esa-sceva/satcom-chunk-collection huggingface dataset:
# Corrosion and Scaling in Geothermal Heat Exchangers
## 2 Heat Exchangers in Geothermal Power Plants
### Types of Heat Exchangers  
The most common heat exchanger design used in geothermal power plants include:  
* Shell and Tube Heat Exchangers: Shell and tube heat exchangers are frequently used in geothermal power plants, especially in binary cycle and hybrid systems. They are known for their versatility and effectiveness in handling high-temperature and high-pressure geothermal fluids. In binary cycle power plants, these heat exchangers are used to transfer heat from the geothermal fluid to the secondary working fluid (e.g., isobutane or isopentane) to drive a turbine.
* Plate Heat Exchangers: Plate heat exchangers offer compact designs and efficient heat transfer capabilities, making them suitable for transferring heat between the geothermal fluid and the secondary working fluid.
* Ground Heat Exchangers: They are used in geothermal heat pumps. The heat exchanger is in direct contact with the ground and allows heat transfer with the thermal fluid cooling/heating the building. This type of heat exchanger is mostly used for domestic applications and will not be covered here.  
The choice of heat exchanger type in a geothermal power plant depends on factors like the temperature and pressure of the geothermal fluid, the characteristics of the secondary working fluid in binary systems, and the overall plant design.
###### Abstract  
Lithium niobate (LiNbO\({}_{3}\)), a multifunctional crystalline material, has critical importance in advancing holographic storage systems. However, persistent challenges such as optical damage, limited diffraction efficiency, and slow response kinetics hinder its practical implementation. This work systematically examines the correlation between the Zn\({}^{2+}\) dopant concentration and the defect architecture, photodamage resistance, and holographic storage properties of Zn:Cu:Fe:LiNbO\({}_{3}\) crystals, employing advanced characterization techniques to elucidate structure-property relationships and optimize performance metrics. The experimental data reveal a pronounced Zn\({}^{2+}\) doping concentration dependence in both photodamage resistance and holographic storage capabilities. Notably, Zn:Cu:Fe:LiNbO\({}_{3}\) crystals doped with 7 mol% Zn\({}^{2+}\) achieve a substantial 416-fold improvement in photodamage resistance (786.55 J/cm\({}^{2}\)) relative to the 1 mol% doped variant. Concurrently, these optimally doped crystals demonstrate superior holographic storage performance, characterized by a response time of 196.4 s, a dynamic range of 9.81, a diffraction efficiency of 66.7%, and a sensitivity of 1.04. The observed performance enhancement is fundamentally attributed to Zn\({}^{2+}\) doping, which concomitantly suppresses intrinsic defect formation and tailors the spatial distribution of Fe\({}^{3+}\)/Cu\({}^{2+}\) photorefractive centers within the crystal lattice. These mechanistic insights establish critical guidelines for the rational design of next-generation holographic storage materials with optimized photorefractive response and defect engineering capabilities.  
Keywords:three-doped; IR transmission spectrum; defect structure; exposure energy; nonvolatile holographic storage +
Footnote †: journal: Applied Sciences
## 3 Map Projection
### Rectilinear Projection  
Equirectangular projected images are not ideal for viewing applications. The image must be re-projected to provide viewers with an approximate natural perspective. The rectilinear projection maps a specific portion of locations that are on the sphere onto a flat image, a process that is referred to as gnomonic projection. This is a fundamental projection strategy for panoramic imaging approaches. Figure 3a shows the basic theory for rectilinear projection and Figure 3b-d demonstrate three types of projected results.  
Figure 3: Basic theory of rectilinear projection and three types of projected results. (**a**) Basic theory for rectilinear projection; (**b**) Polar rectilinear projection; (**c**) Equatorial rectilinear projection; (**d**) Oblique rectilinear projection.  
Figure 2: [PERSON]’s indicatrices and the projected equirectangular results. (**a**) Tissot’s indicatrices; (**b**) Equirectangular projected image.  
