esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Notable Individual Movements  
Several tagged tiger sharks were tracked returning to Bimini following prolonged absences and extensive movements in the region. For example, PTT ID 174072 (acoustic ID 17556), a mature female, was caught and tagged near Bimini on 10 May 2018. Tracking data showed that she departed the area on 27 April 2019 and although satellite transmissions were sparse in the following months, she was detected off the northern coast of Venezuela during June 2019. She soon began to move northward and was detected off the coast of Nova Scotia, Canada, by August 2019. The satellite tag ceased reliable transmissions on 5 September 2019, but the acoustic transmitter detections recorded her in Bimini on 7 April 2020; completing a round trip journey of more than 12,000 km, across a 344-day span (Figure 8). Female tiger shark PTT ID 174073 (acoustic ID 12726) was tagged near Bimini on 26 June 2018. Nearly immediately, she moved northward, reaching the area off the east coast of Nova Scotia two months later, then moving southward during September 2018, a trip of 4500 km across 97 days. The satellite tag ceased transmissions near Bermuda, but she was detected on the Bimini acoustic array from February through May 2019 (Figure 8).  
The movements and reproductive cycle of these two sharks correspond with the hypothesis that at least some tiger sharks reside in Bimini during and immediately following gestation and pumping, but that they may mate elsewhere. Shark 174072 was recently post-partum at the time of tagging and therefore resided around Bimini during'recovery' time following pumping. Her northward movements the following summer corresponded to the proposed mating season, which may occur during seasonal food source aggregations. Shark 174073 was determined to be non-gravid at the time of capture and immediately moved northward before returning to Bimini the following spring, while presumably pregnant. We acknowledge these are limited observations, but they may provide first insights into some drivers of movement and long-term site fidelity, including the possible reproductive fidelity of tiger sharks. Reproductive fidelity, and in some cases even natal philoparity, has been documented in other shark species, such as lemon sharks near Bimini [55]. Future research could include genetic analysis of offspring to investigate repeated pumping in the area across multiple years.  
Figure 8: Location estimates of tiger sharks, _Galeocardo cvier_ (**A**) PTT ID 174072 and (**B**) PTT 174073 generated from Bayesian State-Space Movement Model with ‘Day’ at liberty since tagging. The dashed line represents the shortest potential tiger shark path determined through acoustic telemetry post-satellite tag ceasing transmissions.
## 4 Results  
### Comparison of DInSAR Displacement with Feature Tracking and DEM Differencing  
First, we assessed the accuracy of our product by comparing the annual DInSAR displacement rate with results derived from optical imagery. At Kaiserbergtal, we used the available UAV data for FT to compare horizontal displacement in the E-W direction and DEM Differencing to assess the elevation change. For the site in Grison, we used publicly available orthophotos for FT comparing only the E-W displacement.
# Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things
## 2 Related Work
The thresholds of the RS-A-SISO algorithms were obtained by the density evolution technique in [21]. Another structure is the forward/backward adaptive algorithm. An exact expression for the soft metrics was derived when the unknown parameter was modeled as a Gauss-Markov process in [22], which can be estimated iteratively by the Kalman filter. The author in [23] employed the concept of bidirectional estimation in [22] and derived a generalized a posteriori probability of soft branch metrics. The FSM can also be described by a hidden Markov model (HMM), and the Baum-Welch (BW)/EM algorithm allows for great likelihood estimation of the unknown parameters in the HMM. The batch-BW (BBW) algorithm, as well as two variants were proposed by [PERSON] [24] for time-invariant channels. One is to split the received signal into several sub-blocks, producing different channel estimates in each, called the segmented batch-BW (SBBW) algorithm. However, the variant needs to avoid over-fragmentation because of a poor estimate from fewer data. An alternative algorithm called time-dependent BW (TDBW) was derived by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. The author in [25] proposed an improved Baum-Welch algorithm to directly estimate the channel parameters, avoiding over-parameterization in the estimation problem. In [26], an algorithm for joint channel estimation and equalization by applying the Viterbi algorithm within an EM iteration was introduced, which was used to implement the E-step. However, the major drawbacks among the works cited above are relatively poor convergence with an inappropriate initializer and high complexity. CPM serves as one of the preferred modulation schemes for the transmission of low data rates in the IoT uplink, suffering from the high complexity and poor convergence of the channel estimation at the receiver. Therefore, the paper proposes a low-complexity blind equalization algorithm for short-burst CPM signals based on the HMM. The proposed blind equalizer significantly outperforms the traditional one in complexity, while keeping a similar BER performance, which helps the device achieve online real-time detection. In general, as the spectrum resources are limited and the number of connected devices is increasing day by day, CPM is a promising modulation scheme, which is suitable for battery-powered devices and is expected to play an important role in the physical layer design of UAV-aided IoT communications.
