esa-sceva/satcom-chunk-collection huggingface dataset:
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 4 Genomics in Cancer Treatment
### Overcoming Drug Resistance  
One of the greatest challenges in cancer treatment is drug resistance, a common occurrence as tumors evolve over time. Even with the success of targeted therapies and immunotherapy, resistance can develop due to genetic alterations in cancer cells. These mutations can cause the tumor to become less responsive or even entirely resistant to drugs initially effective in treatment. Genomic monitoring is essential in addressing drug resistance, as it provides a way to track these genetic changes in real time and adapt treatment strategies accordingly. A well-known example is the development of secondary mutations in the _EGFR_ gene, such as the _T790M_ variant, which confers resistance to first-generation EGFR inhibitors [53]. However, third-generation EGFR inhibitors like oismertinib have been designed to target this specific variant, overcoming resistance and providing continued therapeutic efficacy [54].  
Moreover, the application of dynamic genomic profiling and regular monitoring of genetic mutations during treatment enable the use of adaptive therapy strategies. This approach adjusts treatment plans based on the genetic evolution of the tumor, ensuring that therapy remains effective even as the cancer develops resistance. By continuously analyzing genetic data, clinicians can stay ahead of the tumor's adaptive mechanisms, ensuring more prolonged and effective treatment outcomes.  
The evolving role of genomics in cancer treatment led to the development of precision oncology, which adapts treatments not only to the specific genetic alterations in a patient's tumor but also the dynamic nature of those alterations over time [55]. This approach has facilitated more adaptive and personalized therapeutic strategies, providing patients with an individualized treatment plan that maximizes the effectiveness of therapies while minimizing side effects.
## 2 Tension Modeling and Its Analysis
### Tension Control Analysis  
Equations (8) and (9) show that the forward and backward tension are influenced by numerous factors during the rolling process, mainly as described in the following:  
1. The forward and backward tension are coupled with each other. The reason is that the workpiece is rolled by rollers while simultaneously being pulled by tension. A plastic deformation region is formed while rolling, and the forward and backward tensions are then coupled together because of their effects on neutral angle \(\gamma\).
2. The forward and backward tensions are affected by numerous factors such as work roller radius \(R\), rolling speed \(v\), friction coefficient \(f\), pass reduction \(\Delta h\), the type of workpiece material and its geometric dimensions, etc.
3. The tension cylinders must match the rolling speed while maintaining constant forward and backward tension. Therefore, tension control is a typical load-control process with excess force from strong positional disturbances [15; 16; 17]. Only the tension feedback closed-loop controller is incapable of meeting the requirements for synchronizing the speed between the hydraulic cylinders and the workpiece while also maintaining the stability of tension.
4. The lengths \(l_{1}\) and \(l_{2}\) between the clamps and the rollers, shown in Figure 2, are closely related to the displacement of the left and right tension cylinders. During rolling, these change in real time, which causes the tension control system to have properties of variable stiffness, which greatly increases the difficulty of tension control.  
In summary, achieving high-precision tension control for CRPMs with hydraulic tension represents a challenging problem. A satisfactory control effect cannot be obtained using conventional hydraulic circuits and control methods, making it necessary to design and develop both a new tension control platform and control method.
# Offline Payment of Central Bank Digital Currency Based on a Trusted Platform Module
## 4 Results and Discussion
### Double Spending  
The issue of double spending arises from the fact that a payment token can be reused at any time, as the sender retains access to the digital signature key associated with their digital currency wallet. A primary function of intermediaries or distributed ledger blockchains within payment systems is to prevent the reuse of tokens and transactions through verification processes. However, verifying payment tokens that have already been utilized poses significant challenges when operating offline due to network limitations. In the context of this study, tokens are rendered non-reusable; once a token is utilized, the counter value within the TPM is altered. For each payout instruction, the TPM generates a new token with an updated counter value, ensuring that the counter value of the new token differs from that of any previously used token. Consequently, attempting to reuse a token that has already been utilized will yield a different counter number (specifically, a difference in \"cbOtcn\" greater than 1). Furthermore, as the counter value is securely stored within the TPM, offline payments are effectively safeguarded against the risk of double spending.  
\[\text{Input token's ``cbOtcn\" in Payload = current TPM counter number} \tag{3}\]  
\[\text{Output token's ``cbOtcn\" in Payload = increased TPM counter number} \tag{4}\]
# Design of A Parallel Decoding Method for LDPC Code Generated via Primitive Polynomial
## 3 Parallel Decoding Method for m-Sequence Codes  
In this section, we first construct parity-check matrices by using the parity-check constraints of sampling sequence of m-sequence. Then, we analyze the deterioration mechanism of decoding performance caused by short cycles and present a way to eliminate the affection of short cycles. At the last of the section, we develop a new parallel decoding method with sub-decoder consisting of a pre-decode module and a decoding module.
