esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
A wireless sensor network (WSN) is composed of many small devices that are deployed in a physical environment. Each device, called a node, has special capabilities, such as communications with its neighbors, sensing and data storage and processing [1]. The nodes form mesh networks of devices that can collaborate among themselves, which allows for the implementation of distributed solutions to solve complex problems.  
Although sensor networks have many applications [2], environmental monitoring is an area in which the potential impact is extremely large, as described in [3]. These devices permit monitoring an area at a low cost and with little need for human presence, which is very important in these types of applications [4]. These networks are normally used in environmental monitoring to collect information via sensors that are incorporated into each node, and they pool this information in a special device called a Base Station [5]. The Base Station usually sends the information to a main server, where the collected data can be accessible through an infrastructure network, such as the Internet. The technical requirements of a WSN applied to environmental monitoring include:  
1. Autonomy: Batteries must be able to power the nodes during the whole network lifetime. Because the radio transceiver accounts for most of the power consumption in a node, the network must reduce the data traffic as much as possible as well as reducing the number of hops that are required to send a message.
2. Robustness: In this type of application, human maintenance is usually difficult because of the challenging terrain. Therefore, it is important to design robust software and hardware that can be adapted to any incident.
3. Flexibility: The network must be able to add, move or remove nodes to meet the application requirements. The network must automatically detect the changes, organizing the communications as a consequence.  
Although WSNs solve some common problems in wired environmental monitoring installations, in practical installations, these devices have some challenges that must be overcome. These challenges come from the fact that a WSN is typically formed by very small devices that have several restrictions: low power consumption, low weight (especially for mobile devices), low cost and low processing capabilities.  
For many years now, the authors of this paper have collaborated with the Do rana Biological Station. During these years, they have been designing and developing WSNs to be used in this park. In this paper, we describe all of the networks that are used to solve real monitoring problems of the biologists who work in Do rana National park.  
The remainder of this paper is organized as follows: Section 2 summarizes the environment in which the networks are deployed. Section 3 describes the networks chronologically. The future networks that are being developed are described in Section 4. Finally, Section 5 presents the concluding remarks.
# On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations
## 3 Results and Discussion
### Performance during C. glutamicum Fermentation
#### 3.1.1 DO Tendency Control in Cell Growth Phase  
The tendency control method was applied to maintenance of DO levels in the cell growth phase during _C. glutamicum_ fed-batch fermentation. The results are shown in Figure 5 and Table 1.  
For the purposes of comparison, the results of the DO manual control experiments, which were carried out with step-wise manual increase or decrease of agitation, are also depicted in these figures. As shown in Figure 5B, the stirrer speed was gradually increased from 255 rpm at 1 min to 284 rpm at 45 min. During the growth phase, large amounts of oxygen were often required for substrate utilization. As seen in Figure 5A, the range of DO concentrations was fairly large. The actuator was frequently under action during manual control of DO; in contrast, the actuator was much less active when the tendency control method was used. From 0 to 45 min, the actuator acted only three times. Furthermore, a negative DO tendency variation was a precondition for the initialization of tendency control. Although the actual values for DO were 8.2% at 5 min, 8.9% at 17 min, and 8.6% at 34 min,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Different Methods** & **From 0 to 45 min** \\ \hline Manual control & 27 times \\ Tendency control & 3 times \\ \hline \hline \end{tabular}
\end{table}
Table 1: Action times of actuator for different methods.  
Figure 4: The time-course changes in _C. glutamicum_ fermentation. (**A**) Changes in OD\({}_{620}\) (cell concentration) with time, (**B**) changes in substrate concentration (glucose concentration) with time, (**C**) changes in glutamate concentration with time, (**D**) changes in DO concentration with time, and (**E**) changes in stirrer speed with time.  
all of which were less than the DO low limit of 9%, and the average value was greater than 10%. In addition, due to negligence, the manual controller failed to increase the stirrer speed at 34 min in a timely fashion, which caused a decrease in DO to 7.3% at 35 min; however, during the DO tendency control experiments there were no such issues.