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 4 Simulation and Results
### Discussion  
The proposed method uses histogram smoothing and Retinex theory for natural illumination enhancement. It estimates the illumination component of low-light images based on mean and distribution constants. Color correction using compensating channel selection in the RGB channel reduces color distortion and sharpens contrast, and non-local averaging based on a local steering kernel minimizes noise in the resulting image. The resulting images of the proposed method show clear and natural images, unlike the existing methods in the visual evaluation, and the best results are achieved in quantitative evaluations.
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
###### Abstract  
Laser Powder Directed Energy Deposition (LP-DED) is a very powerful Additive Manufacturing process for different applications, such as repair operations and the production of functionally graded material. However, the application is still limited, and one of the main reasons is related to the lack of knowledge of the process mechanisms. Since the mechanisms involved in the process, which are mutually related to each other, directly influence the properties of the produced part, their knowledge is crucial. This paper presents a review of the LP-DED mechanisms and the relationship between the input process parameters and related outcomes. The main mechanisms of the LP-DED process, which are identified as (i) laser irradiation and material addition, (ii) melt pool generation, and (iii) subsequent solidification, are discussed in terms of input parameters, with a focus on their effects on the deposition effectiveness, and interrelation among the mechanisms of the deposition process. The results highlight the complexity of the mechanisms involved in the LP-DED process and guide engineers in navigating the challenges of the deposition process, with a specific focus on the critical parameters that should be investigated when new materials are developed, or process optimization is carried out. additive manufacturing; directed energy deposition; laser metal deposition; powder stream; melt pool; microstructure; residual stress +
Footnote †: journal: _Sci. produce components with a maximum size of 400 mm. DED processes, on the other hand, do not use a closed chamber, and as a result, the deposited dimensions can reach 3000 mm. Another advantage of DED processes over PBF processes is that an existing surface of a component could be used as a building platform. This feature is important for repair applications. Moreover, it is possible to change the material during the deposition process, thus obtaining components characterized by different properties in different areas. Based on these advantages, examples of the applications of DED processes are the production of large components [12; 13], the repairing of high-value components [14; 15], and surface remanufacturing [16; 17]. DED processes can be classified according to feedstock material and energy source [19]. Among the different DED processes, those using laser as the energy source and powder as the feedstock material are the most commonly used [20]. Different technologies that refer to the same process are developed and labeled differently, such as Laser Metal Deposition (LMD), Laser Cladding, Laser Engineering Net Shaping (LENS(r)), Directed Light Fabrication (DLF), and Direct Metal Deposition (DMD(tm)). In this paper, the term Laser Powder Directed Energy Deposition (LP-DED) refers to this process. In the LP-DED process, a deposition head delivers metal powder into the melt pool generated by a focused laser beam. Due to the localized thermal energy and the rapid move
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
## 4 Simulation Results
### Analysis  
In this section, we have provided a comparison to the currently existing variable-weight coding scheme, which is called block coding dimming control scheme [14]. The code weight of the block coding dimming control scheme is variable when the dimming factor is fixed. Thus we can classify it as the variable-weight coding scheme. We can know that compared with the block coding dimming control scheme, the ECWCS scheme has a wider dimming range, a smaller division value, and better error performance.  
The currently existing constant-weight schemes can not provide a constant transmission. All of those schemes have the same code weight when the dimming factor is fixed. However, when the dimming factor varies, the code weight and transmission efficiency will change. Thus it is unfair to compare those schemes with the proposed ECWCS scheme. When we utilize constant-weight codes to realize dimming control with constant transmission efficiency, it will add redundancy compared with the ECWCS scheme. For example, we fix the dimming factor is \(\gamma=3/8\) and the length of the original binary sequence is \(k=2\). The length of the coded binary sequence is \(n=4\) when the ECWCS scheme is utilized, and the length of the coded binary sequence is \(n=8\) when utilizing constant-weight codes.
