esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### CuBTC@PET Characterization Analysis
#### 3.2.2 CuBTC@PET Thermal Stability Analysis  
Figure 12 shows the thermogravimetric-differential thermal curves before and after hydrophobic modification. From the figure, we can see that the thermogravimetric-differential thermal curves of CuBTC@PET composite fibers before hydrophobic modification have  
Figure 11: XRD spectrum at different immersion times; (**a**) SCP, (**b**) CuBTC@PET.  
Figure 10: Water contact angles: (**a**) SCP, (**b**) CuBTC@PET.  
the following phases: the quality of the fibers remains stable at 0-100 \({}^{\circ}\)C, and there is no trend of quality loss; the low-temperature dehydration phase occurs at 100-200 \({}^{\circ}\)C, which is mainly due to the adsorption of water molecules in the pore structure of CuBTC, and the water molecules adsorbed in the pore structure of CuBTC are removed by evaporation in the heating; the preliminary thermal decomposition phase occurs at 200-300 \({}^{\circ}\)C. At 300 \({}^{\circ}\)C, the preliminary thermal decomposition stage, the mass loss in this stage is mainly the breakage of PET chains and the thermal decomposition of the CuBTC organic ligand BTC [34]; at the 300-400 \({}^{\circ}\)C thermal stabilization zone, in which CuBTC shows relatively high thermal stability, further decomposition or desorption of small molecules of the remaining organic components in the framework occurs. Above 400 \({}^{\circ}\)C, the complete thermal decomposition stage, the PET with the CuBTC begins significant thermal decomposition, leading to the collapse of the structural framework. By comparison, it can be concluded that the TG-DTG curves of SCP fibers are more stable and fluctuation-free before 350 \({}^{\circ}\)C. In summary, the POTS coating effectively prevents CuBTC from adsorbing water in the air, prevents its hydrolysis under high-humidity air filtration conditions, and effectively improves the thermal stability before 350 \({}^{\circ}\)C.
## 4 Rigid Robotic Exoskeletons for Hand Rehabilitation
### Actuation System  
Rigid hand exoskeleton devices mainly rely on electric motors for actuating human hand joints. Several types of actuators can be found in the literature, i.e., DC motors, linear actuators, servo motors, and brushless DC motors. DC motors are the most widely used due to their ease of use, backdrivability, and low torques at high speeds. Therefore, these types of motors are accompanied by a gearbox to increase the torque by reducing the motor speed [24; 28; 34; 35; 36; 38; 46; 47; 50]. Linear actuators are another type of system employed in rigid hand exoskeleton actuation units [22; 23; 25; 29; 32; 40; 51; 52]. This kind of technology consists of DC motors connected to a lead screw assembly which allows the rotational motion to be converted into linear motion. Linear actuators are typically used in combination with the four-bar linkage mechanisms, enabling the structure to realize both flexion and extension motions of the actuated joint. Other works present rehabilitative hand devices that employ brushless DC motors. These motors offer advantages with respect to standard DC motors in terms of size, noise, and efficiency [53; 54].  
In other applications, servo motors are used to actuate hand exoskeletons [37; 41; 42; 47]. These types of motors are limited geared DC motors that are controlled via pulse width modulation signals. They are very popular due to their easy control, and compared to DC motors, they do not suffer from the backdrivability problem.  
Overall, selecting a suitable motor for the exoskeleton actuation unit always involves a trade-off among different criteria and depends on specific requirements in terms of the force, range of motion, power consumption, and weight characteristics that the device should satisfy.  
Figure 3: Examples of rigid hand exoskeletons. (**a**) Modular exoskeleton for finger flexion/extension movement support, actuated by linear actuators [32]. (**b**) Hand exoskeleton for assistive and rehabilitative purposes developed by [PERSON] et al. [44]. (**c**) Rigid hand exoskeleton proposed by [PERSON] et al. [37].
# Evaluation of Waste Blends with Sewage Sludge as a Potential Material Input for Pyrolysis
## 3 Results and Discussion
### Paper Rejects  
For all samples containing paper rejects, there is a typical weight loss in the temperature range of approx. 250-350 \({}^{\circ}\)C [56].  