# Enhancing Small Language Models for Graph Tasks Through Graph Encoder Integration
## 4 Experiments
### Node Classification with Different Small Language Models (SLMs)  
In this subsection, we evaluate the proposed graph prompt tuning and fine-tuning methods on various small language models (SLMs) using the Citeseer dataset for node classification tasks. The results, presented in Table 3, include three SLMs--Nano-Mistral-170M, Pythia-160M, and SmolLM2-135M-Instruct--with evaluations of prompt tuning, fine-tuning, and their respective integration with GCN-T.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & \multicolumn{2}{c}{**Citeseer**} \\ \hline
**Model** & **Method** & **Node Classification** & **Link Prediction** \\ \hline \multirow{4}{*}{smolLM2-360M-Instruct} & Prompt tuning (with GCN) & 67.57 & 62.89 \\  & Prompt tuning (with GAT) & 64.40 & 58.07 \\ \cline{1-1}  & Prompt tuning (with G-Trans) & 64.71 & 60.03 \\ \cline{1-1}  & Prompt tuning (with GCN-T) & 69.68 & 64.86 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different graph encoders (GCN, GAT, graph transformer, and GCN-T) for SmolLM2-360M-Instruct on the Citeseer dataset. GCN-T consistently outperformed other encoders in terms of both node classification and link prediction tasks, highlighting the benefits of integrating convolutional and transformer-based approaches.  
For the Nano-Mistral-170M model, fine-tuning (with GCN-T) achieved the highest performance at 68.78, while prompt tuning (with GCN-T) recorded a result of 55.66, significantly outperforming standalone prompt tuning (31.98). These results indicate that Nano-Mistral-170M effectively benefits from the incorporation of graph structural information provided by the graph encoder.  
In the case of the Pythia-160M model, the results highlight its difficulty in handling graph-related tasks when only textual information is provided. Standalone prompt tuning achieved a notably low performance result of 1.06, demonstrating that the model struggles with graph tasks in the absence of structural information. However, integrating the graph encoder with prompt tuning (prompt tuning with GCN-T) improved the performance to 9.05, and fine-tuning (with GCN-T) further increased it to 32.73. These findings underscore the importance of incorporating graph structural information for improving the performance of the Pythia-160M model in graph-related tasks.  
For the SmoILM2-135M-Instruct model, fine-tuning (with GCN-T) achieved the best performance at 71.34, while prompt tuning (with GCN-T) also performed well with a score of 57.92. This demonstrates that SmoILM2-135M-Instruct is highly capable of leveraging the integration of graph structural information through GCN-T.  
Overall, the integration of GCN-T with prompt tuning and fine-tuning significantly improves the performance across all models. Notably, models like SmoILM2-135M-Instruct achieved peak performance in node classification tasks, showcasing the effectiveness of graph prompt tuning in utilizing structural information and pretrained semantic embeddings across diverse SLMs.
## 1 Introduction
However, in specific conditions, such as water restriction in the reaction medium or saturation by hydrolysis reaction products, this enzyme is capable of transglycosylation reaction and synthesis of various galactooligosaccharides (GOS) [19]. \(\beta\)-Gal can be found in the organisms of young mammals, where it is responded to for degradation of the main milk disaccharide, lactose. In addition, it is also present in many fruits (apples, kiwi, tomatoes), where it plays along with other enzymes and substances an important role in softening the crumb and peel of fruits by removal of galactosyl residues from pectin and xyloglucan [20]. In food industrially, this enzyme is used primarily in dairy processing to produce milk with reduced lactose content, simplify the preparation of yoghurts and kefirs, or speed up solidification and maturation in short-term cheese production technology. Moreover, \(\beta\)-galactosidase is used for therapeutic purposes for people struggling with lactose intolerance in the form of dietary supplements. \(\beta\)-Gal is also used in producing prebiotic substances, such as galactooligosaccharides (GOS), an essential component of humanised substitute milk for newborns and infants [19]. Glucoamylase is a hydrolase belonging to the family of amylases (specifically, exoamylases) that catalyses the hydrolysis of starch through the breakdown of 1,\(A\)-\(\alpha\)-glycosidic bonds between glucose subunits. The enzyme is produced by various microorganisms, from which glucoamylase produced by _Aspergillus niger_ is widely used in many industries, e.g., distillery, brewing, baking, and biofuel production [21; 22; 23]. Apart from catalytic potential, the main advantage of the described enzymes is their availability as ready-to-use preparations with good purity and relatively low price. This study aimed to analyse the influence of using different oligo- and polysaccharides converted directly in the cultivation medium by specific enzymes to accessible carbon sources and the effect of this solution on synthesis efficiency and selected material properties of BC.
