esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Finite Blocklength Coding in the Physical Layer  
In practice, a short frame structure is utilized to achieve ultra-low-latency transmission. Thus, the system is obligated to implement channel coding with a finite blocklength over parallel channels. The coding rate and the blocklength for finite blocklength coding are denoted by \(r[t]\) and \(m[t]\), respectively. Given the block error probability \(\epsilon\) and the received signal to noise ratio \(\gamma\), we know that [17]  
\[r[t]\leq\frac{1}{2}\log_{2}(1+\gamma)-\sqrt{\frac{1}{2m[t]}\Big{(}1-(1+\gamma )^{-2}\Big{)}}Q^{-1}(\epsilon)\log_{2}e, \tag{5}\]  
where \(Q^{-1}(\cdot)\) is the inverse of the Gaussian Q-function. Adjusting the coding rate is similar to adaptive modulation and coding in information theory. However, in finite blocklength coding, new challenges arise. The coding length is finite, and the codewords generated together need to be transmitted at a constant signal-to-noise ratio.  
In the finite blocklength coding regime, maintaining a consistent signal-to-noise ratio across the \(m[t]\) symbols that are collectively encoded is critical. Thus, power adaptation should ensure a consistent signal-to-noise ratio \(\gamma=\frac{g[t]P[t]}{N_{0}B}\) over the \(m[t]\) symbols, where \(B\) and \(P[t]\) denote the channel bandwidth and the power consumption, respectively. To mitigate the high levels of power consumption caused by poor channel conditions, we considered implementing truncated channel inversion. To ensure a constant signal-to-noise ratio at the receiver, the power consumption at the transmitter is  
\[P[t]=\left\{\begin{array}{rl}\frac{\gamma N_{0}B}{g[t]}&\text{if }g[t]\geq g_{th},\\ 0&\text{if }g[t]<g_{th}.\end{array}\right. \tag{6}\]  
Our objective is to optimize the threshold of the truncated channel inversion \(g_{th}\) in order to minimize the average power consumption while achieving a hard delay constraint in the system.
## 3 Experimental Apparatus and Method
### Experimental Apparatus
#### 3.1.2 Structure of a Scraped Surface Double Tube Evaporator  
In this study, a scraped surface double tube evaporator with a screw type scraper inserted inside the scraped surface double tube heat exchanger inner tube is used.  
The scraped surface double tube evaporator consists mainly of a double tube heat exchanger, scraper, motor for rotating the scraper and a speed reducer. The structure of the double tube heat exchanger is shown in Figure 4. Since seawater is a corrosive fluid, the inner pipe through which seawater flows is made of titanium, and the outer surface where refrigerant flows is made of copper pipe. An aluminum heat transfer pin was inserted between the outer tube and the inner tube to support the outer tube and widen the heat transfer area to promote heat transfer. The outer diameter of the inner titanium tube is 31.75 mm and the outer diameter of the copper tube is 47.8 mm. The total length of double tube heat exchanger is 1000 mm and the effective length of heat transfer is 800 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Accuracy** & **Unit** \\ \hline T-type thermocouple & \(\pm 0.3\) & \({}^{\circ}\)C \\ Resistance thermometer & \(\pm 1/10\times\) (0.3 + 0.005 t) & \({}^{\circ}\)C \\ Flow meter & \(\pm 0.05\) & \% \\ Mass flow meter & \(\pm 0.1\) & \% \\ Pressure transmitter & \% of span \(\leq\)1.0 & - \\ Salinity meter & \(\pm 0.5\) & \% \\ Power meter & 0.2\% of rng added & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of measuring instruments.  
Figure 4: Schematic diagram of the double tube evaporator using scraper.  
The double tube heat exchanger is arranged vertically and the low-temperature refrigerant flows from the bottom to the upper side of the semi-flooded type heat exchanger, which has a small channel between the copper tube and the aluminum pin to improve the heat exchange. The screw type scraper which is inserted into the titanium tube is made of engineering plastic (POM). The length of the scraper is 1200 mm, the outside diameter is 29 mm, and the inside diameter is 0.5 mm. The scraper is made in a spiral shape to facilitate the transfer of ice from the upper part to the lower part and scrapes off the ice layer formed on the wall of the tube through the rotation and discharges it in the form of ice slurry with seawater.  