The TG curves of the LDPE:SS:PR samples (3:1:1 and 4:2:1) are shown in Figure 11. The TG degradation of LDPE is dominant for these samples [50,53]. The course of the TG curves is similar, and the residual mass is very similar. Higher humidity was observed in the sample LDPE:SS:PR = 3:1:1 (greater mass loss up to 100 \({}^{\circ}\)C).  
The TG curves for the PES:SS:PR and RDF:SS:PR samples are similar up to a temperature of approx. 300 \({}^{\circ}\)C (Figure 12). The sample containing PES decomposes at two temperatures for a temperature range of approx. 300-500 \({}^{\circ}\)C, while the RDF sample decomposes in a narrow temperature range of 400-500 \({}^{\circ}\)C.  
The shape of the TG curve of 3 RDF:SS:PR is similar to the 3 LDPE:SS:PR sample (Figure 13). They also have similar weight losses at similar decomposition temperatures.  
Figure 10: TG/DTG curves of RDF:SS and TP:SS in 2 different volume ratios.  
Figure 9: TG/DTG curves of LDPE:SS in 2 different volume ratios.  
Figure 11: TG/DTG curves of LDPE-SS:PR in 2 different volume ratios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Waste** & **Volume** & **Tmax** & \begin{tabular}{c} **Mass Change** \\ **at Tmax** \\ \end{tabular} & \
\begin{tabular}{c} **T1** \\ **Change at T1** \\ \end{tabular} &
\begin{tabular}{c} **Mass** \\ **Rate at T1** \\ \end{tabular} \\ \hline  & & \({}^{\circ}\)**C** & **\%** & \({}^{\circ}\)**C** & **\%** & **\%/min** \\ \hline TP:SS & 3:1 & 998.7 & 74.0 & 418.2 & 67.0 & 10.9 \\ LDPE:SS & 4:1 & 998.8 & 73.7 & 473.4 & 72.7 & 18.9 \\ LDPE:SS & 6:1 & 998.9 & 70.2 & 474.2 & 67.8 & 16.2 \\ RDF:SS:PR & 3:1:1 & 998.9 & 64.9 & 459.1 & 32.6 & 6.7 \\ LDPE:SS:PR & 3:1:1 & 998.9 & 63.0 & 466.4 & 39.4 & 10.3 \\ LDPE:SS:PR & 4:2:1 & 998.8 & 62.2 & 469.5 & 48.1 & 9.9 \\ PES:SS:PR & 3:1:1 & 999.0 & 59.8 & 360.6 * & & 2.6 \\ PES:SS:PR & 3:1:1 & 999.0 & 59.8 & 410.2 * & 39.7 & 2.7 \\ RDF:SS & 1:3:1 & 998.8 & 56.7 & 461.9 & 31.7 & 5.6 \\ \hline \hline \end{tabular} Note.* Thermal decomposition within the range 250–500 \({}^{\circ}\)C occurred at two temperatures (Figure 13  
\end{table}
Table 2: TGA/DTG results of waste blends.  
Figure 12: TG/DTG curves of PES:SS:PR and RDF:SS:PR in the same volume ratio.  
The weight loss rate (%/min) is highest in the order LDPE:SS > 3 TP:SS > LDPE:SS:PR. The lowest weight loss rate is for 3 PES:SS:PR. These values are important for comparison with future semi-pilot plant pyrolysis experiments.
