esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cost-Optimized Model for Dynamic Port Hinterland
## 3 Methodology
### Methodological Proposal
#### 3.2.4 Time Management Cost (_TMC_)  
The final component of Total Logistics Cost (_TLC_) relates to time value in logistics operations. This temporal dimension encompasses three distinct elements.  
The first element concerns transit time. Increased transit duration represents a cost that can be quantified as cargo value depreciation. This is particularly relevant for perishable goods, where time directly impacts product value.  
The second element addresses just-in-time delivery scheduling rather than transit duration. Transit time itself does not generate costs if cargo maintains value, making any duration acceptable within schedule parameters. This factor minimally impacts route and hinterland selection, except for potential risk-related disruptions.  
The third element relates to route frequency, which was considered in Warehouse Costs.  
Delivery speed only represents a cost factor when cargo value depreciates or warehouses risk stock-outs. A University of Cadiz study at Port of Algeciras demonstrated this concept, where non-perishable containers averaged 6.5-day departure times as cargo owners strategically used the terminal's grace period for temporary storage, reducing warehouse space requirements [83].  
Focusing on the first component related to transit time and cargo value depreciation, we can formulate the time management costs in logistics operations. Building upon the foundational work of [PERSON] et al. [24] and incorporating studies in time-sensitive logistics [21], _TMC_ can be expressed as:  
\[TMC=VAL\cdot\mu\cdot\tau, \tag{13}\]where  
* _VAL_ represents the cargo value;
* \(\tau\) denotes the transit time;
* \(\mu\) is the adjusted loss factor of cargo value.  
This formula, while generally applicable, requires modification when analyzing perishable merchandise. Recent studies have highlighted the increasing importance of time-sensitive logistics, particularly in the context of e-commerce and fresh food supply chains [84]. For perishable goods, the _TMC_ can be more accurately represented as:  
\[TMC=VAL\cdot\mu\cdot\frac{\tau}{LFT}\cdot\frac{365}{LFT}, \tag{14}\]  
where _LFT_ refers to the life span of the analyzed product.  
This approach reflects contemporary research on perishable goods logistics, acknowledging product-specific depreciation rates [56] and extending beyond food to include time-sensitive items like fashion and electronics [85]. Advanced analytics and machine learning now enable more precise product-specific depreciation predictions and dynamic _TMC_ calculations [86], enhancing logistics cost optimization models.
## 1 Introduction
### Extraction of Individual Discontinuity Using the HDBSCAN Algorithm  
In this section, the extraction of individual discontinuity within each discontinuity set is accomplished through the utilization of clustering algorithms. Considering the inability to determine the number of individual discontinuities within each discontinuity set and the requirement of a predetermined number of clusters for K-means or FCM, it is deemed inapplicable. Currently, DBSCAN, a prominent density clustering algorithm, is widely used to extract individual discontinuities from discontinuity sets, necessitating input parameters: search radius (\(\varepsilon\)) and minimum number of points (\(m_{pts}\)) to establish a density threshold for distinguishing discrete points. Additionally, variations in dataset density can affect the stability of this algorithm. [PERSON] et al. [52] proposed the HDBSCAN algorithm, which combines hierarchical clustering with DBSCAN, providing enhanced stability and better clustering results for data with uneven density. This algorithm relies on a single key parameter, the minimum cluster size (_minCluster_), and its performance is not sensitive to the specific value chosen for this parameter. The algorithm defines the mutual reachability distance between two points as follows:  
\[d_{mreach-k}(x_{p},x_{q})=\max\bigl{\{}core_{k}(x_{p}),core_{k}(x_{q}),d(x_{p},x_{q})\bigr{\}} \tag{10}\]  
where \(core_{k}(x_{p})\) and \(core_{k}(x_{q})\) denote the distances from points \(x_{p}\) and \(x_{q}\) to their nearest \(k\) neighbors, while \(d(x_{p}\), \(x_{q})\) represents the distance between the points \(x_{p}\) and \(x_{q}\). The minimum spanning tree is generated based on the mutual reachability distance, and a  
Figure 8: Results of discontinuity sets extraction. (**a**) Assigned colors to each discontinuity set; (**b–d**) selected discontinuities for parameter calculations.  
hierarchical structure is constructed. Subsequently, the hierarchical structure is recursively split, removing noise clusters with sizes smaller than _minCluster_. This process accomplishes the compression of the hierarchical structure and the extraction of clusters.  