The rectilinear projected image is composed of the points on the plane of projection. It is obtained when the rays from the center of the sphere pass through the points on the surface and cast the points onto the plane. Less than half of the sphere can be projected onto the plane. When the points on the projection plane are located further away from the source of ray, there will be more distortions. Figure 3b shows the projected result of a pole. The opposite hemisphere cannot be projected onto the plane. The distortions are increased away from the pole. Figure 3c shows an equatorial rectilinear projection. Only the points that are within \(90\) and \(-90\) degrees of the central meridian (\(0\) degrees) can be projected, and the poles cannot be shown. The distortions will be increased away from the central meridian and the Equator. Figure 3d shows an oblique rectilinear projection. If the central parallel is at a northern latitude, its colatitude is projected as a parabolic arc. The parallels in the southern regions are hyperbolas and those in the northern regions are ellipses, which become more concave toward the nearest pole and vice versa if the central parallel is at a southern latitude.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Values** & **SIE\({}_{7.5\%}\)** & **SIE\({}_{10\%}\)** & **\(t\)-Test** & **ES** \\ \hline Wtot (k) & 7.0 (1.2) & 7.5 (0.6) & \(-\)1.4 & 0.5 \\ Wtot\({}_{\rm IS}\) (kI) & 7.0 (1.2) & 7.7 (0.7) & \(-\)1.7 & 0.7 \\ Wtot\({}_{\rm IS}\) (kJ) & 6.9 (1.3) & 7.2 (0.6) & \(-\)0.9 & 0.3 \\ FI\({}_{\rm IS}\) (\%) & 7.3 (2.5) & 10.6 * (2.6) & \(-\)3.4 & 1.3 \\ FI\({}_{\rm IIB}\) (\%) & 8.7 (3.1) & 10.3 (5.0) & \(-\)1.1 & 0.4 \\ tPPO\({}_{\rm IS}\) (s) & 3.4 (0.6) & 3.5 (0.5) & \(-\)0.5 & 0.2 \\ tPPO\({}_{\rm IS}\) (s) & 3.9 (0.6) & 3.7 * (0.5) & 0.7 & \(-\)0.4 \\ Rpm (repetition\(\cdot\)min\({}^{-1}\)) & 145.3 (5.5) & 147.2 (7.4) & \(-\)0.6 & 0.3 \\ \hline \hline \end{tabular}  
* Wtot—average of the sum of total work in SIE series I and II; Wtot\({}_{\rm IS}\)—average of the sum of total work in SIE series I; Wtot\({}_{\rm IS}\)—average of the sum of work in SIE series II; FI\({}_{\rm IS}\)—fatigue index in SIE series I; FI\({}_{\rm IS}\)—fatigue index in SIE series I; tPPO\({}_{\rm IS}\)—the fastest time to achieve PPO in the last series; tPPO—the fastest time to achieve PPO in the 2 nd series; Rpm—the highest repetition per minute
* statistically significant difference (\(p\) < 0.05) between SIE\({}_{7.5\%}\) and SIE\({}_{10\%}\).  
\end{table} 
Table 3: Physical parameters obtained during repeated sprints in the SIE\({}_{7.5\%}\) and SIE\({}_{10\%}\) condition.
## 3 Results and Discussion
### Analysis of Seismic Principle  
Another important reason for the favorable seismic performance of ancient Chinese buildings is the application of a Dougong bracket, as shown in Figure 14. As a special combination of tenon-mortise joints, the Dougong bracket supports the roof as it stands on columns and undergoes compressional deformation under the action of the vertical seismic load, while the components shift against each other under the action of the transverse seismic load. When an earthquake occurs, there is both friction and rotation that absorbs a large amount of energy in a manner equivalent to that of a car's shock absorber. Although the Dougong bracket becomes loose due to compression and shifting, its structural components do not separate. Acting together, multiple Dougong brackets connect all structures within the entire building to create a \"rigid body\" and pass the force to columns capable of offsetting the seismic impact in accordance with the principle of \"the abler shoulders bear more labour\", which is the key to the anti-earthquake nature of the structure. Structures similar to mortise joints have a good ability to withstand earthquake action [25]. Tenon-mortise joints have strong seismic capacity and can respond to earthquakes rapidly [26].  
The special section of the column also has an impact on the seismic performance of the wooden structure building, as shown in Figure 15. In Figure 15, the displacement peak value of the point is 3.69 mm [16]. When a column is set deep into its foundation, it is easy for it to break during a strong earthquake, especially in the case of the wood material of the columns in ancient Chinese buildings, which are more fragile than concrete [27]. The columns of the structures in the Forbidden City are not deeply embedded underground,  
Figure 14: Anti-shock diagram of a Dougong bracket in an earthquake.  