## 2 Frequency and Active Power Control Schemes
### Alternative aFRR Participation Methods for BESS  
To demonstrate the effectiveness of the proposed control scheme, three alternative methods of BESS participation in the aFRR process are briefly presented in the following section and are further simulated in Section 5.1.
## 3 Method of Sign Word Recognition System
### Hand Segmentation Technique
#### 3.1.2 SkinMask Segmentation  
To detect the hand, we converted the input image into HSV, which contained the hue (H), saturation (S), and value (V) components. The HSV value of each pixel was compared to the pixel quality of the skin and measured in a standard range, which depended on whether the pixel was a skin pixel or the value had a range of predefined threshold values. However, threshold masking was applied to determine which pixels should be presented as its dominant feature. Therefore, we implemented morphological processing (MP), which helped to remove noise and clutter from the image obtained in the output image. The MP created a new binary image in which pixels were only a non-zero value. Then, this method considered the connected region, which ignores small areas that are not possible at all. However, the area of the gesture's skin region was calculated by computing the number of pixels in the skin area of the region. Figure 4 shows the block diagram of the SkinMask process, and Figure 5 shows image preprocessing in a SkinMask segmentation technique.
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 3 System Model
### Link-Level Performance Model  
The link-level performance in this paper is evaluated using the throughput as the performance measure. The Truncated Shannon Bound (TSB) optimally approximates the actual throughput function for an arbitrary set of coding schemes and modulation schemes [23], so the link capacity can be treated as a function of the SINR [24]. The TSB equations approximate the throughput over a channel, with the SINR that the user experiences in reception given as:  
\[Thr_{TSB}=\left\{\begin{array}{ll}\text{Thr}=0&\text{for}\ \ \text{SINR}<\text{ SINR}_{\text{min}}\\ \text{Thr}=\text{B}_{\text{w}}\alpha\log_{2}(1+SINR)&\text{for}\ \ \text{ SINR}_{\text{min}}<\text{SINR}<\text{SINR}_{\text{max}}\\ \text{Thr}=\text{Thr}_{\text{max}}&\text{for}\ \ \text{SINR}>\text{ SINR}_{\text{max}}\end{array}\right. \tag{3}\]  
where \(\text{B}_{\text{w}}\) is the bandwidth and \(\text{SINR}_{\text{min}}\) is a lower limit on the SINR below which throughput is zero and \(\text{SINR}_{\text{max}}\) is an upper limit associated with the throughput of the highest rate coding/modulation. To match the link-level performance, \(\alpha\) has been used [23].
## 3 Proposed Coordinated MPPT Algorithm
The voltage error (\(\mathrm{\Delta V}\)) is the difference between the output reference voltage (\(\mathrm{V_{o,ref}}\)) and the measured voltage (\(\mathrm{V_{o}}\)) of the boost converter. If the power and voltage differences together are greater or equal to zero (\(\mathrm{\Delta P\geq 0}\) and \(\mathrm{\Delta V\geq 0}\)), PI-1 is activated to reduce an error \(\mathrm{e_{1}}\). Then, PI-2 is operated to minimize \(\mathrm{e_{2}}\) error (\(\mathrm{\Delta I=P_{ref}-I_{PV}}\)) and the duty cycle should be increased to produce a constant output power of the converter. The input reference current (\(\mathrm{I_{ref}}\)) and output reference voltage (\(\mathrm{V_{o,ref}}\)) of the boost converter are chosen to be 8.14 A and 325 V for simulation work, respectively. This algorithm can reverse the perturbation when power decreases, and this process repeats until the MPP of the PV module is reached. If the system reaches GMPP, the algorithm produces a steady value of the duty cycle. To increase stability, the oscillation around the MPP of the PV module must be zero, thus the duty cycle perturbation is stopped when \(\mathrm{\Delta P}\) is zero or equal to a present smallest value. The main improvement in this newly proposed method over conventional ones is its convergence and tracking speeds, and power oscillations. The initial input of the PI controllers is the reference voltage (\(\mathrm{V_{o,ref}}\)) at which the system is set to work. Hence, to achieve maximum power at the output, the MPP voltage must be equal to the initial input voltage at