esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Validation of a Capacitor-Current Circuit Model for Evaporation-Induced Electricity
## 2 Development of the Simplified Capacitor-Current Circuit Model
### Development of the Simplified Capacitor-Current Model  
As we discussed above, the full model provides valuable information about the electrokinetic phenomena while bringing extreme complexities that makes its direct application in device design challenging. Thus, to simplify this model, we start by making a few assumptions. First, we assume the system operates under steady-state conditions, which allows us to reduce the need for complex Laplace-domain transformation. Second, the multi-channel structure is approximated as a single channel, which still captures the essential electrical behavior without introducing trivial intricacies. Third, the ion transport and charge storage are represented using a single equation that combines the effects of resistive and capacitive elements.  
Hence, the resulting capacitor hybrid model can be shown as:  
\[I(t)=C\times\frac{\mathrm{d}V(t)}{\mathrm{d}t}+\frac{V(t)}{R} \tag{4}\]  
where \(C\) is the capacitance representing charge buildup in the microchannels, \(R\) is the resistance accounting for ion transport and evaporation loss, and \(V(t)\) is the voltage across the capacitor at time \(t\). In this model, the input is the applied or generated voltage across the system, \(V(t)\), and the output is the resulting current, \(I(t)\). The model aims to predict the time-dependent behavior of \(I(t)\) in response to \(V(t)\), which reflects the system's physical and electrical properties, including its transient and steady-state performance.  
The capacitive component describes how ions accumulate over time, while the resistive component models the leakage effects, such as ion diffusion and evaporation. By combining these two aspects, this model simplifies the underlying physics while still capturing the electrical characteristics for practical applications.
## 4 Results and Discussion
### Discussion
#### 4.2.2 Collapse  
The potential collapse in area 22 is used as an example of the analysis.  
The potential collapse is located on the right bank of the Minjiang River, with geographic coordinates of \(103^{\circ}40^{\prime}40.884^{\prime\prime}\) E, \(32^{\circ}01^{\prime}11.113^{\prime\prime}\) N, and a slope of about \(40^{\circ}\). The height of the slope is about 175 m, and the elevations at the front and rear edges are 2032 m and 2207 m, respectively. As shown in Figure 9a, the coherence points are concentrated in the middle of the slope. The maximum deformation rate of the slope is \(-43.94\) mm/y, and the deformation point is seriously missing [59]. The Google earth image shows that the rock and soil mass at the trailing edge is exposed, which is attributed to the collapse of the upper dangerous rock mass.  
The field investigation indicated that the slope was about 233 m in length and 230 m in width (Figure 9b) with an area of about 53,600 m\({}^{2}\) and a volume of about 100,000 m\({}^{3}\). The area is a medium-sized sliding collapse [60]. The slope consists of three parts: the lower slope covered by soil, the middle slope consisting of broken bedrock, and the upper slope, which is a cliff. The slope of the steep cliff is between \(30^{\circ}\) and \(40^{\circ}\), and the area extends along the right bank of the Minjiang River, with exposed bedrock exhibiting strong weathering and erosion. The lithology is metamorphic sandstone with slate, and the attitude of the rock formation is \(187^{\circ}/63^{\circ}\) (dip angle). The middle part of the slope is the collapsed dangerous rock mass, which is broken and has develop  
Figure 9: (**a**) Velocity map of the collapse. (**b–d**) Field photos of the potential collapse. (**b**) The range of the collapse. (**c,d**) A dangerous rock mass on the slope. Note—the red dotted line represents the boundary of the collapse, and the orange circle represents the dangerous rock mass.  
a width of about 5-10 cm. The collapsed body is steep, with an angle of about 60\({}^{\circ}\), and it protrudes from the slope as shown in Figure 9c,d. The material of the lower slope is mainly rubble intercalated with clay sand with a high argillaceous content. The rubble has different particle sizes, and the larger ones have diameters of several meters. Due to the development of slope cracks, the rock mass is divided into multiple blocks, forming many dangerous rock masses. The largest block is 5 m \(\times\) 4 m \(\times\) 3 m. The collapsed area will increase in depth due to rainfall and earthquakes, resulting in a collapse disaster. Therefore, the slope has a high probability of collapse. Since the slope is near a highway with a restaurant beside the road, the safety of people and property will be threatened if a collapse occurs [61].