According to [PERSON] et al. [9], the optimum rotation speed is 300 rpm when the outer diameter of the rotating body for ice making is 38 mm. To maintain the same line speed as proposed in this study, the rotating speed of the scraper should be about 400 rpm. To satisfy this requirement, a speed reducer is attached to the motor. The motor is connected to the inverter so that the speed of the scraper can be adjusted from 260 to 440 rpm by adjusting the frequency of the inverter.
## 2 Nonlinear Dynamic Modeling of Spur Gear Systems
### Influence of Error Excitation  
The transmission error is also an important factor affecting system stability. Let the load torque \(F_{m}=5.4\) Nm, the supporting stiffness be \(4\times 10^{6}\) N/mm, and the damping coefficient \(\zeta=0.12\). Figure 35 shows the bifurcation diagram of the system with respect to the transmission error \(e_{0}\) in the range of \(e_{0}\in[-0.2,\ 0.2]\) mm.  
Figure 31: Bifurcation diagram with respect to \(C_{\text{kxy}}\).  
Figure 34: \(C_{\text{kxy}}=20\); (**a**) phase diagram; (**b**) Poincaré map; (**c**) time-domain diagram.  
Figure 32: \(C_{\text{kxy}}=2\); (**a**) phase diagram; (**b**) Poincaré map; (**c**) time-domain diagram.  
As shown in Figure 35, when the transmission error is very small, i.e., (\(-0.115\), \(0.117\)) mm, the system is in a stable periodic motion state. Nevertheless, it can be observed in Figures 36 and 37 that the vibration displacement amplitude slightly increases. With an increase in transmission error, the system motion suddenly switches to a quasiperiod-doubling motion, and then quickly changes to the stable periodic motion. Afterward, the system falls into a chaotic motion through period-doubling bifurcation, as shown in Figure 38. As shown in Figure 39, if the transmission error is large, the system is in a stable periodic motion state, where some impacts will occur. Besides, the displacement along the LOA is in (\(-1\), \(2.5\)) mm, as shown in Figure 39c. Since the tooth backlash is \(0.1\) mm, this means that slipping and back-to-back meshing have occurred.  
As shown in Figure 40, although the bifurcation diagram of a large load is similar to that of a light load, its stable range is larger than the latter's stable range.  
In conclusion, when the load torque is large, the dynamic response of the gear system is not sensitive to the transmission error. Therefore, the requirements for manufacturing costs and assembly accuracy can be reduced. However, for a gear system subjected to a light load, its dynamic response is sensitive to the transmission error. Therefore, in order to evade the bifurcation points and the chaotic motion, the transmission error should be decreased by increasing manufacturing accuracy and assembly accuracy.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 3 Significance of Lacase in Biorefinery
### Lacase Sources  
Laccase has been gaining significant attention for its remarkable ability to oxidize a wide range of organic compounds. With its potential applications in various industrial and environmental fields, lacase has become a highly sought-after enzyme. However, its availability and cost have been significant limitations in its widespread use. Microorganisms are the primary source of lacase production, and several bacterial, yeast, actinomycetes, and fungal species have been reported to produce lacase.