In addition, the parameter _minDis_ is used to eliminate extremely small clusters in the extraction results as noise, thereby further enhancing the accuracy of discontinuity extraction. For case A, after testing, parameters _minCluster_ = 5 and _minDis_ = 200 were chosen to successfully extract individual discontinuities (Figure 9):
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 1 Introduction
### Manchester Receivers in the Literature  
While the Manchester OOK transmitter is easy to implement, the receiver is less straightforward and requires a bit more of electronics. The Manchester receivers present in the literature (see Table 1) typically share the same front-end based on a two-level analog-to-digital (AD) converter, realized by comparing the input signal to a low-pass filtered replica [16]. The works present in the literature are mainly focused on the Clock Data Recover (CDR) circuit that follows the two-level AD converter. The most used approaches are based on a Phase-Locked Loop (PLL) or, alternatively, exploit the so called \"blind oversampling\".  
In the first approach, a PLL is synchronized to the incoming signal and used to generate a clock with a phase suitable to sample the data [16; 17]. This approach can achieve very high frequencies, and it is used in Application-Specific Integrated Circuits (ASICs) (for example, Ethernet receivers). On the other hand, it is not compatible to implementation in the general fabric architecture of a FPGA.  
On the other hand, the \"blind oversampling\" method is often the preferred approach for FPGA implementation. An unsynchronized clock, whose frequency is at least two times the bit rate [18], samples the data with different phases. A following digital logic locates the edges and reconstruct the bits [19; 20; 21; 22; 23; 24; 25]. The performance of this method depends on the oversampling ratio and/or the number of phases of the clock, and is quite sensitive to jitter [26].  
The aforementioned methods can be used for rates in the order of Gb/s. For lower rates, such as those at the reach of microcontrollers (uC), the Manchester code is typically decoded by counting the time intervals between edges of the incoming signal by the means of uC peripherals timers [10]. This approach is quite simple, works for rates up to some hundreds of kb/s, but has limited immunity to noise.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **AD Conv.** & **Rates** & **Device** & **Resources** & **BER** & **Ref.** \\ \hline PLL & 1-bit & \textgreater{}1 Gb/s & ASIC & Low & Medium & [16; 17] \\ \hline blind ‘oversampling’ & 1-bit & \textgreater{}1 Gb/s & FPGA, ASIC & Medium & Medium & [19; 20; 21; 22; 23; 24; 25] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Manchester receiver methods.
## 2 Materials and Methods
### Characterization  
Fourier-transform infrared spectroscopy (FT-IR) was performed on a Nicolet iS5 FT-IR Spectrometer. Elemental analyses were performed on a CHNS-O analyzer from Thermo Scientific and results are given as average of three independent measurements.  
The average particle size of CDs was measured by DLS using a VASCO KIN Particle Size Analyzer apparatus (Cordouan Technologies, Pessac, France). All measurements were performed on freshly prepared suspension diluted in ultrapure water, at 25 \({}^{\circ}\)C and in triplicate. Data were analyzed using the multimodal number distribution Nano Kin(r) software supplied with the instrument and expressed as mean (\(\pm\)SD).  
UV-vis spectra were obtained with a Varian Cary(r) 50 UV-Vis spectrophotometer. Fluorescence emission spectra were obtained using a FluoroMax-3 spectrofluorophotometer (Horiba Jobin Yvon) at 298 K.  
SEM images were acquired on a TSCAN VEGA 3 scanning electron microscope equipped with a tungsten filament electron source operating at 30 kV. Samples were prepared by depositing dry powders on conductive tapes. The conductivity of the sample surface was further improved by a very thin conductive carbon coating to prevent charge build-up on it prior to SEM analysis.  
Transmission electron microscopy (TEM) investigations were carried out using a JEM--ARM 200F Cold FEG TEM/STEM operating at 200 kV and equipped with a spherical aberration (Cs) probe and image correctors (point resolution 0.12 nm in TEM mode). Grids were prepared by placing a drop of GQD solution (200 mg/mL in water) on a carbon-coated copper grid for four minutes. The drop was then removed by mean of a blotting paper and the grid dried at room temperature for five minutes and at 80 \({}^{\circ}\)C for twenty minutes.  