## 4 Results and Analysis  
### Technical Theme Identification  
This study further identifies technical themes through data screening and preprocessing. The training of the LDA topic model requires first determining the number of technical topics. It determines the optimal number of technical topics by calculating the perplexity values of different technical topics. Figure 4 shows the perplexity curve under a different number of topics. It can be seen from the figure that when the number of topics is 1-15, the confusion curve shows a rapid downward trend. The number of topics is between 15 and 35, and the decline rate of the confusion curve slows down. The number of topics is between 35 and 50, and the confusion curve shows a smooth fluctuation trend. The lower the perplexity value, the stronger the model's generalization ability and the optimal number of topics. Therefore, based on the needs of this research and the perplexity value, the number of topics is optimal when it is 35.  
According to the weight of the subject words contained in each technical topic, combined with the specific content of the patent abstract corresponding to each technical topic, the 35 identified technical topics were named. And we conducted repeated checks to ensure that the patents under each technical subject can represent that subject. The unmanned boat technology subject terms are shown in Table 3. The specific naming is as follows: Speed control (Topic #0), Wireless communications (Topic #1), Navigation decision-making (Topic #2), Path planning (Topic #3), Hull structure (Topic #4), Automatic parking (Topic #5), Intelligent ship network (Topic #6), Anti-collision technology (Topic #7), Fault detection (Topic #8), Automatic recovery (Topic #9), Positioning and navigation (Topic #10), Remote control system (Topic #11), Surface rescue (Topic #12), Maritime monitoring (Topic #13), Ship body (Topic #14), Module control (Topic #15), Collaborative control (Topic #16), Hydrological observation (Topic #17), Propulsion technology (Topic #18), Anti-overturning technology (Topic #19), Attitude control (Topic #20), Task processing (Topic #21), Acquisition technology (Topic #22), Movable platform (Topic #23), Intelligent aquaculture (Topic #24), Image processing (Topic #25), Remote control (Topic #26), Surveying and mapping technology (Topic#27), Motion optimization (Topic #28), Antenna technology (Topic #29), Renewable
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 2 Dataset and Target  
This section provides an overview of the dataset and target QP emissions in this study. It includes detailed descriptions of the CSES-01 satellite and its payloads, the electromagnetic features of QP waves, and the process of identifying and characterizing QP emissions
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 4 Metal Telluride Thin Films
### Cadmium Telluride Films  
The cadmium acetate (Cd(CH\({}_{3}\)COO)\({}_{2}\)) and sodium tellurite or sodium tellurite (IV) (Na\({}_{2}\)TeO\({}_{3}\)) were used to synthesize thin films on glass slides [123]. Hydrazine hydrate was added to the beaker during the experiment to improve the precipitation of CdTe on the substrates. The reaction mechanism was reported as indicated in Equations (39) and (40).  