# Substrate-Integrated Waveguide Microwave Sensor for Water-in-Diesel Fuel Applications
## 5 Cross-Slot SIW Applicator Characterization, Air Background  
The cross-slot SIW applicator was constructed and characterized. Figure 14 shows the fabricated device.  
The cross-slot SIW applicator was characterized with air as the background, i.e., without considering the fuel sample to be detected, with the VNA Keysight FieldFox. The measured modulus of the scattering parameters \(S_{11}\) and \(S_{21}\) as a function of the frequency \(f\) is shown in Figure 15 (solid curves) and compared with the simulated parameters (dotted curves). The results are in agreement with the simulations. A right shift of about \(\Delta f=0.5\;\mathrm{GHz}\) can be seen for both \(S_{11}\) and \(S_{21}\) curves. This behavior can be caused by the fabrication tolerance leading to a little mismatch between the geometric dimensions of the fabricated device and the nominal/optimized ones reported in Table 5.
## 2 Materials and Methods  
### Research Framework  
The study uses Google Earth Engine data (Landsat 5 TM, Landsat 7 TM, and Landsat 8 OLT/TIRS) to derive vegetation indices and land surface temperature data. These datasets undergo cloud masking and water masking before being processed for further analysis. Key indices like k_NDVI_, _LST_, _WET_, and NDBSI are normalized and analyzed through Principal Component Analysis (PCA) to construct the _RSEI_ (Remote Sensing Ecological Index) from 1990 to 2022. The study incorporates a variety of analyses, including the linear fitting method and the Sen+Mann-Kendall test to examine the spatiotemporal changes of _RSEI_. The influence of human activities on _RSEI_ is also assessed using neighborhood analysis. The contributions of both climate factors and human activities to _RSEI_ changes are quantified using partial derivative methods, helping to determine the relative importance of each factor in influencing ecological quality.
# Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
## 2 Harmony Search and Kernel Fuzzy Clustering  
### Harmony Search Algorithm  
Harmony search (HS) is a phenomenon-mimicking algorithm inspired by the improvisation process of musicians proposed by [PERSON] in 2001 [9]. The method is a population-based evolutionary algorithm and it is a simple and effective solution to find a result which optimizes an objective function. Parameters of the harmony search algorithm usually include harmony imagery size (_lms_), harmony memory considering rate (_lmcr_), pitch adjustment rate (_par_), and fret width (_fw_).  
The main steps of the classical harmony search mainly include memory consideration, pitch adjustment, and randomization. Details of HS are explained as follows:  
1. Initialize algorithm parameters
2. This step specifies the HS algorithm parameters, including \(hms\), \(hmcr\), \(par\), and \(fw\).
3. Initializing the harmony memory  
In HS, each solution is called a harmony. The harmony memory (HM) is equivalent to the population of other population-based algorithms. In HM, all solution vectors are stored. In this step, random vectors, as many as \(hms\), are generated following Equation (1):  
\[x_{i}^{j}=(UB_{i}-LB_{i})\cdot U(0,1)+LB_{i},i=1,2,\cdots,D \tag{1}\]  
where \(LB_{i}\) and \(UB_{i}\) are the lower and upper bounds of the \(i\)th variable. \(D\) represents the dimensions of the problem. \(U(0,1)\) is a uniformly-distributed random number between 0 and 1.  