## 2 Theoretical Analysis
### Principle of Single-Exposure Method
is illuminated solely by the reference beam \(\widetilde{R}\); and \(U_{o}^{\prime}(x,y)\) refers to the deformed object optical field. The complex amplitude of the transmitted diffracted wave through the hologram is derived as  
\[E(x,y)=T(x,y)\cdot U_{R}(x,y) \tag{6}\]  
In Equation (6), \(E(x,y)\) denotes the complex amplitude of the diffracted optical wave at the observation plane (\(x,y\)) after passing through the hologram; \(T(x,y)\) denotes the amplitude transmittance function of the hologram; and \(U_{R}(x,y)\) denotes the complex amplitude distribution of the reconstructed optical field when the hologram \(I\) is illuminated solely by the reference beam \(\widetilde{R}\). Extracting the first-order diffraction term from Equation (6) yields  
\[T_{1}=\varepsilon U_{o}^{\prime}(x,y)-\beta t_{0}U_{R}(x,y)U_{o}^{\prime}(x,y) \tag{7}\]  
In Equation (7), \(T_{1}\) denotes the complex amplitude of the first-order diffraction term of the hologram, and \(\varepsilon\) denotes the coefficient related to the intensity of the reference beam. The intensity calculated from Equation (7) is  
\[I(x,y)=T_{1}\cdot T_{1}^{*}=\left(\varepsilon^{2}+\beta^{2}t_{0}^{2}a_{R}^{4} \right)a_{0}^{2}-2\varepsilon\beta t_{0}a_{R}^{2}a_{0}^{2}\cos[\phi(x,y)-\phi_ {0}(x,y)] \tag{8}\]  
In Equation (8), \(a_{R}\) denotes the amplitude of the reference beam; \(a_{0}\) denotes the amplitude of the object beam; and \(\phi(x,y)-\phi_{0}(x,y)\) is the phase difference induced by object deformation. In \(\phi(x,y)-\phi_{0}(x,y)=\frac{2\pi}{\lambda}\delta\), \(\delta\) denotes the optical path change; bright fringes occur when \(\delta=N\lambda\) and dark fringes occur when \(\delta=\left(N+\frac{1}{2}\right)\lambda\). In the single-exposure method, dynamic interference patterns can be directly observed or recorded by using a camera for subsequent quantitative analysis. This approach facilitates real-time monitoring and evaluation of time-varying object deformations or displacements.
# Beyond Prompt Chaining: The TB-CSPN Architecture for
## 4 TB-CSPN: A Hybrid Architecture for Semantic and Structural Coordination
### Layered Communication Architecture  
In this layered model, topics act as semantic glue that enables coordination between agents with different capabilities and modalities. Surface agents interpret raw or ambiguous inputs, observation agents refine them into structured topic representations, and computation agents act upon these representations. This process renders topic propagation interpretable, traceable, and context-aware, thereby supporting both autonomy (as in multi-agent systems) and tight coupling (as in Centaurian systems).  
The whole organization can be represented as the composition of all nets formed with Transactions realizing TaskSpecifications. However, we focus on modeling specific processes expressed as sequences of tasks defining flows across the _Surface_, _Observation_, and _Computation_ layers. Each specific process is modeled as a _Workflow Net_ (stereotype WFN) [37], characterized by having one _input_ and one _output_ place, with any place and transition in the net lying on a path from the input to the output place.  
In particular, the doubly stratified, SOC-SCW (for Surface, Observation, and Computation, and Supervisor, Consultant, and Worker, respectively) structure of TB-CPNs provides some additional properties, since two kinds of flow, for both control and information, can be identified:  
1. a flow in the S \(\rightarrow\) O \(\rightarrow\) C direction, until some Worker operates to process the information by operations performed at layers above and certified by some agent at a higher level in the hierarchy; and
2. a flow in the C \(\rightarrow\) O \(\rightarrow\) S direction where the operations by the worker are certified and transmitted towards the output place.  
Figure 5: Petri Net instantiation of task coordination within TB-CSPN. The stereotypes CPN and WFN characterise each such net as a Colored Petri Net and a WorkFlow Net. This formal view illustrates how externally specified semantic resources are integrated into executable coordination nets. The Petri Net substrate remains structurally constant even as token content evolves semantically.  
Note that in this organisation, both the input and output places are located at the Surface level, and are connected to transitions performed under the control of a Supervisor. Moreover, no loop is formed, i.e., no transition or place can be visited in both the downward and the upward flows. This is because no rework is required: if the result of an operation is not considered relevant, it does not contribute to the formation of a valid response; instead, information about this type of failure is reported.
## 3 Results  
### Separation of CD4+ T Cells from PBMCs Using NP-CD4  
We aimed to establish a counting system that accurately determines CD4+ T cells in the whole blood. PBMC is known to be rich in CD4+ T cells (35%, [28]); therefore, to test the performance of NP-CD4 for the targeted cells, we first performed CD4+ T cell separation from PBMCs according to the recommended protocol.  