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 3 Materials and Methods
### AKF Based on MC and OAGS
The three indicators are normalized and utilized as input to the sigmoid function, which is defined as follows:  
\[h(\mathbf{y})=\frac{1}{1+e^{-\alpha(\mathbf{\theta}^{T}\mathbf{y})+\beta}} \tag{12}\]  
This function gives the relationship between the confidence of position \(h(\mathbf{y})\) and the three indicators (denoted as vector \(\mathbf{y}\)). \(\mathbf{\theta}\) is a weighted vector, and \(\alpha\) and \(\beta\) are linear transformation parameters. Based on the observation that the matching coefficient is positively correlated with the confidence of the matching result, while the latter two indicators are negatively correlated with it, the parameter \(\mathbf{\theta}\) is set as \([1,-1,-1]\), the scaling factor is set to 10 to effectively amplify the effect of \(\mathbf{\theta}\), and the bias term \(\beta\) is set to 0. This configuration is fixed through all our experiments. Based on the evaluation of position observation confidence, the OAGS is formulated as below:  
\[\hat{K}=h(\mathbf{y})K \tag{13}\]  
\[\hat{\mathbf{p}}_{t}=\overset{\vee}{\mathbf{p}}_{t}+\hat{K}(\mathbf{Z}-H\mathbf{p}_{m}) \tag{14}\]  
\[\hat{E}_{t}=\big{(}I-\hat{K}H\big{)}\overset{\vee}{E}_{t} \tag{15}\]  
Figure 5: Motion compensation. where \(K\) represents the original Kalman gain, the position-related components of which are adaptively adjusted based on the confidence of the position observation, denoted as \(h(\mathbf{y})\). Consequently, the updates to the position state \(\hat{\mathbf{p}}_{t}\) and the covariance matrix \(\hat{E}_{t}\) are modified accordingly, as shown in Equations (14) and (15). Equations (13) and (14) show that the filter places greater reliance on the observation when the confidence is high, while reducing the influence of the erroneous position observation when the confidence is low.
# Crack Detection of Concrete Based on Improved CenterNet Model
## 4 Experiment and Result Analysis
### Data  
The experimental dataset in this study consisted of 3000 crack pictures captured via the UAV, which were divided into training and test sets at a 9:1 ratio.  
In the pre-processing stage, part of the training set was augmented to improve the generalization ability of the algorithm model. The image transformation method adopted in the dataset enhancement was still close to the tunnel crack image collected after image processing, including random brightness transformation, random horizontal flipping, and random vertical flipping. The transformation results after processing are shown in Figure 7.  
The image was scaled and standardized before being input into the network. The widths and heights of the scaled images were 512. The mean values of the standardized RGB three-channel were 123.675, 116.28, and 103.53, and the standard deviation was 58.395, 57.12, 57.375.  
CenterNet determines the target's location by predicting the target centre point, target centre point bias, and target size. Therefore, the corresponding labels of the image include the target centre-point Gaussian heat map, target centre-point bias, and target size, which are represented by a tensor of the same size as the network output.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Index** & **Implication** \\ \hline FLOPs & The number of floating-point operations used to measure the computational complexity of the model \\ FPS & The number of images the algorithm processes per second—the higher the value, the faster the algorithm processes \\ P & The size of the video memory occupied by the algorithm in the inference stage—the smaller the video memory occupation, the fewer resources are required \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation index and meaning interpretation.
# A Novel Connectivity-Based LEACH-MEEC Routing Protocol for Mobile Wireless Sensor Network
## Proposed Framework
### Cluster-Leader Election  
In our proposed algorithm, the CL selection process starts with setup phase, where in previous studies CL selection for the first round is carried out as described in [13; 16; 34; 35; 39]. During the first round, a mobile sensor node selects a random value between 0 and 1. The selected value should be less than the defined threshold value \(O_{thr}(x)\) calculated by Equation (16).  
\[O_{thr}(x)=\begin{cases}\frac{p}{1-p*(umod(\frac{1}{p}))},&\text{if }n\in G\\ 0,&\text{otherwise},\end{cases} \tag{16}\]  
where \(p\) replicates the required percentage of CLs, \(n\) reflects the number of nodes, \(u\) is the present round and \(G\) is the set comprising certain nodes that have not been nominated as CL from last \(1/p\) rounds. After the election of CL, it broadcasts announcement messages to all sensor nodes in neighborhood to become member of its cluster.  