Figure 13: Flexibility of the wooden structure.  
which allows them a certain degree of movement and can thus prevent structural collapse due to broken columns. In addition, the heavy roof acts as a counterweight, pushing down the substructure, so the flexible parts can move with the quake [28; 29].
## 3 Problem Formulation
### Mathematical Modeling
#### 3.1.1 Explanation of the Objective Function  
Objective function (IP) is actually the summation of average number of packets on each link, _i.e._, the queue length, which is obtained by the product of link mean delay and aggregate flow on the link. The expression of the form \(D_{i}(g_{i})\) is a monotonically increasing and convex function with respect to \(g_{i}\), where \(g_{i}\) is the aggregate flow on link \(l\) measured in packets per second [15, 21, 22, 23].  
Through Little's Law (_i.e._, \(N=\lambda T\)), the objective function is proportional to the average cross-network packet delay. Thus, for a given traffic input (_i.e._, \(\sum\limits_{w\in W}\gamma_{w}\) ), minimizing the average number of packets in the network is equivalent to minimizing the average cross-network packet delay [24, 25].
# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 2 Materials and Methods
### Loss Function  
As with YOLOv3, the loss function of proposed method is shown in the following equalization:  
\[loss=Error_{boxes}-Error_{confidence}-Error_{classes} \tag{1}\]  
\(Error_{boxes}\) is the coordinate regression of the bounding box, representing the location and coordinates of the bounding boxes, which is defined as follows:  
\[Error_{boxes}=\lambda_{coord}\sum_{i=0}^{S^{2}}\sum_{j=0}^{K}l_{ij}^{obj}[(t _{x}-t_{x}^{\prime})^{2}+(t_{y}-t_{y}^{\prime})^{2}+(t_{w}-t_{w}^{\prime})^{ 2}+(t_{h}-t_{h}^{\prime})^{2}] \tag{2}\]  
where \(S^{2}\) is the number of cells, \(K\) is the number of bounding boxes predicted by each grid cell, \(t_{x}\), \(t_{y}\), \(t_{w}\), \(t_{h}\) represent coordinates, width, and height of the bounding boxes. \(\lambda_{coord}\) is the weight of the coordinate error. If there is a target in the \(i\)-th cell, the \(t\)-th bounding box of that cell is responsible for predicting the target, and if there is a corresponding ground truth, then \(l_{ij}^{obj}=1\), otherwise, \(l_{ij}^{obj}=0\).  
\(Error_{confidence}\) concludes the loss of confidence in the bounding box of the existing object and the objects that do not exist, which is defined as follows:  
\[Error_{confidence}=\sum_{i=0}^{S^{2}}\sum_{j=0}^{K}l_{ij}^{obj}[c_{i}^{\prime} \log(c_{i})+(1-c_{i}^{\prime})\log(1-c_{i})]+\lambda_{noobj}\sum_{i=0}^{S^{2} }\sum_{j=0}^{K}l_{ij}^{noobj}[c_{i}^{\prime}\log(c_{i})+(1-c_{i}^{\prime}) \log(1-c_{i})] \tag{3}\]  
In (3), \(c\) is the number of classes, and \(\lambda_{noobj}\) is the weight of the confidence error.  
\(Error_{classes}\) is the classification loss of the grid cell in which the object exists and it is defined as follows:  
\[Error_{classes}=\sum_{i=0}^{S^{2}}l_{i}^{obj}\sum_{c\in classes}[p_{i}^{ \prime}(c)\log(p_{i}(c))+(1-p_{i}^{\prime}(c))\log(1-p_{i}(c))] \tag{4}\]  
In (4), \(p\) is the probability, and cross-entropy is introduced to the loss function.
## 4 Experimental Analysis  
In this phase, we evaluated the performance of the original log files and those processed with the algorithms described in the previous section. To this end, we used the BERT model (Bidirectional Encoder Representations from Transformers) [29]. Bidirectional Encoder Representations from Transformers (BERT) is based on a deep bidirectional architecture to understand the context of a word based on its surrounding words, leading to superior performance in various NLP tasks. This model pre-trains on vast amounts of text data to capture linguistic nuances, which are then fine-tuned for specific applications. BERT, with its advanced contextual understanding and ability to process large volumes of textual data, presents a promising solution for enhancing anomaly detection using system logs.  