1. Improvising a new harmony  
In this step, a new harmony \(x^{\prime}=(x_{1}^{\prime},x_{2}^{\prime},\cdots,x_{D}^{\prime})\) is generated based on three rules: memory consideration, pitch adjustment, and random selection. The procedure is shown in Algorithm 1.  
1. For \(i=1\) to \(D\) do
2. If \(U(0,1)\leq hmcr\) then
3. \(x_{i}^{\prime}=x_{i}^{j}\) where \(j\) is a random integer from \((1,2,\cdots,hms)\)
4. If \(U(0,1)\leq par\) then
5. \(x_{i}^{\prime}=x_{i}^{\prime}+U(-1,1)\times fw\)
6. end if
7. else
8. \(x_{i}^{\prime}=(UB_{i}-LB_{i})\cdot U(0,1)+LB_{i}\)
9. end if
10. end for  
**Algorithm 1 Improvisation of a New Harmony**  
1. Update harmony memory  
If the new harmony generated in Step 2 is better than the worst harmony stored in HM, replace the worst harmony with the new harmony.  
1. Check the terminal criteria  
If the maximum number of improvisations (NI) is reached, then stop the algorithm. Otherwise, the improvisation will continue by repeating Steps 3 to 4.
## 1 Introduction
The CFD results were analyzed and compared with the commonly accepted values of the local coefficient of the sampling hole and friction factor. The disturbances occurred because of the jet flows from the sampling holes. [PERSON] et al. [7] have performed an analytical and experimental study on complex compressed air pipe networks. They applied a matrix to describe the topology structure of the compressed airflow. Then, the relationship between the pressure and the flow of air was derived, and a prediction method of pressure fluctuation and airflow was proposed. The predicted results had the right consistency in the experimental data. In addition, [PERSON] et al. [8,9] have considered the smoke spread process in large-space buildings with various heights by using the Fire Dynamics Simulator (FDS). In that study, the fire source location was in the center of the room. Besides, twelve aspirator samplers were used in the ASD system. They were divided into three groups, group 1 and group 3 were located at the edge of the building. Group 2 was located at the centerline. The results showed that as the aspirator samplers were located nearby to the building's edge, the alarm for the fire was faster. So, the obscuration of the ASD was affected by the building's edge. [PERSON] et al. [10] also investigated the performance study of fire alarm systems in large space buildings. The study showed that the numerical calculation can forecast the response time for the ASD system very well and can be applied as a guide for designing an ASD system in large space buildings. Furthermore, many studies relating airflow, fires, and smoke detectors have been carried out and are described in the literature. The flow pressure analysis of pipe networks with linear theory method [11], modeling airflow in a prototype sanitary sewer system [12], and prediction of fan-assisted flow in a pipe network [13] have been investigated. Besides, the efficient iterative method for looped pipe networks [14] and the flow analysis in aspirating smoke detectors [15] speed was studied by changing airspeed. The airflow rate measurement with the differential pressure method [16] and experimental of turbulent flow through an orifice [17] has been considered. The study showed the turbulent flow through the orifice is a function of the orifice geometry. Moreover, based on the fire spread in the warehouse [18; 19] as well as fire safety management [20], the study of fire detection systems, such as aspirator smoke detectors for warehouses and large space buildings [21; 22; 23; 24; 25] have been performed. Although there are many papers regarding the pressure drop in the pipe and the obscuration in larger-space buildings, there has been no research relating to predicting the fire source location by using the pipe hole network in the aspirating smoke detector. For this reason, the Pipe Hole Network Program was developed to calculate the parameters of the pipe network. Based on the program, a numerical study and an experimental study were carried out to calculate the parameters to predict the fire source location when a fire occurs.