## 1 Introduction
Recent engineering research has proposed a polarity dialog system model based on neural network-based machine learning [18], as well as several methods for predicting and classifying politeness in conversation using transformer-based large language models [26; 27; 28; 29; 30]. Additionally, previous works have suggested that ChatGPT may be able to take advantage of the dialog strategies defined in the politeness theory [31], increasing our knowledge of how to use politeness in human-machine interactions [32]. However, even after numerous studies on politeness in dialog systems from both psychological and engineering viewpoints, the impact of speech level shifting as a fundamental strategy using linguistic considerations to adjust the psychological distance from the user during interactions with the dialog system remains uncertain. This study examined the effects of a dynamic speech level shift by a text chat dialog system on dialog with users in Japanese. We developed a non-task-oriented text dialog system that dynamically controls the formal and informal speech levels in Japanese dialog and verified its effectiveness through psychological experiments. We adopted the formal speech level ratio in the utterances of the user (rate of using the formal style) as a trigger for a speech level shift. Furthermore, with reference to politeness theory [21], which is a framework of linguistic considerations in human dialog, we designed dialog situationsthat take into account information regarding the speaker. Three experimental conditions were set: a shift in the speech level and the application of formal and informal styles. We performed analyses on these three conditions based on a subjective evaluation by the users and the dialog logs between the user and the dialog system. This study provides valuable insights into the design of dialog systems by elucidating the effects of dynamic speech level shifts. These shifts are crucial for conveying politeness in linguistic communication, i.e., the dialog between dialog systems and humans. It is important to note that the focus of this research is to investigate the effects of dynamic speech level shifts in dialog systems on user interaction through psychological experiments. The study does not claim to have developed novel engineering methods or evaluated their usefulness. The remainder of this paper is organized as follows: Section 2 outlines related studies. The non-task-oriented dialog system for controlling the speech level is described in Section 3. Thereafter, the evaluation experiments and their results are explained in detail. The overall results of the evaluation experiments are discussed in Section 5. Finally, a summary of this research is presented in Section 6.
## 1 Introduction
### Image Forgery Detection Techniques  
Image forgery detection techniques are classified into three types based on the integrity authentication method used. Some techniques use watermarks, signatures, and passivity [5; 6; 7; 8; 9; 10]. The watermark-based technique distributes watermarked images by inserting invisible noise-like bits (watermarks) that are easily broken by correction to the original image. Authentication is performed by detecting and checking the watermarks present in the image [11; 12; 13; 14; 15; 16; 17; 18].  
The signature-based technique involves extracting a feature set from an original image, which is significantly smaller in size. This feature set is saved as a file and is identified by a unique signature for each image. To authenticate an image, the signature extracted from the forged image is compared with the signature extracted from the original image. The primary advantage of signature-based techniques over watermark-based techniques is that they do not require any modification to the original image. However, while most watermark-based methods can detect modified regions in forged images, signature-based techniques may not have this ability [19; 20; 21; 22; 23].  
Passive techniques have the advantage of detecting images without any signatures or watermarks during the detection process and are based on the statistical consistency of natural landscape images such as lighting, lens radius distortion, color aberration, sensor pattern noise, dust pattern, camera response function, projective geometry, color filter arrangement, pixel interaction, and compression distortion [5; 6; 24; 25]. However, the passive technique's detection accuracy is significantly lower than that of watermark-based and signature-based techniques. Passive techniques consistently produce false positive and false negative decisions [5].