The specific checkpoint called 'bert-base-uncased' refers to a model configuration pre-trained using a vast corpus of English text without distinguishing between uppercase and lowercase letters.  
For the experimental evaluation of our system, we implemented a set of tests using distributed file system logs (HDFS). For the analysis, we selected two log files: one containing a smaller set of 2000 messages and the other significantly larger with 149,477 messages and an approximate size of 20.4 MB. These data volumes were chosen to provide a clear perspective on the system's performance under different processing loads.  
The format of the HDFS logs we handled follows a standardized schema that facilitates automatic analysis. Each log entry includes several comma-delimited fields: the date and time of the event (including minutes and seconds), a unique identifier for the process that generated the message, the message's severity level, the system component the message refers to, and the textual content of the message itself. This structured format is essential for the proper functioning of our program, as it allows efficient segmentation and categorization of information for subsequent processing and analysis.
## 5 Subjective Evaluation
### Subjective Evaluation: Localization Accuracy  
In order to evaluate the proposed stereophonic-microphone array in terms of localization accuracy when simulating spatially-distributed sound sources, subjective data were obtained in a localization experiment within a real room from listeners. The loudspeaker signals were generated using a single sound source and by simulating the delays between the microphones and the sound source. The optimized filters \(w^{opt}\) were applied on each microphone signal to obtain the output signal for the left and right array, which was then played back via the two loudspeakers during the listening experiment. The loudspeaker and array configurations are shown in Figure 3.  
The sound sources were placed on virtual locations between \(-30^{\circ}\) and \(+30^{\circ}\) in a five degree resolution, resulting in a phantom source stereo image based on intensity-panning between the left and the right loudspeakers. The evaluation took place in a reverberant room with the dimensions \((7.5,7.1,2.97)\) m with a reverberation time of \(T_{60}=0.45\) s. The distance between the loudspeakers was 3 m, and the listeners were seated at the position that created a \(60^{\circ}\) stereo triangle with the loudspeakers (cf. Figure 3). As a source signal, three short pink noise bursts with a total length of 1.1 s were presented to the listeners. The noise covered a frequency rang from 100 Hz to \(f_{s}/2\) covering the spectral content of musical signals. Data were obtained from seven listeners, and the 13 source position angles were presented in random order. For each subject, the experiment covered one training session and three measurement sessions. The task of the participants was to indicate the perceived direction between the loudspeaker using indicators placed between the loudspeakers in five degree steps.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 3 Localization and Mapping
### Local Refinement
#### 3.2.2 Refinement
We solve the linear system, \((\mathbf{H}+\lambda\mathbf{I})\Delta\mathbf{s}=-\mathbf{b}\), using sparse Cholesky factorization and update the state vector by \(\mathbf{\hat{s}}\leftarrow\Delta\mathbf{s}\). As we did for global alignment, we terminate the algorithm when \(\Delta\mathbf{s}\) is less than some predetermined convergence threshold.
## 3 Application of AIEgens in the Detection of Pesticide Residues in Fluorescence Sensors
### Detection of Pesticides by Porous Organic Polymer Sensors Based on AIEgens
The above three studies employ fluorescent porous polymers and AIE molecules to create composite luminescent materials. By utilizing their fluorescence characteristics and porous structures, along with mechanisms such as PET or IFE, they enable highly sensitive and selective detection of pesticides. Figure 17: (**a**) Illustration of the synthesis of carbazole-based porous polyaminals; (**b**) Photographs of the solid A17-1, A17-2, and A17-3 under UV light at an excitation wavelength of 365 nm; (**c**) Sensing experiments of A17-1 test paper for six pesticides in water medium at a concentration of 35 \(\upmu\)M; (**d**) Recycling sensing experiments of A17-1 for trifurlrain in the water medium at a concentration of 58 \(\upmu\)M. (Reproduced with permission from ref. [83]. copyright 2020 American Chemical Society). can enhance detection sensitivity, reduce autofluorescence interference, improve fluorescence stability, broaden the detection range, and support simple, rapid, and visual detection. [PERSON] et al. synthesized doped AIE polymer microspheres (DAIEPMs) and 