esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
To demonstrate the theory above, a time-bin phase encoding intrinsic-stabilization QKD experimental setup is built. The schematic setup of the QKD system is shown in Figure 4a. In the setup, the transmitter Alice encodes the key information randomly into a phase basis \(\{0,\pi\}\) and a time-bin basis, and sends quantum pulses to the receiver [PERSON]. Both [PERSON] and [PERSON] have a Q-M interferometer as the phase encoder and decoder for phases \(\{0,\pi\}\), respectively, as seen in the red dash boxes in Figure 4a, which is similar as that in Figure 3. The stability of the QKD system validates the effectiveness of the Q-M interferometer. The BS\({}_{1}\) and BS\({}_{4}\) are used for time-bin encoding and decoding as seen in the black dash boxes in Figure 4a. A variable optical attenuator (VOA) on [PERSON]'s side is used to attenuate the transmitted light to a single photon lever, the BS\({}_{3}\) on [PERSON]'s side is used for passive basis selection, and a dense wavelength division multiplexer (DWDM) used on [PERSON]'s side is for spectral filtering to reduce the scattered and background noise. The system works in a way of decoy-state BB84 protocol including vacuum and weak decoy states [2; 23]. The geographic distribution of the quantum channel with a standard telecommunication optical fiber is shown in Figure 4b, which is a round-trip loop fiber with the fiber length of 60.6 km and the optical loss of 16.9 dB.  
In the experiment, the photons are generated by four strongly attenuated 1549.32 nm distributed-feedback pulsed laser diodes with a pulse width of 500 ps and 100 MHz repetition rate. The average photon number per pulse is 0.6, including transmitted signal, decoy state, and vacuum state. The vacuum state is generated by not triggering the lasers. The ratio of signal, decoy, and vacuum state numbers is 6:1:1. Four avalanche diode single photon detectors are used on [PERSON]'s side with a gate width of 1 ns.  
With the setup described above, we measure the QBER and safe key rate to examine the performance of the system. As shown in Figure 5, the average QBER and safe key rate are 1.3% and 1.0 kbps during 10 h, respectively. Here, we deal with the data collected from detectors every second as a QBER point in Figure 5a. In Figure 5b, every point represents the average number of the safe keys in one minute. The experimental results indicate that the Q-M interferometer scheme can keep the QKD system working stably.  
Figure 3: Double Q–M interferometers on [PERSON]’s and [PERSON]’s side, respectively, connected with each other by optical fiber quantum channel. LD: laser, Cir: optical circulator, PMC: polarization maintaining coupler, PS: phase shifter, QWPR: quarter-wave plate reflector, SPD: avalanche diode single photon detector, \(L_{i}\) (\(i=a\) or \(b\)): the long arm operator of the Q–M interferometer on [PERSON]’s or [PERSON]’s side, \(S_{i}\) (\(i=a\) or \(b\)): the short arm operator of the Q–M interferometer on [PERSON]’s or [PERSON]’s side.
## 2 Materials and Methods
### Methodology  
The basic methodology adopted for preparing LSM is shown in Figure 2. The initial stage of a susceptibility assessment involves gathering all available information and data about the study area. This stage is crucial as it lays the foundation for further analysis of the relationship between landslide occurrence and conditioning parameters. However, assessing the cause-effect relationships can be challenging since landslides are typically caused by multiple factors. To create a landslide susceptibility map for the Darjeeling region of West Bengal, ten inputs were chosen based on the most significant factors influencing landslides, including slope, aspect, earthquake activity, drainage density, rainfall, land use/land cover (LULC), lineament density, geology, geomorphology, and soil type [12; 29]. Our study expanded upon the number of factor classes considered compared to prior research. We reviewed the literature by other authors and sought to assign non-homogeneous weights to each factor class, in contrast to the uniform weighting used in previous studies. The various layer classes are rated in the range between 0 and 9, where higher ratings represent more decisive landslide influence. After preparing the final LSM, the study area was differentiated into different susceptibility zones, such as low, high, moderate, and very high.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sl. No.** & **Datasets Name** & **Purpose** & **Resolution (m)** \\ \hline
1 & ALOS PALSAR DEM &
\begin{tabular}{c} To prepare aspect, slope, lineament \\ density, drainage density \\ \end{tabular} & 12.5 \\ \hline
2 & Catrosat-2 series (MX) & To extract LULC & 1.6 \\ \hline
3 & CRU & To generate a rainfall map & Numerical \\ \hline
4 & Soil & To generate a soil map & - \\ \hline
5 & Geology and geomorphology & To extract geology and geomorphology map & - \\ \hline
6 & Earthquake data & Earthquake map generations & Point data \\ \hline \end{tabular}
\end{table}
Table 1: Data used for generating landslide conditioning factor.
