30 days at \(-20\)\({}^{\circ}\)C [75].