The X-ray photoelectron spectrometry (XPS) analyses were performed on a Kratos Axis Ultra (Kratos Analytical, U.K.). The spectrometer is equipped with a monochromatic Al K\(\alpha\) source (1486.6 eV). All spectra were recorded at a 90\({}^{\circ}\) take-off angle, with an analyzed area of about 0.7 \(\times\) 0.3 mm. Survey spectra were acquired with a 1.0 eV step and 160 eV analyzer pass energy. The high-resolution regions were acquired with a 0.1 eV step (0.05 eV for O 1 s and C 1 s) and 20 eV pass energy. A neutralizer was used to perform the recording to compensate for the charge effects. Curves were fitted using a Gaussian/Lorentzian (70/30) peak shape after [PERSON]'s background subtraction and using CasaXPS software. The carbon C 1s was calibrated at 284.8 eV for C-C and C-H bonds.
# A Complex Network Important Node Identification Based on the KPDN Method
## 5 Results and Analysis
### Analysis of Artificial Networks  
We also checked the performance of the proposed algorithm on synthetic networks with a clear community structure, as shown in Figure 10. The basic configuration obtained by the LFR algorithm is: \(N=2000\), \(t1=3\), \(t2=1.5\), \(mu=0.1\). In order to compare different average degrees, we set the average degree to 7, 8, and 9, respectively. Each column from left to right corresponds to the LFR networks with an average degree of 2.131, 3.448, and 4.7565, respectively. The first row compares the connected coefficient. The second row compares the remaining connected edges. The third row compares subnet sensitivity. From  
Figure 9: Sensitivity values of the 5 newly added datasets of scale \(10^{4}\sim 10^{5}\). The data in (**a**–**e**) correspond to: (**a**) dimas10-astro-ph, (**b**) ca-Astroph, (**c**) gotton-reuters, (**d**) facebook-wosn-links, and (**e**) prosper-loans.  
the figure, we can easily find that the dividing advantage of the method proposed in this paper on the LFR network is still quite obvious.  
Similarly, we compared the small-world network, as shown in Figure 11. The basic configuration is: \(N=2000\), \(<k>\) = 3, 4 and 5, from left to right, the average degree is, as well as the Albert-Barabasi network, as shown in Figure 12. The basic configuration is: \(N=2000\), \(<k>\) = 6, 7, and 8, respectively.  
Through the results shown in Figures 10-12, the advantage of the KPDN method in analyzing important nodes in artificial networks is verified because the best performance is achieved in most cases and under different indicators. Especially for the LFR community networks and Watts-Strogatz small-world networks, we find that as the average degree of the network gradually increases, the effects of the subnet connectivity coefficient and sensitivity indicators are relatively more obvious. Compared with the Barabasi-Albert network, the effects of the three indicators of the proposed method are also in a leading or front position.  
Figure 10: Comparison of LFR community networks, where each row from left to right has an average degree of 2.131, 3.448, and 4.7565, respectively. The first row compares the connected coefficient. The second row compares the remaining connected edges. The third row compares the subnet sensitivity.  
Figure 11: Comparison of small-world networks (average degrees are 3, 4, and 5 from left to right).  
Figure 12: Comparison of Albert–Barabasi networks (average degrees are 6, 7, and 8 from left to right).
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 2 Original Survey  
In 1991, [PERSON] and his team from the National Design Institute for Cultural Heritage carried out a photogrammetric survey of the south wall of the Padise Abbey. As a photogrammetric method he chose stereophotogrammetry. Using a metric camera UMK 6.5/1318, he captured six stereo-pairs. As well as the glass plate negatives, we found only some of the documentation to the photogrammetric survey. From that documentation, we were able to read only partially how the original survey was carried out. Unfortunately, the calibration report of the camera was not found.