\[\mathrm{TeO}_{3}^{2-}+2\mathrm{OH}^{-}\rightarrow\mathrm{Te}+2\mathrm{O}_{2} +\mathrm{H}_{2}\mathrm{O}+4\mathrm{e}^{-} \tag{39}\]  
\[\mathrm{Cd}^{2+}+\mathrm{Te}+2\mathrm{e}^{-}\rightarrow\mathrm{CdTe} \tag{40}\]  
The XRD studies indicated a hexagonal (wurtzite) phase with a crystallite size of about 22 nm. Electrical resistivity and activation energy were 4.11 \(\times\) 10\({}^{3}\)\(\Omega\cdot\)cm and 0.215 eV, respectively. The direct band gap was obtained (1.41 eV) for the films with thicknesses of about 272 nm, as shown in Figure 7. The cadmium chloride (CdCl\({}_{2}\)) and Na\({}_{2}\)TeO\({}_{3}\) were used to prepare thin films on glass substrates [124]. It was seen that the activation energy (0.39 to 0.195 eV), electrical resistivity (10.52 \(\times\) 10\({}^{3}\) to 4.78 \(\times\) 10\({}^{3}\)\(\Omega\)-cm), and band gap (1.86 eV to 1.47 eV) decreased; however, the grain size increased (14.5 to 32.8 nm) with increasing film thickness from 96 to 312 nm. The CdTe film formation took place by using tartaric acid as the complexing agent, as reported by [PERSON] and co-workers [125]. It was observed that the film thickness increased when the deposition cycle increased (up to 30 cycles) and the growth rate was measured at about 6.7 nm per deposition cycle. The obtained films (30 cycles) showed cadmium (Cd) as poorer and tellurium (Te) as richer based on the EDX analysis. Further, the activation energy (0.164 eV) and electrical resistivity (6.64 \(\times\) 10\({}^{4}\)\(\Omega\)-cm) of these films were determined.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 15 Enhancements
#### 2.3.5 Time Division Duplex (TDD) Support  
In Release 15, a new feature of TDD support is introduced with a new TDD frame structure (type 2). For both 3.75 kHz and 15 kHz spacing, some specified restrictions are introduced i.e., only a normal cyclic prefix is supported for NB-IoT transmission. To support some of the TDD configurations with few downlink subframes, some of the system information (SI) can be transmitted on non-anchor carriers. In this way, the UE will have reduced system information acquisition and search time, and hence reduced UE differentiation and access control [30; 46; 47].
## 3 AS01 SAR Satellite Data Experiment and Analysis
### AS01 SAR Satellite Data Processing Experiment
Through the enhancement method, the ship's wake becomes clearly visible. The bright streak persists consistently and has a relatively large contrast with the background. Additionally, some wave features are also clearly visible in the image, and further processing can reveal the distribution of ocean currents. The reduction in noise is evident from the statistical histogram, where the distribution in the enhanced image is more concentrated compared to the original image, demonstrating the effectiveness of the enhancement method. The enhanced image demonstrates clearer wake features and reduced noise, highlighting the effectiveness of the proposed SAR wake enhancement algorithm. This refined image provides improved visibility of ship wakes and enhances the overall utility of SAR imaging in maritime applications. Figure 5: Comparison of algorithm processing images and histograms.
## 2 Materials and Methods
### Acartia tonsa Culture  
Natural seawater (SW), collected from a site far from known pollution sources, filtered through filters with a porosity of 0.45 m (Millipore) using a vacuum pump in a recirculation system to remove suspended matter, was used for _Acartia_ cultures. Before the experiment, the water underwent analysis for salinity, dissolved oxygen (DO), pH, nutrients, and contaminants to check the ambient conditions and absence of contamination. Temperature, salinity, DO, and pH measurements were conducted using a Seven Excellence Metler Toledo instrument (N.V. Mettler-Toledo S.A., Zaventem, Belgium) calibrated before each use. Nutrient and contaminant analyses followed international seawater analysis methods [50; 51; 52; 53; 54].  
_A. tonsa_ eggs (Figure S1) from the market (Recifathome, France, [55]) were inoculated into 500 mL flasks containing 250 mL of dilution water and kept at 20 \(\pm\) 2 \({}^{\circ}\)C with a 16:8 light photoperiod and gentle aeration (1 bubble/s) (Figure S2) for 48 h, as specified by the producer.  
After inoculation and hatching (after 48 h), microscopic analysis was performed to assess the number of nauplii, which were transferred to 4 L flasks with 3 L of dilution water, maintained with gentle aeration (1 bubble/s) at 20 \(\pm\) 2 \({}^{\circ}\)C under a 16:8 light photoperiod (Figure S3).  
After reaching copep date I, the cultures were moved to a temperature-controlled room at 20 \(\pm\) 2 \({}^{\circ}\)C. The cultures were observed as they progressed to copepite I- V and the adult stage (Figure 1) by microscopic analysis every 3 days.  
Figure 1: Schematic representation of _A. tonsa_ development stages (original photos).  
The culture water was renewed at each evaluation. The old water was passed through a series of different sieves (60-180 \(\upmu\)m mesh size), depending on the copep's development stage.