## 2 Collection and Statistics of Case Histories
### Statistics of Case Histories
#### 2.2.3 Relative Position between the Tunnel and Deep Excavation  
Figure 3 shows a histogram of the relative position between the tunnel and deep excavation. It is observed from Figure 3a that the value of \(H_{\text{t}}/H_{\text{e}}\) used to characterize the vertical position of the tunnel is in the range of 0.4-3.0, in which 0.8-1.0 accounts for the largest proportion, and the tunnels with \(H_{\text{t}}/H_{\text{e}}\leq\) 1.0, 2.0, and 3.0 account for 42.6%, 93.6%, and 100%, respectively. It is observed from Figure 3b that the value of \(L_{\text{t}}/H_{\text{e}}\) used to characterize the horizontal position of the tunnel is in the range of 0-7.0, in which 0.5-1.0 accounts for the largest proportion, and the tunnels with \(L_{\text{t}}/H_{\text{e}}\leq\) 1.0, 2.0, and 3.0 account for 48.9%, 78.7%, and 87.2%, respectively.  
The above statistical results of \(H_{\text{t}}/H_{\text{e}}\) and \(L_{\text{t}}/H_{\text{e}}\) provide data support for the setting of adjacent tunnel positions in the following numerical simulations.  
Figure 3: Histogram of relative position between the tunnel and deep excavation: (**a**) Histogram of vertical tunnel position; (**b**) Histogram of tunnel horizontal position.  
Figure 2: Histogram of deep excavation depth \(H_{\text{e}}\).
## 6 Key Issues and Challenges  
The main aim of the monitoring system for the PV power plant is to transmit the data in a reliable, secure, and efficient manner. However, several issues significantly affect the performance of various monitoring technologies in terms of efficiency, security, range, data processing capability, sampling rate, and signal interference. Some of the identified key issues and challenges are discussed in the following subsections.
## 3 Formulation of the Mathematical Model  
### Pore Size Distribution within Matrix  
Pores in tight gas reservoirs have fractal characteristics that range from a micrometer scale to a nanometer scale [9]. Understanding the pore size distribution is critical to modeling gas flow in shale reservoirs. Previous studies have found that the pore size distribution of a porous media can be mathematically obtained using fractal theory [32]. The number of pores of a radius that is greater than a certain value \(r\) is given by:  
\[N(L\geq r)=\Big{(}\frac{r_{\rm max}}{r}\Big{)}^{D_{f}}, \tag{3}\]  
where \(r_{\rm max}\) is maximum pore radius in a shale reservoir. \(D_{f}\) is the pore area fractal dimension, \(1<D_{f}<2\), and is given by [9]:  
\[D_{f}=\xi-\frac{\ln\varphi}{\ln\Big{(}\frac{r_{\rm min}}{r_{\rm max}}\Big{)}}, \tag{4}\]  
where \(\varphi\) is the effective porosity of a shale reservoir, \(r_{\rm min}\) is minimum pore radius of a shale reservoir, \(\xi=2\) in two dimensions, and \(\xi=3\) in three dimensions.  
Figure 2: Initial and boundary conditions and flowchart of calculation procedures.  