which the system must operate. When the system reaches GMPP, the control algorithm maintains the operation of the PV array at this point until there is a change in the operation of the system. The change can cause a change in the output current due to unstable atmospheric conditions and the MPP. This control algorithm is operated to either increase or decrease the reference voltage, to track the new GMPP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters/PSCs**} & \multicolumn{2}{c}{**PI Controller 1**} & \multicolumn{2}{c}{**PI Controller 2**} \\ \cline{2-5}  & \(\mathbf{K_{P}}\) & \(\mathbf{K_{i}}\) & \(\mathbf{K_{P}}\) & \(\mathbf{K_{i}}\) \\ \hline STC & 0.008 & 9.5 & 0.025 & 2.51 \\ Shading pattern 1 & 0.012 & 20.3 & 0.05 & 3.25 \\ Shading pattern 2 & 0.017 & 50.8 & 0.502 & 5.32 \\ Shading pattern 3 & 0.205 & 100.2 & 0.8 & 8.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The tuned parameters of the PI controllers.
## 5 Conclusions  
In development to date, M-MIMO has become the hope for the next generation of advanced wireless communications. In this article, we propose a high-efficiency and low-complexity two-stage receiver called CHSOR to overcome the challenge of M-MIMO technology applied to B5G uplink environments.  
First, we utilize the double refinement feature of SOR and MSOR to provide more accurate initial status and estimated parameters for the next stage. In the second stage, the Chebyshev recurrence relation is used to achieve rapid convergence and higher BER performance estimation. As expected, the first stage facilitates the speed and convergence direction of the second stage.  
Our simulation results show that the proposed CHSOR scheme has the best BER performance compared to previous detectors. In particular, it delivers a 91.624% BER improvement over CSOR, demonstrating superior detection capability. Moreover, thanks to its rapid convergence, the proposed CHSOR scheme does not need to sacrifice complexity for BER performance. At the same time, its computational complexity is independent of the number of receiving antennas; as the antenna ratio increases, not only does its BER performance improve, its complexity decreases instead of increasing, since convergence is accelerated. This is undoubtedly an important benefit for the B5G uplink system, which urgently needs M-MIMO. It is worth mentioning that as the antenna ratio continues to increase, the proposed CHSOR scheme converges to the optimal MMSE baseline in almost one iteration; therefore, we expect that the load of the Chebyshev stage can be significantly reduced.  
In summary, the proposed method uses a double refinement feature and a relatively simple SOR-based architecture. This results in excellent characteristics of high BER performance and moderate computational complexity that make it suitable for application in current and next-generation wireless communication environments and provides a high-performance option with guaranteed convergence for B5G uplink systems.  
Conceptualization, Y.-P.T. and G.-H.L.; methodology, Y.-P.T. and G.-H.L.; software, G.-H.L.; validation, Y.-P.T.; formal analysis, Y.-P.T.; investigation, Y.-P.T.; resources, Y.-P.T. and G.-H.L.; data curation, Y.-P.T. and G.-H.L.; writing--original draft preparation, Y.-P.T. and G.-H.L.; writing--review and editing, Y.-P.T.; visualization, G.-H.L.; supervision, Y.-P.T.; project administration, Y.-P.T.; funding acquisition, Y.-P.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 19: Bar chart of computational complexity for different detectors with \(N_{T}=16\).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data underlying the results are available as part of the article, and no additional source data are required.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 3 Description of UBSI Resource Allocation Protocol
### Protocol Messages and Procedures
#### 3.2.2 Initialization Phase  
The initialization phase is executed in two cases. One is at the beginning of a UCWN. The other is when a UBSC lacks its reserved capacity to provide a UBS with additional backhaul capacity in the normal operation phase, and thus decides to return to the initialization phase. At the beginning of a UCWN, the initial resource to send any message of a UBS is assumed to be provided using a given MAC protocol.  