2,3-bis(4- (bis(4-(tert-butyl)phenyl)amino)-phenyl)-furanonitrile polymer microspheres (BAPFPMs) using a microemulsion method [86]. They electrostatically adsorbed DAIEPMs onto the chlorothalonil (CTN) antibody to form the immune probe DAIEPMs@mAb (A20) (Figure 20a-c). The excellent AIE behavior and large Stokes shift of DAIEPMs in probe A20 enable it to function as a high-performance fluorescent marker in LFIA for the sensitive detection of CTN. In the absence of CTN, A20 is captured by CTN-BSA on the test line (T-line), while excess A20 migrates and is captured by the secondary antibody (sAb) on the control line (C-line). When CTN is present, A20 binds competitively to CTN, preventing its capture by CTN-BSA on the T-line, thus enabling sensitive detection of the organochlorine pesticide chlorothalonil (Figure 20d). The LFIA coupled with AIegens achieved highly sensitive and specific detection of CTN with a low detection limit of 0.012 ng/mL. This method offers a novel strategy for enhancing the optical properties of fluorescent materials and constructing sensitive and reliable detection platforms. Figure 20: (**a**) Detailed synthesis steps of DAIEPMs and BAPFPMs; (SDS: sodium do-decyl sulfate; PMMA: Poly (methyl methacrylate); PMAO: Poly (maleicanhydride-alt-1-octadec)); (**b**) Structures of TCBPEME (top) and BAPF (bottom); (**c**) Synthesis steps of A20; (**d**) Sensing performance of A20 for CTN detection and schematic diagram of CTN test strips. (Reproduced with permission from ref. [86]. copyright 2020 American Chemical Society). The detection methods discussed incorporate AIE characteristics into LFIA techniques, enhancing sensitivity for pesticide detection. In practical applications, these methods demonstrate strong recovery rates and low coefficients of variation, offering a novel approach to improve the optical properties of AIE materials and develop reliable detection platforms.
## 3 Experimental Results and Discussion
### Convergence Trajectory Analysis  
In Figure 4, with F1-F13 set to 30 dimensions, displays the convergence curves of seven different methods. GSLEO-AEFA converged to the global optimum on F9-F11, F16-F17, and F16-F23 and exhibited a greater convergence rate than compared algorithms in multi-modal functions. GSLEO-AEFA significantly increased the convergence accuracy for unimodal functions as compared to the original AEFA (F1-F7). The effective implementation of the LEO mechanism and application of changes to the initial population generation approach ensure that GSLEO-AEFA has enhanced optimization and convergence performance.  
Figure 4: _Cont._
## 4 Environmental Approach Using Air and Thermometric Sensors
### Setup and the Obtained Second Dataset  
We obtained another benchmark dataset using our originally developed sensor system, as depicted in Figure 6. Figure 7(a) depicts the room layout used in this experiment. The room is 8 m long and 3 m wide, with an area of approximately 24 m\({}^{2}\). This room is used mainly for a meeting as our satellite laboratory. The two desks have different orientations. We installed the respective sensor modules on the walls at the centers of the longitudinal and latitudinal positions. The installation height was approximately 2.5 m from the floor. Figure 7(b) depicts an image after sensor installation on the wall.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting Values** \\ \hline Learning iteration & 50 epochs \\ Batch size & 2 \\ Validation split & 0.2 \\ Num. input layer & 20 or 40 units \\ Num. hidden layer & 50 units \\ Num. output layer & 8 units \\ Optimization algorithms & Adam [213] \\ Look-back & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 14: LSTM parameters and their setting values.  
Figure 8: Experiment environment and sensor installation.  
We obtained time-series images using a hemispheric camera (PIXPRO SP360; Eastman Kodak Co., Rochester, NY, USA) for annotation. We used our proposed and evaluated method in Section 3 as a semi-automatic annotation method for labeling behavior patterns for this dataset. The behavior patterns comprise eight categories: eating (ET), operating a laptop computer (LC), operating a smartphone (SP), playing a game (GM), reading (RD), exiting the room (EX), taking a nap (NP), and sitting (ST). Of these categories, ET, LC, SP, RD, and ST are similar to the first dataset. We added GM, EX, and NP as new categories in the second dataset. We annotated them manually.  
Table 15 presents the number of signals in each behavior pattern. The total data amount is 11,521 signals. The quantities of data in each behavior are uneven because the subject spent with no restrictions in the room during the data recording. The number of GM data is the largest, accounting for one-third. The numbers of EX, NP, and ST data are small.