As shown in Figure 1a,b, NP-CD4 had very high specificity for targeted cell capturing as CD4+ T cells accounted for >95% of the total lymphocyte population after the purification process, whereas only 32% of the population before separation was detected as CD4+ T cells. Further, we stained PBMCs and the separated cells with fluorescence-labeled CD4-FITC (green signal) and CD3-PE (red signal) antibodies and observed fluorescence emitting cells under a fluorescence microscope (Axio Observer Co Apotome II, [PERSON]). In this case, the monocytes emitted only green signals and CD4+ T cells emitted both green and red fluorescent signals, which were represented as green- and yellow-colored cells, respectively, in merged images under different light source illumination. Figure 1 indicated that the yellow-colored CD4+ T cells only form a minor part (Figure 1c), whereas after cell separation, they form the major population (Figure 1d). The cell separation efficiency in five samples ranged from 46.2-85.2% (Figure 1e).  
This result suggests that the procedure recommended by the manufacturer is of good quality for CD4\({}^{+}\) T cell purification. However, this procedure requires a very large blood volume and complex instruments such as a swing-out centrifuge for operation. In the subsequent experiments, we tried to improve the cell separation conditions to be oriented for diagnostic purposes.
## 1 Introduction
[17] developed a CNN model to estimate soil organic carbon content using data from a digital elevation model, including slope, topographic wetness index, yearly average temperature, and annual rainfall. This study was successful in using images to predict soil characteristics and showed that the argumentation of training data can increase model accuracy while reducing overfitting. However, because the prediction unit is 100 m wide, there is a limit to applying it to precise agricultural land management. [PERSON] [18] validated the applicability of multiple artificial neural networks and CNNs to predict soil permeability using X-ray CT scan images. The above studies applied various deep learning methods to analyze soils, but the scale of the predicted results is too large for use in detailed management of agricultural lands. In particular, most of these studies do not properly take soil dry density into account. Conventional experimental methods and existing studies had practical limitations in performing accessible, rapid, and detailed soil water content and soil dry density monitoring of agricultural land. Therefore, this study focuses on developing a digital-image-based soil property prediction model that can be implemented in agricultural land. The study was conducted on high-resolution soil surface images taken with a commercial-level camera for which the results of previous studies were limited. CNN was used as the analysis method for soil surface images. Soil water content (WC) and soil dry density (\(\rho_{dry}\)) were determined as the objectives among the soil properties. Four major agricultural land soils were investigated. Image segmentation was performed for the argumentation of the training data in the CNN model. Image segmentation was performed in three cases. The CNN model was constructed for each case, and the accuracy was compared.
## 3 Investigation Development  
### Construction of the Prototype and Projection of the Modular Structure  
In this phase, the Sensor's general operation was established, and the considerations for technological implementation were incorporated, though not in excessive detail. System components that addressed the aforementioned functions were also designed. Construction of the prototype and its projection of the modular structure were diagrammed to describe the interaction between the parts and the sequencing in greater depth. Figure 2 shows the components of the design.  
The virtual classroom in which the sensor would be tested was built in Sakai LMS, working with the MySQL database engine. The data inputs to be taken from the virtual classroom were established as: Learning Style, Login Reports, Total Connection Time, Individual Student Performance vs. Group  
Figure 2: Logical design of the sensor.  
Performance, Individual Performance by Activity, Activity Average, and finally, and Resources Consumed (i.e., by the student). These resources included the reading of content, participation in chat rooms, tests taken, consumption of web content, the reading of the syllabus, evaluations sent, wiki participation, and forum participation, among others.  
This data was addressed in the following LA phases:  
* Explanation: Here, the teacher is shown a student's footprint in the virtual classroom. These data are important for understanding academic performance. They are generally presented using graphs and tables.
* Diagnosis: The student group is divided into quartiles, with each student located and identified within a quartile.
* Prediction: This stage features a risk assessment matrix that was designed to allow the teacher to review each student individually, and to identify which ones might possibly fail the course.
* Prescription: Here, the teacher can analyse a student's predominant learning style, see which sources have been used, send emails, send additional study material, or send reminders for activities that are still pending.  