## 2 Proposed Localization System  
The architecture of the proposed AdaBoost-based localization scheme is illustrated in Figure 1 and consists of two phases: the offline phase and the online phase. During the offline phase, RSS values are measured from the AP group, and the collected raw data are stored in a database. In the online phase, the system accesses the fingerprint data from the offline database, filters the raw data to remove noise, and selects qualified APs. The processed data are then used to train the AdaBoost model. When a localization request is made, the measured RSS values are fed into the trained model for positioning.  
The procedures of the proposed AdaBoost-based localization scheme are described in the following subsections.
# An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks
## 2 The Optimization Problem and the Momentum Method
### The Optimization Problem  
In order to satisfy \(y_{h,j}=y_{j}\), we constructed the cost function as:  
\[C(\mathbf{w})=\frac{1}{l}\sum_{j=1}^{l}\Bigl{(}y_{j}-y_{h,j}\Bigr{)}^{2}, \tag{1}\]  
where \(l\) is an integer (number of learning data), and we solved the following the optimization problem:  
\[argmin_{\mathbf{w}}\{C(\mathbf{w})\}. \tag{2}\]  
To solve the optimization problem (2), gradient-based methods are generally used [1; 3]. Gradient-based methods are used to find parameter \(\mathbf{w}\), such that the gradient of the cost function is zero. However, the cost function \(C(\mathbf{w})\) is not convex in general. If the cost function \(C(\mathbf{w})\) is not convex, the idea of zeroing the gradient may not work well. Therefore, in order to increase the efficiency of learning, we applied the Lagrange multiplier method to define \(F(\mathbf{w})\) as follows, and to find the parameter \(\mathbf{w}\) that makes \(F(\mathbf{w})\) zero.  
\[F(\mathbf{w}):=\frac{\partial C(\mathbf{w})}{\partial\mathbf{w}}+\mu C( \mathbf{w}), \tag{3}\]  
where \(\mu\) is a small constant. In order to obtain \(\mathbf{w}\) that satisfies Equation (3) by resulting in zero, we used an iterative parameter change rule:  
\[\mathbf{w}_{i+1}=\mathbf{w}_{i}-\lambda F(\mathbf{w}_{i}), \tag{4}\]where \(\lambda\) is a constant and is called the learning rate. Equation (4) is similar to the momentum method.
## 2 Study Area  
This study was conducted for an area of approximately 3,800 km\({}^{2}\) on the northern Seward Peninsula, Alaska (66.5\({}^{\circ}\)N, 164.5\({}^{\circ}\)W; Figure 1). The study area is situated in an ice-rich permafrost region and is largely covered by thermokarst-affected terrain with a large number of thermokarst lakes and DTLBs and erosional remnants of uplands. The northern Seward Peninsula is identified as one of the major lake districts of Alaska, where 7% of the 6,418 km\({}^{2}\) coastal lowland area is covered with lakes larger than 1 hectare [44] (Figure 1). Thermokarst lakes in the lowland plain of the region mainly formed as a result of permafrost degradation due to warming throughout the Holocene [8, 45]. Currently, the mean annual ground temperature is recorded as \(\sim\)-3 \({}^{\circ}\)C [46]. This allows for current formation of permafrost and epigenetic ice wedges following drainage of thermokarst lakes in the region [47]. The depth of permafrost exceeds 90 m [47] and the active layer is 0.3-0.6 m thick [25]. The permafrost thickness very likely varies due to the distribution and thermal impact of thermokarst lakes [46, 48]. However, detailed data on permafrost distribution and thickness is not available for the Seward Peninsula.  