# Horizontal Bending Angle Optimization Method for Scraper Conveyor Based on Improved Bat Algorithm
## 3 The Proposed Method  
### Improvement of BA  
Bat algorithm has the advantages of simple structure, few parameters, strong robustness and easy implementation. However, the bat algorithm has problems such as slow convergence and easily falling into local optimum.  
To improve the convergence speed of the bat algorithm, this paper uses the inertia weight (\(\omega\)(t)) method to adjust the step size of the bat flight. The \(\omega\)(t) coefficient distribution diagram is shown in Figure 2. The inertia weight function is shown in Equation (7). The speed update formula of the bat search is described in Equation (8).  
Figure 1: Foraging process of the bat.  
Figure 2: Distribution of inertia weight coefficient.  
\[\omega(t)=exp\bigg{(}-\Big{|}\frac{t-N_{t}}{N}\Big{|}\bigg{)}=\left\{\begin{array}{ ll}exp\bigg{(}\frac{t-N_{t}}{N}\bigg{)}&0<t\leq N_{t}\\ exp\bigg{(}\frac{N_{t}-t}{N}\bigg{)}&N_{t}<t\leq N\end{array}\right. \tag{7}\]  
where \(t\) is the number of iterations of the bat; \(N\) is the maximum number of iterations of the bat algorithm; and \(N_{t}\) is the iterative demarcation point, it can be determined by the actual work in the field.  
\[v_{i}^{t}=\Big{(}x_{i}^{t-1}-x^{*}\Big{)}f_{i}+v_{i}^{t-1}\cdot\omega(t) \tag{8}\]  
To solve the problem of falling into local optimum, this paper expands the range of bat fly distance range (\(FR\)) obeying the t-distribution. The shape of the curve of the t-distribution is related to the degree of freedom (df). When df = 1, the t-distribution curve coincides with the Cauchy distribution curve. When df \(\rightarrow\infty\), the t-distribution is normally distributed. The probability density function curves of Cauchy distribution, t-distribution, and normal distribution are shown in Figure 3. The rules for random migration of bat populations is expressed as:  
\[x_{i}^{t}=x_{i}^{t-1}+v_{i}^{t}+FR \tag{9}\]  
The Cauchy distribution has a good global development capability, while the Normal distribution has a stable local search capability. The number of iterations of the bat population is taken as the df of the t-distribution. As the number of iterations increases, the \(FR\) varies between the Cauchy distribution and the Normal distribution. At the beginning of the bat population iteration, the t-distribution is close to the Cauchy distribution. At this time, the bat search flight range and population diversity are improved, so that the local optimum is avoided. In the late stage of the bat population iteration, the t-distribution is close to the Normal distribution. In this process, the bat's local search ability is strong and the convergence speed is fast.
# A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
## 5 Research Challenges and Future Directions
Accuracy  
Despite having the capabilities of learning algorithms to detect and identify anomalous behavior in real-time, these algorithms are still subject to optimization to improve accuracy, including the reduction in the false positive detection rate, especially in large-scale sensor networks [15; 27; 69; 75]. Scalability is another major requirement for anomaly detection algorithms because many algorithms lose their efficiency when handling a large volume of data [41]. Most current data stream algorithms for anomaly detection lose their effectiveness in the presence of high-dimensional data [25]. Therefore, accurately and efficiently redesigning existing models to detect outliers is necessary. More precisely, when many characteristics are observed, a set of outliers may appear at a given time in only a subset of dimensions. This set of outliers appears natural regarding the different subset dimensions and/or time frame. The many features are another challenge faced by anomaly detection algorithms in selecting the most important data features [37]. Therefore, the reduction in features is critical in choosing the most important ones that display the whole data.
# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Methods  
In this section, deep learning-based person ReID methods are grouped into four main categories, as represented in Figure 4, including methods for feature learning, sequence learning, generative learning, and deep learning metrics. These categories encompassed several methods, and they are discussed in the following in terms of their main aspects and experimental results.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **\#** & **\#** & **\#** & **Label Method** & **Size** & **Tracking Sequences** \\  & **Identities** & **Cameras** & **Images** & & & \\ \hline VIPeR & 632 & 2 & 1264 & Hand & \(128\times 48\) & NO \\ ETH1,2,3 & 853,528 & 1 & 8580 & Hand & Vary & YES \\ QMUL iLIDS & 119 & 2 & 476 & Hand & Vary & NO \\ GRID & 1025 & 8 & 1275 & Hand & Vary & NO \\ CAVIRateroid & 72 & 2 & 1220 & Hand & Vary & NO \\
3 DPeS & 192 & 8 & 1011 & Hand & Vary & NO \\ PRID2011 & 934 & 2 & 24,541 & Hand & \(128\times 64\) & YES \\ WARD & 70 & 3 & 4786 & Hand & \(128\times 48\) & YES \\ SAIVT-Softbio & 152 & 8 & 64,472 & Hand & Vary & YES \\ CUHK01 & 971 & 2 & 3884 & Hand & \(160\times 60\) & NO \\ CUHK02 & 1816 & 10 & 7264 & Hand & \(160\times 60\) & NO \\  & & (5 pairs) & & & & \\ CUHK03 & 1467 & 10 & 13,164 & Hand & Hand/DPM & NO \\  & & (5 pairs) & & & & \\ RAiD & 43 & 4 & 6920 & Hand & \(128\times 64\) & NO \\ iLIDS-VID & 300 & 2 & 42,495 & Hand & Vary & YES \\ MPR Drone & 84 & 1 & - & ACF & Vary & NO \\ HDA Person & 53 & 13 & 2976 & Hand/ACF & Vary & YES \\ Dataset & & & & & \\ Shipunhkan & 24 & 16 & - & Hand/ACF & \(128\times 48\) & YES \\ Dataset & & & & & \\ CASIA Gait & 124 & 11 & - & Background & Vary & YES \\ Database B & & & & & subtraction & \\ Market-1501 & 1501 & 6 & 32,217 & Hand/DPM & \(128\times 64\) & NO \\ PKU-reid & 114 & 2 & 1824 & Hand & \(128\times 64\) & NO \\ PRW & 932 & 6 & 34,304 & Hand & Vary & NO \\ Large scale & 11,934 & - & 34,574 & Hand & Vary & NO \\ person search & & & & & \\ MARS & 1261 & 6 & 1,191,003 & DPM+GMMCP & \(256\times 128\) & YES \\ DukeMTMC- & 1812 & 8 & 36,441 & Hand & Vary & NO \\ reid & & & & & \\ DukeMTMCreid & 1852 & 8 & 346,261 & Doppia & Vary & NO \\ Airport & 9651 & 6 & 39,902 & ACF & \(128\times 64\) & NO \\ MSMT17 & 4101 & 15 & 126,441 & Faster RCNN & Vary & NO \\ RPIfield & 112 & 12 & 1,601,581 & ACF & Vary & NO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Global overview of the found public available databases for person ReID.
## 1 Introduction  
Various acoustic sources generate underwater noise in the ocean, and the noise level and frequency differ depending on the sources, such as ocean current and turbulence, wind, rainfall, aquatic organisms, and ships [1, 2]. The ambient noise in shallow water is more complex. Still, it can be divided into two categories, one from human activities and the other from natural phenomena, which have different frequency characteristics [1, 2]. Ship noise affects frequencies below 100 Hz but sometimes up to 1 kHz [3, 4, 5, 6, 7]. The natural noise generated by waves, aquatic life, and thermal and aquatic biological noise affect the high-frequency band above 1 kHz [8, 9, 10, 11].  
The soundscape study in the habitats of snapping shrimp revealed that snapping is the dominant noise source in the high-frequency band in the coral reefs [12, 13, 14]. As a result of studying the diurnal variation of underwater ambient noises in the habitats of the snapping shrimp in the west and south seas of Korea, it was observed that the activity of snapping shrimp showed more dependence on the water temperature rather than the wind speed and the twilight [15]. The soundscape trend measured at various locations at the estuary showed semidiurnal tide characteristics [12, 16, 17]. Still, there was no noticeable change in shrimp sound with tidal and lunar phases, unlike other previous investigations [12, 18, 19]. Our earlier study confirmed that the sound in the low-frequency band (\(\sim\)100 Hz) changeswith the tide and the lunar cycle [20]. To observe the circadian and tidal characteristics of snapping shrimp sounds, it is necessary to measure the sound for an extended period.  
In this paper, underwater ambient noise was measured by a moored hydrophone for 3 months to investigate the temporal change of the sound of snapping shrimp near the wave power generation test site in the western part of Jeju Island, South Korea. Snap rate per minute was computed and analyzed based on the acquired acoustic data showing circadian and tidal variations.