esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Theoretical analysis in [9] further indicates that if packets are routed through the unsafe area, the attacker can trace back to the source quickly. [PERSON] _et al._[9] proposed a protocol based on angle area to protect the source, which has several limitations: (1) determining the angle area incurs extra computational cost; (2) the generated phantom locations still gather with a high probability in two regions because each next forwarding sensor is chosen based on angle area; and (3) packets are not guaranteed to be routed bypassing the unsafe area. In [14] an opportunistic routing protocol (OpRo) is used to enhance source-location privacy. This work introduces three schemes, _i.e._, non-repeating opportunistic routing, opportunistic routing with random delay, and opportunistic routing with random relay to protect the source. Based on this work, [PERSON] _et al._[15] further proposed an Angle-based Dynamic Routing Scheme (ADRS) to enhance the source location privacy. ADRS employs the location information of nodes and calculates an inclination angle to avoid cycles around the source, but it cannot be applied to the network without knowing each node's location. Another solution against traffic analysis is introduced in [PERSON] _et al._[16]. In his work, [PERSON] _et al._[16] made use of the principle of anonymous connections for the Internet, in which the traffic between sender and receiver is obfuscated and routed through several different stations. This principle was realized with the concept of onion routing. Later on, [PERSON] _et al._[17] provide a solution called CROWDS, which uses anonymity sets, which include different degrees of anonymity, directly. Solutions in this category hide either the identity or the location of a sensor node. Other typical work in this category includes the anonymous communication scheme [18], the anonymous path routing [19], and the hashing based ID randomization [20]. Note that anonymity and unobservability may not be enough to protect a source's location. In fact, the source can be also endangered if an adversary is able to find the previous locations of a node, which allows the adversary to create a trajectory and forecast the next position [21]. To address the above limitations, we propose our new protocols SLP and SLP-E, to enhance the source location protection with low communication cost.
## 4 Design and Implementation of the Optimization Framework
### Cost Function (Weighted Sum)  
As the metrics listed above, the performance evaluation of the specific application scenario is an overall and comprehensive process which must consider multiple objectives at the same time. The evaluation engine of iMASK returns a set of performance metrics **p** (Section 4.2) that need to be optimized/improved simultaneously and is given as:  
\[\min_{(x)}\textbf{p}=[p_{1},p_{2},p_{3},......,p_{n}] \tag{1}\]  
where \(X\) is the parameter space. However, the solutions that can simultaneously optimize every metric are difficult to find for such a multi-objective problem. Therefore, a commonly used trade-off method combines different objectives into a linear cost function presented as a weighted sum:  
\[\min_{(x)}=cost(\textbf{p})=\sum_{i=1}^{n}w_{i}\cdot p_{i} \tag{2}\]  
where \(w_{i}\) is the weight vector that is designed to emphasize the importance of each performance metrics \(p_{i}\). \(X\) is the parameter space, which can consist of different types of design parameters for the sensor network as shown in [28], or it can be just a set of protocol configurations if only the communication protocol is under investigation.  
Thus, the multi-objective problem becomes single objective and the optimization can proceed by directly evaluating the value of the cost function. Note that this cost function in GA-based optimization is also known as the fitness function. Based on the application-specific requirements at hand, the designers can decide whether to choose all or only part of the most competing performance metrics for the fitness function. An example of a fitness function in terms of energy consumption, network reliability, and network delay can be presented as follows:  
\[f_{performance}=w_{1}\cdot nodeEnergy+w_{2}\cdot PktLoss+w_{3}\cdot SucPktLatency \tag{3}\]  
However, even with the same importance on each metric, each contribution to the weighted sum could also be different, since the scale of each metric is not the same. Hence, percentage data values are preferred to be used to reduce the impacts caused by the metrics' scale. The above formula is thus modified as:  
\[f_{performance} =w_{1}\cdot(nodeConsumedEnergy/TotalEnergy)+... \tag{4}\] \[w_{2}\cdot(PktLoss/TotalSensedPkt)+...\] \[w_{3}\cdot(SucPktLatency/SampleInterval)\]  
where \(nodeConsumedEnergy\) denotes the average energy consumption on every sensor node. \(TotalEnergy\) represents the maximum energy volume that can be used on each node. Other metrics are self-explanatory and some of them have been defined in the Section 4.2.
# Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review
## Gas Sensor
### Graphene Gas Sensors with Different Working Principles
#### 2.2.3 Quality Sensitive Gas Sensor  
The principle of quality sensitive sensor is that the operating frequency of the device changes with the type and concentration of the gas being detected. The change in frequency is proportional to the mass of gas molecules absorbed [19]. Quality sensitive sensors can be divided into surface acoustic wave (SAW) sensors and quartz crystal microbalance (QCM) sensors. Among them, the SAW gas sensors are more common than the QCW gas sensor. The SAW device is composed of a delay line, and graphene film is wrapped on the signal propagation path of the delay line to adsorb the gas molecules. When the gas concentration and species change in the sensitive region, the information of the sound speed and phase at the delay line will change, and leads to the change of electrical signal.
## 3 Results and Discussion
### Effects of Resin Content
#### 3.4.4 Thermal Conductivity  
As expected, higher amounts of urea formaldehyde resin in the straw mixture resulted in a higher thermal conductivity at constant density (0.7 g/cm\({}^{3}\)), as shown in Figure 14c. Thermal conductivity was increased from 0.095 to 0.113 W/m K with increasing the resin content from 10 to 14%. Thermal conductivity of the boards blended with a resin ratio of 10% was 19% lower than boards blended with a resin ratio of 14%. In general, it can be concluded that the thermal conductivity of the boards was positive to its resin content.
###### Abstract  
Wind energy is an alternative to meet the growing energy demand and protect the environment; however, in places with limited wind resources, only the installation of small horizontal-axis wind turbines (SHAWTs) is profitable. At the height of these turbines, the wind is usually unstable with gusts and turbulence due to obstacles in its path such as buildings and trees. The pitch angle must be adaptable to guarantee nominal rotation speed, and it is commonly regulated with a proportional-integral-derivative (PID) feedback controller. This controller works well when the wind is stable, but not with drastic changes in wind speed. To correct this problem, this article introduces a PID controller with automatic adjustment of the gain values using a fuzzy logic controller (FLC). The PID gain adjustment allows an optimal response speed of the system for different wind conditions. The membership functions of the FLC are determined from a methodology that includes: The measurement of the wind speed at a calculated distance, a statistical analysis of the wind variability, and a dynamic analysis of the wind path. In this way, it is possible to anticipate the response of the actuator to the arrival of a gust of wind to the rotor. The algorithm is implemented in 14 kW SHAWTs where the difference in performance with a conventional controller is quantified. Satisfactory results were obtained, the electrical output increased by 7%, and the risk of rotor damage due to vibrations or mechanical fatigue was reduced by 20%.  
renewable energy; small wind turbine; pitch control; PID controller; auto tuning; fuzzy logic controller +
Footnote †: journal: Applied Sciences  
Article  
Pitch Angle Optimization for Small Wind Turbines Based on a Hierarchical Fuzzy-PID Controller and Anticipated Wind Speed Measurement
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 4 Materials and Methods
### Model Selection
#### 4.2.1 The Emebeddibg Techniques Used in Our Study  
In our study, we tested a variety of embedding techniques, including OpenAI Text-embedding, Google Gemini Text Embedding, BERT, Sentence-Transformers, Sent2 vec, and Doc2 vec. We used this range of embedding techniques first to select the most effective embedding approach for our specific dataset and context. Second, to compare older static embeddings with newer dynamic embeddings. Traditional static embeddings, such as those produced by Doc2 Vec, map each word to a fixed vector, which does not change with context. In contrast, dynamic embeddings, like those generated by OpenAI's models, adjust the vector representation of a word depending on the sentence or context in which it appears. However, this approach requires greater computational resources compared to the static embeddings, which are generally faster and more efficient in terms of computation and memory usage. Our goal behind this comparison was to determine if the computational investment in newer embedding techniques leads to substantial improvements for classification tasks, thus justifying the additional computational resources. And finally, to use embedding techniques that have shown promising results in other domains to test if those methods will be beneficial in our context.