The elevation in the study area ranges from close to sea level near the coast, to less than 50 m in most of the uplands that consist of erosional remnants of ice-rich deposits. The landscape is dominated by tundra-type vegetation, which is classified as Bering Tundra [44]. Recently drained thermokarst  
Figure 1: Digital Elevation Model (DEM) base map of the Seward Peninsula showing the location of lakes (\(>\)1 ha) mapped in National Hydrography Dataset (NHD) and DTLBs that drained (\(>\)90% of surface area loss) 0–50 years ago (based on times series analysis of aerial and satellite images from 1950 to 2010 [43]) and 50–10,000 years BP (based on radiocarbon dating [10]). The footprints of six TerraSAR-X scenes used in analysis are outlined in blue with their respective acquisition dates.  
lake basins are dominated by productive grasses, such as _Calamagrostis canadensis_ and _Duponia fisherii_[10]. The sedge _Carex aquatilis_ is also common in some of the very productive younger basins. Older DTLBs have a higher component of a less productive plant community that includes _Carex bigelowii, Eriophorum angustifolium_ and _Sphagnum sp._ tundra with _Betula nana_, _Salix sp._ and prostrate ericaceous shrubs [10]. Dry surfaces in the older basins, due to heave from reformation of ground ice and the presence of mature ice wedge polygon ridges, may show abundant lichen [10].  
Climate in this region is characterized by long cold winters and short growing seasons (May-September) [49]. The mean annual air temperature of the region is -6.1 \({}^{\circ}\)C, and the mean annual precipitation is 255 mm, with roughly 125 mm falling in the form of snow based on historical summaries for the period 1971-2000 recorded at Kotzebue, Alaska [27], which is located 60 km to the northeast, but has a similar coastal position as the northern Seward Peninsula [25].
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 2 Titanium Dioxide
### Hydrogen Production Exploiting TiO\({}_{2}\) under Visible and Solar Light
#### 2.1.3 Coupling TiO\({}_{2}\) with MoS\({}_{2}\)  
The efficiency of photocatalytic hydrogen production can also be improved by exploiting MoS\({}_{2}\) as a co-catalyst for TiO\({}_{2}\)[48]. Several strategies have been developed to improve the performance of MoS\({}_{2}\)/TiO\({}_{2}\) photocatalysts under visible light. [PERSON] et al. [49] introduced a composite photocatalyst system based on TiO\({}_{2}\) nanosheets and MoS\({}_{2}\), in which the visible light absorption of TiO\({}_{2}\)/MoS\({}_{2}\) was expanded by incorporating CuInS\({}_{2}\) quantum dots as a light-harvesting inorganic dye. The ternary CuInS\({}_{2}\)/TiO\({}_{2}\)/MoS\({}_{2}\) photocatalyst with 0.6 mmol g\({}^{-1}\) of CuInS\({}_{2}\) and 0.5 wt% of MoS\({}_{2}\) exhibited a H\({}_{2}\) evolution rate of 1034 \(\upmu\)mol h\({}^{-1}\) g\({}^{-1}\) under visible light in the presence of Na\({}_{2}\)S and Na\({}_{2}\)SO\({}_{3}\) as sacrificial reagents. A possible mechanism for H\({}_{2}\) production is reported in the work of [PERSON] et al. [49] and is schematically represented in Figure 4. A Zn(II)-5,10,15,20-tetrakis(4-carboxyphenyl)-porphyrin (ZnTCPP)-sensitized MoS\({}_{2}\)/TiO\({}_{2}\) photocatalyst