From Equation (3), we can see that there is a relationship between pore number and pore radius. Furthermore, the total number of pores within a shale reservoir can be obtained by [33]:  
\[N(r\geq r_{\text{min}})=\bigg{(}\frac{r_{\text{max}}}{r_{\text{min}}}\bigg{)}^{D _{f}}. \tag{5}\]  
By differentiating Equation (3), the number of pores with a pore radius between \(r\) and \(r+dr\) can be defined as follows:  
\[-dN=D_{f}r_{\text{max}}^{D_{f}}r^{-(D_{f}+1)}dr. \tag{6}\]  
Furthermore, the frequency of pores with a pore radius between \(r\) and \(r+dr\) is found by combining Equations (5) and (6):  
\[-\frac{dN}{N(r\geq r_{\text{min}})}=D_{f}r_{\text{min}}^{D_{f}}r^{-(D_{f}+1)} dr=f(r)dr, \tag{7}\]  
where \(f(r)=D_{f}r_{\text{min}}^{D_{f}}r^{-(D_{f}+1)}\) stands for the probability density function, which satisfies the following condition:  
\[f(r)\geq 0. \tag{8}\]  
Based on probability theory, the probability density function \(f(r)\) also satisfies the relationship as follows:  
\[\int_{0}^{\infty}f(r)dr=\int_{r_{\text{min}}}^{r_{\text{max}}}f(r)dr=1-\bigg{(} \frac{r_{\text{min}}}{r_{\text{max}}}\bigg{)}^{D_{f}}\equiv 1. \tag{9}\]  
Clearly, the prerequisite for the availability of Equation (9) is that:  
\[\bigg{(}\frac{r_{\text{min}}}{r_{\text{max}}}\bigg{)}^{D_{f}}\cong 0. \tag{10}\]  
In general, Equation (10) is valid if \(r_{\text{min}}/r_{\text{max}}\leq 10^{-2}\)[28]. For shale reservoirs, the maximum pore radius is far greater than the minimum pore radius. Thus, fractal theory is a valid method for defining the pore size distribution of shale reservoirs.
# An Energy-Efficient and Obstacle-Avoiding Routing Protocol for Underwater Acoustic Sensor Networks
## 3 Models
### Underwater Acoustic Channel Model  
The Thorp propagation model [12] is used to describe the underwater communication, and the underwater acoustic channel gain is given as:  
\[h=\frac{1}{A_{0}d^{k}\alpha\left(f\right)^{d}} \tag{1}\]  
where \(A_{0}\) is a normalization constant and \(d\) is the distance between the current node and neighbor nodes of it. The geometry of propagation is described using the spreading factor \(k\). In the proposed protocol, \(k\) is given as 1.5, and \(f\) is the signal frequency with unit kHz. The absorption coefficient \(\alpha\left(f\right)\)[26], in dB/km for \(f\) in kHz, is described by the [PERSON]'s formula written by:  
\[\alpha(f)=\left\{\begin{array}{c}\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{41 00+f^{2}}+2.75\times 10^{-4}f^{2}+0.003,\text{if }f\ \geq 0.4\\ 0.002+0.11\frac{f^{2}}{1+f^{2}}+0.011f^{2},\text{if }f\ <0.4\end{array}\right. \tag{2}\]  
Figure 1: 3D underwater acoustic network model.  
The signal-to-noise ratio (SNR) [26], which is:  
\[SNR(d,\ f)\ =\ \frac{P_{s}}{A\ (d,f,\ k)\cdot N(f)} \tag{3}\]  
\[\text{and}\ A(d,f,\ k)=\ d^{\ k}\cdot\left(10^{\frac{\alpha(f)}{100}}\right)^{ \frac{d}{1000}},\]  
where \(d\) represents the distance from the current node to the neighbor node and k is the spreading factor which equals to 1.5. \(P_{s}\) is the transmit power of a sensor node. \(N(f)\) is the total noise which contains four kinds of noise in the acoustic channel. These four kinds of noise are turbulence \(NSL_{I}(f)\), shipping \(NSL_{2}(f)\), wind noise \(NSL_{3}(f)\), and thermal noise \(NSL_{4}(f)\), respectively. The total noise and the four kinds of noise are defined in [27; 28] as follows:  
\[N(f)_{dB}=NSL_{1}(f)+NSL_{2}(f)+NSL_{3}(f)+NSL_{4}(f), \tag{4}\]  
\[10 logNSL_{1}(f)=17-30\log(f),\]  
\[10 logNSL_{2}(f)=40+20(s-0.5)+26\log(f)-60\log(f+0.03),\]  
\[10 logNSL_{3}(f)=50+7.5\sqrt{w}+20\log(f)-40 log(f+0.4),\]  
\[10 logNSL_{4}(f)=-15+20 log(f)\]  
where \(s\in(0,1)\) and \(w\) is the wind velocity which ranging from 0-10 m/s, respectively, and \(f\) is the signal frequency (Unit: kHz).