A UBSC starts this phase by broadcasting an RIREQ message as shown in Figure 3. Any UBS which receives the RIREQ message sends an RIREP message to the UBSC which includes its traffic information (i.e., \(\overline{Q_{i}}\) and \(\overline{P_{i}}\)). If the UBSC receives RIREPs from all UBSs, it determines their backhaul capacities as explained in Section 3.1.2. Then, the UBSC broadcasts an RAIS message which includes the backhaul capacity information of all UBSs and waits for their responses, i.e., RAISACK messages. After receiving the RAIS message, a UBS enters the normal operation phase by sending its uplink traffic to the UBSC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} An acknowledgement of RAACK \\ (used in only NP) \\ (used in only NP) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Messages of the UBSI resource allocation protocol. RIREQ: Resource Information Request; IP: initialization phase; NP: normal operation phase; RIREP: Resource Information Response; RAIS: Resource Allocation Information Send; RAISACK: Resource Allocation Information Send Acknowledgement; RAREQ: Resource Reallocation Request; RAACK: Resource Reallocation Acknowledgement.  
Figure 3: The initialization phase. RIREQ: Resource Information Request; RIREP: Resource Information Response; RAIS: Resource Allocation Information Send; RAISACK: Resource Allocation Information Send Acknowledgement; NP: normal operation phase.  
A UBSC maintains a traffic information table, which includes traffic information of all the UBSs, to utilize it as a reference to calculate their backhaul capacities in the two phases. In addition, a UBS also keeps track of the number of packet drops and the resource utilization values in order to ask the UBSC to adjust its backhaul capacity. The resource utilization of a UBS \(i\) at time \(n\), denoted by \(\rho_{i}(n)\), is the degree of using given backhaul capacity. This quantity is defined as a ratio of the served number of packets to the given backhaul capacity, and its maximum value is one. Thus, it is expressed as  
\[\rho_{i}(n)=\frac{min[\mu_{i},\ Q_{i}(n-1)+\lambda_{i}(n)]}{\mu_{i}}. \tag{6}\]
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
## 2 Materials and Methods
### Analysis of Sulfonamides  
A concentration of 10,000 mg/L sulfonamide antibiotic stock solution was prepared by weighting 0.5 g of a sulfonamide antibiotic and dissolving it in a solvent of methanol: acetonitrile = 1:1 in a dosage bottle to 50 mL. The standards of 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50 and 100 mg L\({}^{-1}\) were obtained by serial dilution with a solvent of methanol: acetonitrile = 1:1 and analyzed by HPLC. Calibration curves of the three sulfonamide antibiotics were computed by linear regression. The detection limits for SMX, SDM, and SMZ were 0.1 mg L\({}^{-1}\). The 2, 10, and 20 mg L\({}^{-1}\) working solutions used in experiments were obtained by dilution of the stock solution using a solvent of methanol: acetonitrile = 1:1. Sulfonamides were extracted and analyzed as previously described [11]. Briefly, the 0.5-mL aqueous phase samples were collected, and equal volumes of extraction solution (containing water with 0.1% formic acid: acetonitrile: methanol = 10:3:1) were added, mixed, and centrifuged at 10,000 rpm for 5 min. The supernatant was filtered with a 0.22-mm filter into a vial. Samples were extracted twice, and the products were pooled for HPLC analysis. Sulfonamides were analyzed using an Agilent 1260 HPLC system equipped with a 4.6 \(\times\) 250-mm column (Zorbax Eclipse Plus C18, Agilent) and a photodiode array detector monitoring at 270 nm. The remaining percentage \(R_{p}\) is computed by the formula: \(R_{p}=(RC_{SA}/IC_{SA})\times 100\%\), where \(RC_{SA}\) is the residue sulfonamide concentration. \(IC_{SA}\) is the initial sulfonamide concentration. The sulfonamide degradation rate constants (\(k\)) and half-lives (\(t_{1/2}\)) were estimated by a linear regression equation (a first-order decay model): \(t=-\ln(C/C_{0})/k\), where \(C_{0}\) is the initial concentration, \(C\) is the substrate concentration, \(t\) is the time period, \(t_{1/2}\) is the half-life, and \(k\) is the degradation rate constant [12].