## 3 Methods
### Data Preprocessing
#### 3.1.1 Data Integration  
We collected the operating data for the EAA-305 electric bus on route 355 in Taichung City. Route 355 is approximately 11.2 km long and starts from the Youyuan-Zhongzhe Intersection to Xiyuan High School, bypassing the Tzu Chiang market. The route mainly consists of flat sections in the downtown area and slow sections adjacent to markets and schools. The passengers are mainly students and housewives and the daily numbers are constant, and therefore the driving behaviors are suitable for energy consumption analysis. The BMS in the electric bus collects the vehicle driving data and returns real-time vehicle information about every 10 s. The data packets are transmitted to the cloud platform via the mobile network and are written into the database. In this study, a backend database was used to collect vehicle driving data. The data were collected between August 2018 and the end of March 2019, with a total of 1,145,053 records. Each data record had a total of 18 fields, as shown in Table 1.  
Figure 2: Data transmission flow of the battery management system (BMS).
# Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems
## 3 Experiment
### Experiments Using Different Algorithms at the Same Communication Modulation Frequency  
Next, the communication modulation frequency of the experimental variables was controlled consistently to verify the improvement effect of the different algorithms on beam quality. When the modulation frequency of each communication path was 500 MHz, Figure 6 presents the spot images obtained under different conditions without the algorithm, with the SPGD algorithm and with the AdaDelSPGD algorithm.  
When the modulation frequency of each communication channel was 500 MHz, the calculated values of streak contrast and main lobe energy concentration were as shown in Table 3. It could be concluded that the SPGD algorithm could improve the streak contrast and main lobe energy concentration. The AdaDelSPGD algorithm proposed in this paper can further improve the beam quality under the same conditions; it can improve the contrast by 25.9%, and the main lobe energy concentration by 15.9%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Continuous Laser** & **Each Way with 200 MHz Communication** & **Each Way with 500 MHz Communication** \\ \hline Break contrast & 0.566 & 0.454 & 0.356 \\ Contrast reduction rate & - & 19.8\% & 37.1\% \\ Main lobe energy & 7.92\% & 7.15\% & 6.72\% \\ concentration & - & 9.7\% & 15.2\% \\ reduction rate & - & 9.7\% & 15.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The beam quality of interference fringes.  
Figure 6: Images of interference fringes with 500 MHz communication each way. (**a**) Without algorithm; (**b**) with the SPGD algorithm; (**c**) with the AdaDelSPGD algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Without Algorithm** & **SPGD Algorithm** & **AdaDelSPGD Algorithm** \\ \hline Streak contrast & 0.363 & 0.404 & 0.457 \\ Contrast enhancement rate & - & 11.3\% & 25.9\% \\ Main lobe energy concentration & 6.79\% & 7.18\% & 7.87\% \\ Concentration enhancement rate & - & 5.7\% & 15.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The beam quality of interference fringes of different algorithms.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 4 Dopant Implantation in CMOS
### Challenge of Ion Implantation in 3D Structure Devices
#### 4.1.2 Damage Control  
The damage control during doping is another key point to promote the performance of device. In planar devices, low temperature cryogenic implant is used to eliminate the end of range defects, while for 3D FinFET structure, the situation is quite different. It was found that a narrow fin which is isolated from large crystal volume; surface proximity and the 3D structure make amorphous silicon recrystallization problematic. If the fin is completely amorphized, only a very small seed for recrystallization, which results in a defective growth, and it degrades the resistivity and drive current of the transistors [118; 119]. Therefore, for 3D transistors, it is necessary to reduce the amorphization depth created by implantation as well as the annealing to minimize the fin damage.  
Hot implantation is proved to be a solution for damage-free fin source and drain extension (SDE) doping. By increasing the implantation temperature, damage accumulation could be mitigated, thus the self-amorphization of implant can also be decreased. By using this method, [PERSON] et al. demonstrated a half magnitude improvement in fin line conductance and one magnitude in junction leakage for n-type source and drain extension (NSDE) doping compared to a room-temperature implant [120].