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 4 Results
### Results of Spatiotemporal Trend Analysis  
In order to explore the dynamic changes of GDP in time and space, Sen + MK trend analysis was performed in MATLAB (2023b) to obtain the trend changes from 2013 to 2023, and reclassification and map rendering were performed in ArcGIS. The reclassification results were determined based on \(\beta\) and \(Z\) values, that is, based on the slope value of the Sen method and the standard normal test statistic \(Z\) value of the MK method, and the proportion of pixels in each category was counted. Table 4 shows the classification basis of the change trend and the proportion of each type of pixels. Pixels with a significantly increased GDP accounted for 95.57% of the total pixels, pixels with a slightly increased GDP accounted for 2.99%, and pixels with an increased GDP accounted for 98.56%, indicating that the GDP in most areas of mainland China showed an upward trend, and most of them were significantly increased pixels. Pixels with a significantly decreased GDP accounted for 0.27% of the total pixels, pixels with a slightly decreased GDP accounted for 0.72%, and pixels with a basically unchanged GDP accounted for 0.46%.
## 4 Experiments
### Quantitative Comparisons  
#### 4.2.1 Overall Performance  
The CFSA tracker has been quantitatively compared with 9 state-of-the-art trackers and codes of these trackers are publicly available, including CSK [16], KCF [18], TLD [44], SAMF [28], Struck [13], MIL [12], ASLA [45], SCM [46], and DSST [22]. In these trackers, CSK, KCF, SAMF, and DSST are correlation filter based trackers, and TLD, Struck, MIL, ASLA and SCM are representative trackers using single or multiple online classifiers. Moreover, SAMF and DSST trackers pay more attention on the estimation of the target scale. The quantitative comparison results of OPE on OOTB dataset and OTB-100 dataset are shown in Figures 3 and 4, respectively. For success plots, the values in square brackets indicate the area under curve (AUC) value. For precision plots, the values in square brackets represent the scores at error threshold of 20 pixels. It can be observed that the CFSA trackerperforms favorably against other state-of-the-art and competitive trackers. Compared to the original KCF, the CFSA tracker outperforms KCF on the OOTB dataset by 13% in the precision plot and 8.3% in the success plot, respectively. Moreover, the CFSA outperforms both DSST and SAMF on the OTB-100 dataset, giving 6.4% and 1.7% better overlap scores.
## 1 Introduction  
The guided mode resonance (GMR) is a unique resonant phenomenon in which incident waves on a diffraction grating layer are diffracted and converted into a leaky guided mode, depending on conditions such as the wavelength of incident light, incident angle, waveguide thickness, and effective refractive index. Hence, when the conditions are optimized as per the GMR peak, highly resolved resonant peaks and high efficiencies can be achieved. Moreover, depending on the type of GMR sensor, the sensitivity and spectral dynamic ranges of the sensor can be easily customized by controlling the refractive index of the grating and waveguide layers, as determined by the requirements of the applications. Based on these properties, GMR devices have been widely studied for label-free sensing [1; 2; 3], optical band pass filters [4; 5; 6], and communication [7].  
Recently, GMR devices operating at the terahertz (THz) frequency have received much attention for THz bandpass filters [8; 9] and biological- or chemical-sensing applications [10] because a GMR device in the THz range has many advantages. For example, various conformational energies of biochemical sensing molecules can be traced to a specific GMR peak design, based on their fingerprint patterns in the THz region, which can be determined by inter- and intra-molecular energies of rotational and vibrational modes in those frequency ranges. Thus, the molecular dynamics of proteins and drugs and the sensing mechanism of target molecules have been successfully investigated in this range [11; 12; 13; 14; 15; 16]. Second, because of the long wavelengths of THz waves, the GMR sensor can be easily designed and manufactured by using inexpensive processing equipment, compared with those at other frequency ranges, such as visible or infrared. As a result, low-cost and highly sensitive devices based on the GMR sensing platform in the THz range can be applied to various smart packaging fields.  
Here, we report on the theoretical simulation of the GMR device in the THz region, using a finite-difference time-domain (FDTD) method to investigate its sensing properties toward highly sensitive and low-cost sensing platforms. We focus on dielectric materials based on a freestanding GMR sensing platform and modulate the structural geometry and the refractive index of the grating layer. Accordingly, we propose an inexpensive freestanding THz GMR sensing device with a high-quality factor (Q-factor) for various biochemical-sensing applications.
# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 2 Theory and Method
### Using the CycleGAN Model for Data Augmentation
#### 2.1.1 CycleGAN Model Structure  
The CycleGAN model uses two generators and two discriminator networks based on GAN to achieve mutual mapping between two image datasets, X and Y. The CycleGAN structure is a circular network composed of two symmetric GANs, as shown in Figure 2.  
The model has two pairs of GANs that have a generator and discriminator. The generators are used for style transferring between optical images and sonar images. The optical image \(C\) is taken as input, and the generator \(Gc\) converts \(C\) into a pseudo-sonar image \(Gc(C)\). Then, the generator \(Gs\) converts \(Gc(C)\) into a pseudo-optical image \(C^{\prime}\). Symmetrically, the optical image S is taken as input, and the generator Gs converts S into a pseudo-optical image \(Gs(C)\). Then, the generator \(Gc\) converts \(Gs(C)\) into a pseudo-sonar image \(S^{\prime}\), while the discriminator is used to distinguish between real and generated pseudo images (\(Gc(C)\) and \(Gs(S)\)).  
The goal of CycleGAN is to minimize the difference between the generated image and the real image to maintain consistency between \(S\) to \(S^{\prime}\) and \(C\) to \(C^{\prime}\) in image style transferring, so a cycle-consistency loss is introduced. Therefore, the \(Loss\) of the CycleGAN model consists of adversarial loss \(Loss_{GAN}\) and cycle-consistency loss \(Loss_{cycle}\) as follows:  
\[Loss=Loss_{GAN}+Loss_{cycle}. \tag{1}\]
## 4 Illustrated Case Study
### Crash Classification Results  
Crashes on the TMC segments were classified using the methodology described Section 3. Figure 7 shows the contour map for one of the crashes classified \"Case 1--Crash not in congested conditions\". The number of Case 1 crashes was 419 out of 500 (i.e., 84%).  
In Step 3, the remaining 81 crashes were further analyzed. Of them, 62 were classified as belonging to Case 2 (a Case 2 is one that occurs in non-recurrent congestion). Panels (a) and (b) of Figure 8 show a crash that was identified as belonging to Case 2.  
Figure 8e shows an instance where the Case 3 classification was based on the CI contour map. Figure 8f presents an instance where the Case 3 classification was based on the AHCI contour map.  
The remaining nine crashes were passed to Step 4. Of these, nine had an AHCI value of more than 60% and were placed in Case 3. Panels (c) and (d) of Figure 8 show an example of a TMC\((AHCI(t)\geq 60\%)\) that was identified as belonging to Case 3. The remaining 10 crashes were identified as being moved on to the sub-test in Step 4 and were subsequently also classified as Case 3.  
The results of the crash classifications are summarized in Table 2. As can be seen, the proportions of cases 1, 2, and 3 were 84%, 12%, and 4%.  
Figure 7: Schematic representative a Case 1 crash condition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **I-40** & **Westbound** & **Eastbound** & **Total** \\ \hline \multirow{3}{*}{Step 2} & Number of crashes & 234 & 266 & 500 \\  & Case 1 (crash not in congestion) & 192 (82\%) & 227 (85\%) & 419 (84\%) \\ Step 3 & Case 2 (crash in non-recurrent congestion) & 36 (15\%) & 26 (10\%) & 62 (12\%) \\ Step4 & Case 3 (crash in recurrent congestion) & 6 (1\%) & 13 (3\%) & 19 (2\%) \\  & To algorithm for congestion type classification & 3 (1\%) & 7 (3\%) & 10 (2\%) \\  & Case 2 (crash in non-recurrent congestion) by the sub-test & 0 (0\%) & 0 (0\%) & 0 (0\%) \\  & Case 3 (crash in recurrent congestion) by the sub-test & 3 (1\%) & 7 (3\%) & 10 (2\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of the crashes based on the proposed methodology.  
Figure 8: Examples of TMc classified as Cases 2 and 3 in Steps 3 and 4: **(a)** A TMc classified as Case 2 in CI (congestion); **(b)** A TMc classified as Case 2 in AHCI (non-recurrent); **(c)** A TMc classified as Case 3 in CI (congestion); **(d)** A TMc classified as Case 3 in AHCI (recurrent); **(e)** A TMc classified as Case 3 in CI (congestion) by the sub-test; **(f)** A TMc classified as Case 3 in AHCI (recurrent) by the sub-test.