was synthesized by [PERSON] et al. [10], exploiting the ZnTCPP to improve the absorption in visible light and the MoS\({}_{2}\) to enhance the photocatalytic activity. The ZnTCPP-MoS\({}_{2}\)/TiO\({}_{2}\) photocatalyst was prepared by synthesizing MoS\({}_{2}\)/TiO\({}_{2}\) composites, and then adsorbing the ZnTCPP dye on the TiO\({}_{2}\) surface. TiO\({}_{2}\) P25 was used. A H\({}_{2}\) production rate of 10.2 \(\upmu\)mol h\({}^{-1}\), under visible light, was achieved using the photocatalyst loaded with 1.00 wt% of MoS\({}_{2}\). TEOA was used as the sacrificial electron donor in the photocatalytic hydrogen evolution experiments. [PERSON] et al. [50] synthesized MoS\({}_{2}\) nanosheet-porous TiO\({}_{2}\) nanowire hybrid nanostructures using the hydrothermal method. A H\({}_{2}\) evolution rate of 16.7 mmol h\({}^{-1}\) g\({}^{-1}\) was achieved under visible light in the presence of Eosin Y dye in TEOA aqueous solution. Flower-like MoS\({}_{2}\)@TiO\({}_{2}\) catalysts were synthesized by [PERSON] et al. [51] using a hydrothermal method, using a metal-organic framework (MOF) as a precursor. The MoS\({}_{2}\)@TiO\({}_{2}\) photocatalysts, with a loading of 14.6 wt% of MoS\({}_{2}\), allowed a H\({}_{2}\) production rate of 10,046 \(\upmu\)mol h\({}^{-1}\) g\({}^{-1}\) under visible light to be achieved in the presence of fluorescein as photosensitizer in an aqueous acetone solution and using TEOA as a sacrificial donor.  
Figure 3: Schematic representation of a possible mechanism for H\({}_{2}\) production using the CdS/WO\({}_{3}\)/TiO\({}_{2}\) photocatalyst under visible light.
# Bloch Oscillations in the Chains of Artificial Atoms Dressed with Photons
## Appendix B Derivation of Formula for Tunneling Current  
For calculating the tunneling current we introduce the operator of the particle number in the \(j\)-th atom \(\hat{N}_{j}=\big{|}a_{j}\big{\rangle}\big{\langle}a_{j}\big{|}+\big{|}b_{j} \big{\rangle}\big{\langle}b_{j}\big{|}\) and formulate the equation of continuity  
\[\hat{J}_{j}-\hat{J}_{j-1}=-e\frac{d\hat{N}_{j}}{dt}, \tag{100}\]  
where \(\hat{J}_{j}\) is the current density operator in the \(j\)-th atom and the left-hand part in (100) is a discrete analog of divergence in 1D-case. These operators act only to the electronic states; therefore all relations of this Appendix are given in terms of Wannier states. Using Heisenberg equation for the operator \(\hat{N}_{j}\), we rewrite (100) in the form  
\[\hat{J}_{j}-\hat{J}_{j-1}=-i\frac{e}{\hbar}\big{[}\hat{H},\hat{N}_{j}\big{]}. \tag{101}\]  
The tunneling currents over excited and background energy levels are independent, thus \(\hat{J}_{\text{Tunneling},j}=\hat{J}_{\text{Tunneling},j}^{(a)}+\hat{J}_{ \text{Tunneling},j}^{(b)}\). Using (101) we obtain  
\[\hat{J}_{\text{Tunneling},j}^{(a,b)}-\hat{J}_{\text{Tunneling},j-1}^{(a,b)}=- i\frac{e}{\hbar}\big{[}\hat{H}_{\text{Tunneling}}^{(a,b)}\hat{N}_{j}\big{]}, \tag{102}\]  
where  
\[\hat{H}_{\text{Tunneling}}^{(a)}=\sum_{j}(t_{a}\big{|}a_{j}\big{\rangle} \big{\langle}a_{j+1}\big{|}+t_{a}^{*}\big{|}a_{j}\big{\rangle}\big{\langle}a_ {j-1}\big{|}) \tag{103}\]  