# Analysis of Building Platform Inhomogeneities in PBF-LB/M Process on Alloy 718
###### Abstract  
Additive Manufacturing (AM) processes, particularly PBF-LB/M, are considered advantageous due to their flexibility, which allows process engineers to design and fabricate intricate structures both in the prototyping and component manufacturing phases. It is well known that the behavior of the process directly impacts the quality of the materials and thereby induces inhomogeneities on the powder bed on the building platform. Several parameters can be tuned to keep the process under control, getting rid of process uncertainty and distinguishing aspects of a specific machine model. Such behavior requires an extended analysis of the powder bed inhomogeneities and the definition of limits in the printing process. In this work, carried out on Alloy 718 specimens printed using an EOS M290 machine, the inhomogeneities of the melt pool stability, density, and material properties were investigated based on three main factors: the amount of area melted or fused, the gas flow speed setpoint, and the location on the building platform. The test results for Track Stability, melt-pool shape, and porosity analysis show that criticality occurs when more than 50% of the building platform is exposed. This can be partly fixed by raising the differential pressure value.  
additive manufacturing; PBF-LB/M; Alloy 718; building platform; melt-pool shape; single track analysis Article
## 2 Materials and Methods
### Characterization of Solid and Liquid Products  
Raw materials and hydrocarbons obtained from HTC runs were characterized in terms of proximate analysis and higher heating value (HHV).  
Proximate analysis were carried out by a LECO Thermogravimetric Analyser TGA 701. Approximately, 400-500 mg of solid samples were used to evaluate moisture content (MC), volatile matter (VM), ashes (Ash), and fixed carbon (FC) according to the following a thermal program: (1) 5 \({}^{\circ}\)C/min ramp to 105 \({}^{\circ}\)C in air until constant weight (\(<\)\(\pm\)0.05%) for MC calculation; (2) 16 \({}^{\circ}\)C/min ramp to 900 \({}^{\circ}\)C, hold time 7 min in N\({}_{2}\), to determine the VM; (3) natural cooling down to 500 \({}^{\circ}\)C in N\({}_{2}\); (4) 30 \({}^{\circ}\)C/min ramp in air to 800 \({}^{\circ}\)C and isothermal until constant weight to determine ash content. Fixed carbon was evaluated by difference.  
Higher heating values of raw and treated feedstock were evaluated according to the CEN/TS 14918 standard by means of a LECO AC500 calorimeter.  
The energy densification ratio (EDR) and the energy yield (EY) of hydrocarbons were determined via Equations (1) and (2), respectively:  
\[EDR(\%)=(HHV_{HClb}/HHV_{Rdb})\times 100 \tag{1}\]  
\[EY(\%)=MY\cdot EDR \tag{2}\]  
where \(HHV_{HClb}\) and \(HHV_{Rdb}\) are the higher heating values of hydrochar and raw LP (on dry basis), respectively; \(MY\) is the hydrochar yield computed as mass of dry hydrochar collected to the mass of initial dry feedstock.  
Process water collected after each HTC test was characterized in terms of pH and TOC. The pH of liquid samples was measured using a XS instruments Bench pH-meter. TOC content was measured by a Shimadzu Total Organic Carbon Analyser (liquid samples were previously filtered through 0.45 \(\upmu\)m syringe filters).  
Figure 2: Flow diagram of HTC experimental procedure and HTC products characterization.
## 4 Experimental Results and Discussions
The \(x\)-axis is scaled in millimeters. cracks of length 9-11 mm decreased. In the process FT60 (Figure 10c), the number of cracks of length 0-2 mm significantly increased from 45 to more than 130, and those of length 2-3 mm nearly doubled, whereas the number of cracks of higher length slightly decreased. In the case of FT90 (Figure 10e), the number of tiny cracks doubled, medium cracks of length 2-7 mm strongly decreased, and a single crack of length 14 mm was formed. In the case of FT120, shown in Figure 10g, the number of cracks of length up to 9 mm increased from 20 to 40, and two more cracks of length 17 and 19 mm were formed. The trends regarding FT150 and FT90 are similar concerning the disappearance of small cracks, as confirmed by their KDEs. In the FT1 process (Figure 10b), the number of cracks of 0-5 mm and 10-16 mm slightly increased, while the number of cracks of medium length decreased. FTC2 (Figure 10d) and FTC3 (Figure 10f) increased the number of cracks, excluding several in the 8-14 mm range and 2-3 mm, respectively. The process FTC4 (Figure 10h) increased the number of cracks by 50%, whereas FTC5 tripled the number of small cracks and formed several 12-18 mm cracks, as shown in Figure 10). Notice that the length of a crack was determined by considering its thickness; see Section 4. Generally, LN\({}_{2}\) changes the physical and mechanical properties of coal specimens, and the damage is more significant as LN\({}_{2}\) treatment increases. The effects of thermal stress and temperature gradients in a porous coal sample are complex. The different phases present respond to thermal stress with different magnitudes and rates. Elements with large defacement are firmly pressed, while parts with low deformation are extended by force, resulting in unequal strain. Another critical factor is moisture after the method of freezing-thawing cycle LN\({}_{2}\) treatment. The greater the internal water content, the more precise the effect of freezing and thawing damage on the coal sample. During the extension period, the pore wall forms the mechanical action frost heave force, which is much greater than the compressive strength of coal, and the pore walls are pressed through this period, resulting in more damage. This leads to the formation of new cracks and the evolution of pre-existing fissures (permeability increases), while the frost heaves at the tip of fractures, leading to crack extension and, finally, to the fracture network, which is the desired outcome. In the freezing time method, ice is a consolidation agent for coal samples, which prevails over the frost forces that tend to decrease specimen strength. Future research is recommended to focus on exploring various stages of freezing-thawing cycles and developing models to simulate real-world coal bed methane (CBM) reservoirs.