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.2.2 System Rate  
Affected by CSI errors, both the ergodic communication rate and the ergodic SINR do not admit explicit expressions. To handle this challenge, the statistical average method  
Figure 4: Schematic diagram of the hierarchical clustering algorithm.  
Figure 5: Schematic diagram of multibeam coverage with 7 multicast groups.  
is adopted to model the SINR and the communication rate. Therefore, the SINR and the communication rate of the \(k\)th user in the \(l\)th multicast group can be expressed as:  
\[\mathrm{E}\left\{\mathrm{SINR}_{k,l}\right\}\approx\frac{\mathrm{E}\left\{ \left|\mathbf{h}_{k,l}^{H}\mathbf{f}_{l}\right|^{2}\right\}}{\mathrm{E}\left\{\sum \limits_{j=1,j\
eq l}^{L}\left|\mathbf{h}_{k,l}^{H}\mathbf{f}_{j}\right|^{2}\right\}+ \sigma^{2}}, \tag{17}\]  
\[R_{k,l}\approx B\log_{2}\left(1+\frac{\mathrm{E}\left\{\left|\mathbf{h}_{k,l}^{H} \mathbf{f}_{l}\right|^{2}\right\}}{\mathrm{E}\left\{\sum\limits_{j=1,j\
eq l}^{L} \left|\mathbf{h}_{k,l}^{H}\mathbf{f}_{j}\right|^{2}\right\}+\sigma^{2}}\right), \tag{18}\]  
where \(B\) denotes system bandwidth. Equations (17) and (18) are approximations with closed form, the feasibility of which have been discussed in detail in Refs. [31; 32].
## 3 Materials and Methods  
As illustrated in Figure 2, the proposed method mainly consists of three key steps: adaptive density homogenization, hierarchical trajectory segmentation and road structure delineation. In detail, a data preprocessing is first performed on raw trajectory data to filter out the abnormal points and traces. Then, an adaptive density homogenization is utilized to reduce the impacts of various traffic volume on trajectory sampling density. After that, a hierarchical trajectory segmentation strategy is designed to gradually extract multi-level road intersection elements according to their direction and position characteristics. Finally, the geometric structures of road intersections are delineated by an iterative piecewise fitting algorithm.
###### Abstract  
This study pioneers the enhancement of 5G antenna manufacturing efficiency and reliability by integrating blockchain and smart contract technologies, supported by an in-depth Analytic Hierarchy Process (AHP) analysis. At the heart of our innovation lies the blockchain-based SER-M (B-SER-M) model, which delineates 'Subject', 'Environment', and 'Resources' as crucial factors in the manufacturing process. Our refined AHP analysis reveals 'Subject' as the paramount factor, with a pivotal influence weight of 0.465, underscoring its significance in elevating production efficiency and reliability. The integration of blockchain technology facilitates impeccable record-keeping and tracking at each production stage, thereby bolstering data integrity and enhancing traceability. Furthermore, the incorporation of smart contracts streamlines operations by automating processes, enabling the rapid identification and resolution of issues. These technological advancements not only significantly elevate manufacturing efficiency but also markedly improve reliability and quality control across antenna production. The enhanced results of our study demonstrate the formidable potential of integrating cutting-edge technologies in manufacturing, presenting a solid model for sustaining industry competitiveness in an increasingly digital and interconnected realm. Our contributions lay the groundwork for transformative advancements in manufacturing practices, setting a new benchmark for the integration of blockchain and smart contract technologies in enhancing 5G antenna production efficiency and reliability.  