is a component of the Hamiltonian (2), chargeable for the tunneling at the excited level (the similar equation may be written for the Hamiltonian \(\hat{H}_{\text{Tunneling}}^{(b)}\)). Using (103), we calculate the commutator in the right-hand part of (102) and obtain  
\[\hat{J}_{\text{Tunneling},j}^{(a)}-\hat{J}_{\text{Tunneling},j-1}^{(a)}=-iet_ {a}\big{(}\big{|}a_{j-1}\big{\rangle}+\big{|}a_{j+1}\big{)}\big{\rangle} \big{\langle}a_{j}\big{|}+H.c. \tag{104}\]  
It corresponds to the operator of the tunneling current at the excited level  
\[\hat{J}_{\text{Tunneling},j}^{(a)}=-iet_{a}\big{|}a_{j}\big{\rangle}\big{ \langle}a_{j+1}\big{|}+H.c. \tag{105}\]  
The observable value of the tunneling current is  
\[\hat{J}_{\text{Tunneling},j}^{(a)}(t)=\Big{\langle}\hat{J}_{\text{Tunneling},j}^{(a)} \Big{\rangle}=-iet_{a}a_{j}^{*}(t)a_{j-1}(t)+c.c. \tag{106}\]  
Using the approximation \(a_{j-1}(t)-a_{j}(t)\approx\big{(}a_{j+1}(t)-a_{j-1}(t)\big{)}/2\), and adding the similar support of the ground level, we obtain Relation (10). This relation corresponds to the well-known definition of the probability flow \(\mathbf{j}=i\hbar(\Psi\text{grad}\Psi^{*}-c.c.)/2m\) in 3D continuous case [70].
# Earthquake Detection Using Stacked Normalized Recurrent Neural Network (SNRNN)
## 3 Stacked Normalized Recurrent Neural Network (SNRNN) Architecture
### Layer 4: Long Short-Term Memory Model (LSTM)  
The long short-term memory (LSTM) model is the next layer of the proposed model. The LSTM is built up of many individual cells or units [16]. The hidden layer has many cells. The three gates of an LSTM cell are forgotten, input, and output. The forget gate keeps the cell state in memory, while the input and output gates handle incoming and outgoing  
Figure 3: The framework of our proposed model’s inputs and outputs.  
data [30]. By utilizing these gates, LSTM can fix the issue of vanishing gradients. The following equations show the list of operations on the input sequence for the LSMT model:  
\[i_{t}=\sigma(x_{t}U_{i}+h_{t-1}\ W_{i}+b_{i}\ ) \tag{3}\]  
\[f_{t}=\sigma(x_{t}U_{f}+h_{t-1}W_{f}+b_{f}) \tag{4}\]  
\[o_{t}=\sigma(x_{t}U_{o}+h_{t-1}W_{o}+b_{o}) \tag{5}\]  
\[q_{t}=tanh(x_{t}U_{q}+h_{t-1}W_{q}+b_{q}\ ) \tag{6}\]  
\[p_{t}=f_{t}*p_{t-1}+i_{t}*q_{t} \tag{7}\]  
\[h_{t}=o_{t}*tanh(p_{t}) \tag{8}\]  
\[r_{t}=\sigma(W^{(r)}x_{t}+U^{(r)}h_{t-1}) \tag{9}\]  
\(i_{t}\) is the output of the input gate, \(o_{t}\) is the result of the output gate, and \(f_{t}\) is the output of the forget gate. \(i_{t}\), \(o_{t}\), and \(f_{t}\) are activated by the sigmoid function \(\sigma\). These three results are the ensemble of input \(x_{t}\) and the preceding hidden state \(h_{t-1}\) and the biased value \(b\). A \(tanh\) nonlinearity function is applied over the input \(x_{t}\) and the previous hidden state \(h_{t-1}\) to generate a temporary result \(q_{t}\). The current step \(t\) is then used to calculate the hidden step. Then, to obtain an updated version of the history \(p_{t}\), \(q_{t}\) is combined with history \(p_{t-1}\) using the input gate \(i_{t}\) and forgot gate \(f_{t}\) in that order. In the end, the output gate \(o_{t}\) will make use of the updated history \(p_{t}\) to determine the final hidden state, which will serve as the output to the softmax layer, producing the outcome.