# Preparation of an Asymmetric Membrane from
## 3 Results and Discussion
### Characterization of Cellulose Acetate  
#### 3.2.1 FT-IR  
FT-IR spectroscopy has also been used to examine the acetylated products. The FT-IR spectra of CA-1 and CA-2 are shown in Figure 2, in which five main changes in the cellulose acetate structure are visible. Firstly, a rise of the peaks at 1738 cm\({}^{-1}\) and 1224 cm\({}^{-1}\) assigned to the stretching of C=O ester carbonyl and -CO- bond in the acetyl group, respectively [17], has been observed. A peak at 1369 cm\({}^{-1}\) was observed, attributed to the bending vibration of the C-H bond in the acetyl groups [4]. The decrease of the band at 3400 cm\({}^{-1}\) relates to the replacement of the hydroxyl groups for the acetyl groups in the cellulose structure. The decrease in the intensity of the peak at 1634 cm\({}^{-1}\) indicates that CA represents a hydrophobic nature [7]. The peak at 1032 cm\({}^{-1}\) is characteristic of C-O-C pyranose ring skeletal vibration. The complete removal of acetic acid and acetic anhydride in the CA synthesized was confirmed by the absence of bands at 1760-1840 cm\({}^{-1}\) and 1700 cm\({}^{-1}\)[17].  
Figure 1: Fourier transform infrared spectroscopy (FT-IR) of (**a**) raw sugarcane bagasse (SB) and extracted cellulose from SB: (**b**) cellulose extracted from bleaching with 2% H\({}_{2}\)O\({}_{2}\) (CE-2), (**c**) cellulose extracted from bleaching with 5% H\({}_{2}\)O\({}_{2}\) (CE-1).  
Figure 2: FT-IR of cellulose acetates (CA-1 and CA-2) synthesized from CE-1 and CE-2.
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 4 Experiment and Results
### Dataset and Evaluation Metrics Introduction
#### 4.1.3 Evaluation Metrics  
This study employs standard deep learning evaluation metrics: accuracy, recall, and precision. To elucidate these metrics, we define key terms: False Positive (FP) refers to instances that are actually negative samples but are erroneously classified as positive samples; False Negative (FN) refers to instances that are actually positive samples but are erroneously classified as negative samples; True Positive (TP) represents instances that are actually positive samples and are correctly classified as positive samples.  
Precision is the ratio of the number of samples correctly predicted by the model to be in a particular category to the total number of samples predicted by the model to be in that category:  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
Recall is the proportion of positive samples correctly predicted by the model among all results predicted as positive in the validation set:  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
Average Precision (AP) is a comprehensive evaluation of a model's prediction accuracy and recall across different categories. It assesses the overall performance of an algorithm by computing the average precision across different categories. Firstly, for each category, the detection results are sorted based on their confidence scores. By calculating the precision-recall curve, the average precision (AP) for that category is obtained. Subsequently, the average precision for all categories is summed up and divided by the number of categories to derive the mean Average Precision (mAP). [EMAIL_ADDRESS] denotes the mean average precision when the Intersection over Union (IoU) threshold is set to 0.5. [EMAIL_ADDRESS] represents the average precision across the confidence score thresholds, ranging from 0.5 to 0.95 for the detection results.  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{4}\]  
In the equation, \(N\) represents the number of categories, and \(AP_{i}\) denotes the average precision of the \(i\)-th category.