blockchain; smart contract; 5G ceramic antenna; analytic hierarchy process (AHP); consistency rate; decision-making +
Footnote †: journal: Applied  
1
# MIMO Self-Heterodyne OFDM Using Band Selection Technique
## 4 Simulation Results and Discussion
Figure 10 shows the BER performance of the STBC MIMO self-het OFDM system using BS technique compared to the FSTBC MIMO self-het OFDM system using BS technique, assuming imperfect channel estimation. As noticed in Figure 10, the FSTBC MIMO self-het OFDM system using BS technique improves the BER performance by 2 dB, at a BER of \(10^{-3}\), when compared to the STBC MIMO self-het OFDM system using BS technique. The performance of the STBC MIMO self-het OFDM system and FSTBC MIMO self-het OFDM system using different numbers of subcarriers (K = 64, 128, and 256), for perfect and imperfect channel estimation, is presented in Figure 11. The simulation results in Figure 11 show that, increase the number of the subcarrier has nearly no effect on the performance the FSTBC MIMO self-het OFDM system and STBC MIMO self-het OFDM system, for perfect and imperfect channel estimation. The performance is nearly the same and the curves are overlap. The FSTBC MIMO self-het OFDM system will have less complexity because it can introduce the desired BER performance with low number of subcarriers and less SNR compared to STBC MIMO self-het OFDM system. For example, FSTBC MIMO self-het OFDM system obtains the BER of \(~{}10^{-3}\) at a SNR equal to 17.0 dB, using 64 subcarriers, where imperfect channel estimation is assumed. While STBC MIMO self-het OFDM system obtains the BER of \(~{}10^{-3}\) at a SNR equal to 19.35 dB, using 64 subcarriers, where imperfect channel estimation is assumed. Hence, the FSTBC MIMO self-het OFDM system obtains the desired BER using a smaller number of sub carriers and lower SNR compared to STBC MIMO self-het OFDM system. Moreover, comparative details (SNR) for STBC MIMO self-het OFDM system and FSTBC MIMO self-het OFDM system, assuming perfect and imperfect channel estimation for different numbers of subcarriers at BER equal \(10^{-3}\) are summarized in Table 2.  
Figure 11: BER for STBC MIMO self-het OFDM system and FSTBC MIMO self-het OFDM system, assuming perfect and imperfect channel estimation for different numbers of subcarriers. Figure 10: BER for STBC MIMO self-het OFDM system using BS technique and FSTBC MIMO self-het OFDM system using BS technique, assuming imperfect channel estimation.
###### Abstract
Backbones (on the X-axis) are ordered in increasing order of the corresponding measure. In the left figure, the bar-plot shows the difference between each backbone 1-diverse NSR and average 1-diverse NSR over all backbones. The corresponding NSR change scale appears on the right with \(\pm 20\%\) labels. Red bars indicate a below-average 1-diverse NSR while blue bars indicate an above average 1-diverse NSR. The most improved NSRs, on the right of the left figure, mostly appear on weak (red, below average) 1-diverse NSRs. Figure 9: Comparison of the computation times of sequence sets without diversity \(\delta=1\), with sequence sets with diversity \(\delta>1\). The color scale on the right indicates the corresponding value of \(\delta\). With dissimilarity threshold \(\delta\geqslant 6\), it is clear that the set of diverse suboptimal sequences have better quality than the 10 best enumerated sequences. Moreover, as shown in Figure 12, for harder instances, suboptimal diverse resolution becomes faster than exact enumeration. Figure 10: Comparison of the best NSR value obtained with ten 1-diverse sequences (\(\delta=1\), blue curve) with the best NSR value obtained with libraries of ten sequences of increased diversity all predicted with an allowed gap top optimal energy of 3 kcal/mol. Each plot corresponds to a specific additional value of \(\delta\) (\(\delta=2\) to 15, golden curve). Plots are lexicographically ordered from top-left to bottom-right, with increasing values of diversity (\(\delta\)). In each plot, the X-axis ranges over all tested backbones, sorted in increasing order of NSR value for the 1-diverse case and the Y-axis gives the corresponding NSR value. As the diversity requirement \(\delta\) increases, the NSR value indicated by the golden curve increases also visibly. Figure 11: Comparison of the best NSSR value obtained with ten 1-diverse sequences (\(\delta=1\), blue curve) with the best NSSR value obtained with libraries of ten sequences of increased diversity all predicted with an allowed gap top optimal energy of 3 kcal/mol. Each plot corresponds to a specific additional value of \(\delta\) (\(\delta=2\) to 15, golden curve). Plots are lexicographically ordered from top-left to bottom-right, with increasing values of diversity (\(\delta\)). In each plot, the X-axis ranges over all tested backbones, sorted in increasing order of NSSR value for the 1-diverse case and the Y-axis gives the corresponding NSSR value. As the diversity requirement \(\delta\) increases, the NSSR value indicated by the golden curve increases also visibly. Therefore, when predicting a library of sequences, if the instance is hard, it seems empirically wise to generate suboptimal diverse sequences, instead of enumerating minimum energy sequences, without diversity. Doing so, there is a higher chance of predicting better sequences \"in practice\" faster.