The analysis of data gathered in the virtual classroom, and the subsequent application of these stages will result in a performance indicator for the student, which is the average grade to date. This indicator will have a dual purpose for the teacher: (a) it will assist the teacher in outlining customised tutoring options for students at risk of failing the course, which will be an expected performance improvement measure, and (b) it will prompt the teacher to do a review of the content and planning of course activities. This will occur because the teacher is able to answer the following questions: 'What is the student's predominant learning style?' 'Which resources are the participants consuming?' 'Are they reading more?' 'Are they participating in forums?' and 'Are they watching videos?' These two previous steps should lead, in turn, to an adaptation of content and methodology, in order to again validate the data, and to boost the students' academic performance.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Generation  
Watermark generation is a crucial step in robust video watermarking algorithms. Watermark generation, which is also called watermark preprocessing, randomly scrambles watermark information  
Figure 3: A framework of a general robust video watermarking algorithm.  
Figure 2: The application of video watermark in copyright protection.  
to enhance the security of the watermarking algorithm. The general model of watermark generation is shown in Figure 4.  
Now, several main encryption algorithms used in watermark preprocessing stage will be introduced.
## 2 Proposed Antenna Design
### Evolution of WLAN Band Notch  
Furthermore, when a CSRR is placed at the ground plane, it enables the attainment of a WLAN notch band characteristic. This notching characteristic effectively eliminates  
Figure 1: The geometrical representation of the proposed antenna: (**a**) front and back, (**b**) CSRR and arch, (**c**) MIMO antenna system, (**d**) prototype of antenna.  
Figure 2: The simulated result of C-band rejection.  
crosstalk between the narrow WLAN band and the UWB bands, which experience a high volume of data transfer. The corresponding wavelength of the CSRR, as determined by Equation (1), is calculated to be \(\lambda\) = 17.6 mm for the 5.5 GHz WLAN band shown in Figure 3.
## 4 Results and Discussion
### Message Generation Interval  
In this section, we further evaluate the delivery rate and overhead with different message generation intervals. We also compare our protocols with two protocols: Hybrid Type routing [23] and PRoPHET+ routing [25]. The simulation parameters we used are shown in Table 4.  
As we can observe in Figure 5(a), the proposed protocol performs a little bit better than both Hybrid Type and PRoPHET+. In the proposed protocol, since delivery predictability is used to choose intermediate nodes, it can avoid over-replication, and overflow in storage. That is why the delivery rate of Scheduling-PROPET can increase, even though the network load is high. From Figure 5(b), we can see that the proposed protocol has got a lower overhead, because it assigns priority for messages by using the message management mechanism (described in Section 3.3).
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### Csi  
With new technology developments in wireless communication systems, 4G long-term evolution (LTE) mobile transmissions, and Wi-Fi systems have used orthogonal frequency division multiplexing (OFDM). OFDM converts information on several altered subcarriers at one band. In the IEEE 802.11 standard, the receiver wants to approximate CSI in the physical (PHY) layer for the data translation. The CSI is the channel frequency response of each subcarrier under the OFDM system within the frequency field. Thus, CSI utilizes dozens of times more data than traditional RSSI in the network features between the sender and the receiver [82]. In the frequency field, CSI is definitely the PHY layer data with a fine-grained characteristic value that defines the amplitude and phase of a single subcarrier [83]. In the field of narrowband transmissions, this denotes the network property of the transmission link that expresses the reduction in the signal in the development of communication between the two nodes, containing scattering, distance and environmental attenuation, as well as other information [84]. The CSI-based method uses the physical layer channel state information of a communication link. A corresponding CSI can be measured when a target is displayed indoors. The CSI fingerprint matching, triangulation, and trilateration method can be used to determine the location of the target [85; 86].  
The CSI-based method shows good stability and can achieve higher location accuracy than the RSSI-based method [51; 87]. Moreover, CSI is favoured more than RSSI, since it develops the frequency diversity of Wi-Fi networks and is not coarse-grained like RSSI.  
The CSI-based approach has many advantages such as the ease of arrangement given the pervasiveness of a Wi-Fi setup [88]. In addition, the CSI-based Wi-Fi localization system can achieve decimetre-level accuracy. On the other hand, CSI-based Wi-Fi schemes need a labour-intensive site survey to calibrate the access points (APs) location and the antenna array direction, which obstructs real-world implementation [89]. Another disadvantage is that the CSI-based fingerprinting method needs larger space and more comprehensive time due to a larger measurement of CSI compared with RSSI, which is not appropriate for most situations [82; 90].