## 4 Results
### Accuracy Evolution  
The classification accuracy of SWI is presented in Table 2 and plotted in Figure 5 to facilitate visualization. In the Poyang Lake region, the overall accuracy was more than 96%. On 27 September 2016, the producer's accuracy and user's accuracy of water were 91.51% and 92.58%, respectively. On 9 September 2015, the producer's accuracy and user's accuracy of water were 94.18% and 93.41%, respectively. The approximation between producer's accuracy and user's accuracy leads to higher quantity accuracy than it does overall accuracy because the rates of omission and commission counteract. The quantity accuracy of water was 99.18% and 98.84% on 9 September 2015 and 27 September 2016, respectively. The accuracy was also superior in the Taihu Lake region. Therefore, the SWI threshold classification method could be applied to different regions during different periods with high and reliable accuracy.
## 4 Analysis of Simulation Findings
### Simulation Analysis with the Introduction of Variable Loads  
In order to investigate the impact of load variation on the performance of the Fuzzy-ADRC system, we examined the load variation during motor operation. To examine the motor speed tracking, the system set the initial speed to 1000 revolutions per minute, applied a load torque of 15 Nm at 0.15 s, and then removed the load torque at 0.3 s. Figure 9 illustrates the corresponding variations in motor speed and winding current as the load changes.  
According to the observations shown in Figure 9a, under Fuzzy-ADRC operation control, the speed of the motor remains relatively stable when the load torque is introduced. After measurement, the Fuzzy-ADRC initially stabilizes the motor speed at 1003 rpm; the speed becomes 996 rpm after adding the load torque, with a difference of 5 rpm; and the speed is restored to 1003 rpm after removing the load torque at 0.3 s. This results in a Fuzzy-ADRC with better load-carrying capability.  
This section introduces the use of variable loads to achieve load superposition and further validate the accurate compensation of Fuzzy-ADRC for minor disturbances in PMSM. To observe the motor speed tracking, the system applies a load torque of 20 Nm at 0.1 s, 15 Nm at 0.2 s, and 10 Nm at 0.3 s. Figure 10 illustrates the corresponding variations in motor speed and winding current during the load superposition process.  
It is clear from Figure 10a that changing load values for the Fuzzy-ADRC controller will result in varying oscillation and speed landing intensities; the larger the load, the more intense the oscillation, and the speed landing also rises. The motor's starting speed was determined to be 1003 rpm. By applying 20, 15, and 10 Nm load torques, respectively, at intervals of 0.1, 0.2, and 0.3 s, speeds of 993, 996, and 998 rpm were attained. The motor's rated load was tested and found to be 20 Nm. This experiment demonstrates the efficacy of the fuzzy self-tuning-based Fuzzy-ADRC technique for ensuring high-precision speed tracking of the PMSM servo system.  
Figure 10: Effect curve of Fuzzy-ADRC control under motor load superposition. Note: (**a**) Velocity tracking; (**b**) load stacking; (**c**) the three-phase current waveform of motor windings.  
Figure 9: State tracking of Fuzzy-ADRC control during variable motor load. Notes: (**a**) Velocity tracking; (**b**) variable load; (**c**) the three-phase current waveform of motor windings.