From second phase onwards, the CL selection is carried out based on two metrics, remaining energy and connectivity. If a mobile sensor node \(N_{i}\) has maximum remaining energy and maximum connectivity within neighboring nodes, it will be elevated for CL selection process. In this paper, we consider connectivity of neighboring nodes within \(R_{max}\) radio range for mobile LEACH routing protocol, which, to the best of our knowledge, is a novel approach for mobile LEACH, where CL is selected based on \(CV_{i}\) value. All nodes within \(R_{max}\) radio range are compared by their \(CV_{i}\) values. The one node with maximum value of \(CV_{i}\) is selected as CL. \(CV_{i}\) is calculated by Equation (17).  
\[CV_{i}=E_{Rem}(N_{i})+PC(N_{i}) \tag{17}\]  
Hence, the proposed CL selection process is energy efficient, since nodes located outside radio range cannot become part of cluster. The new CL then creates TDMA schedules to accommodate all new sensor nodes and broadcasts it to all cluster nodes in the neighborhood. In steady-state phase, mobile member sensor nodes in a cluster transmit data to CL by turning on their radios in respective  
Figure 2: Connectivity model diagram for LEACH-MEEC protocol.  
time slots. CL transmits the collected data from cluster nodes to the BS. This procedure is repeated until all nodes are dead. Steady-state phase is beyond the scope of this paper.  
Figure 3 explains a flow diagram of the proposed work. It includes mobile sensor nodes distribution, dataset generation, setup phase (CL selection phase) and steady state phase. This process will repeat until all nodes are dead.
###### Abstract  
The mining of deep coal resources occurs in a high-stress geological environment as well as an engineering environment of rock excavation and unloading. Research on the re-bearing capacity characteristics and damage mechanism of rock masses damaged by peak front unloading is critical in revealing the destabilization and rupture law of deep rock bodies. The triaxial pre-peak unloading point was controlled to prepare damaged sandstone specimens, and the RMT-150C rock mechanics test loading system and the AEwin USB-type acoustic emission monitor were used to perform uniaxial reloading tests on the pre-peak unloading-damaged sandstone and to monitor the acoustic emission signals during the rupture process. Among them, the peak front unloading point was set to 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, and 90% of the peak strength at 10 MPa of the surrounding pressure for a total of 11 working conditions. The test results show that: (1) The degree of unloading before the peak controls the uniaxial reload deformation characteristics of sandstone. The higher the unloading point, the faster the deformation of the rock sample, even directly into the crack instability extension stage, and the sandstone deformation characteristics transform from plastic--elastic to elastic--viscous. (2) The cumulative energy characteristics of the 40% to 60% sandstone at the unloading point are comparable to those of the complete sandstone and are separated into smooth, steady growth, and secondary smooth phases. The acoustic emission energy characteristics of the 65% and 70% sandstone at the unloading point are mostly focused on during the crack expansion stage. The sandstone's acoustic emission energy characteristics exhibit a \"double peak\" occurrence at 75% of the unloading point. The cumulative energy characteristics of the 80% to 90% sandstone at the unloading point reveal a \"stepped\" rise. (3) Sandstone's pre-peak unloading rupture morphology influences the reload damage characteristics: 40% to 70% of the specimens at the unloading point exhibit \"Y\"-type double-slope shear damage features. The predominant cause of specimen damage in 75% of the specimens at the unloading point is secondary primary cracks based on the pre-peak tensile rupture pattern. The damage path of 80% to 90% of the specimens at the point of unloading occurs in shear damage along the pre-peak unloading rupture pattern. (4) A closed crack mechanics analysis model under uniaxial reload was established, and the basic solution of pseudo-force for fine microcracks subjected to far-field stress, the stress intensity factor at the crack tip, and the crack fracture angle were theoretically derived. Furthermore, the relationship between the fracture angle \(\theta\) of rock compression-shear cracks, the crack angle \(\beta\), and the friction coefficient \(f\) at the crack surface was clarified.  
unloading damage; uniaxial reloading; mechanical properties; fracture volume strain; failure mechanism +
Footnote †: journal: Applied Sciences
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 4 Coherent SC Sources Based on ChG Fibers
Consequently, highly coherent SCs can be obtained from such fibers. Coherent SCs based on ChG fibers with ANDDi profiles have been reported in several experiments and simulations [168; 140; 143; 169]. Liu et al. [168] used a ChG MOF with a core material of AsSe\({}_{2}\) and four air holes replaced by As\({}_{2}\)S\({}_{5}\) glass rods to engineer the dispersion to ANDi. A coherent SC extended to 3.3 \(\upmu\)m was generated in a 2-cm-long, all-solid MOF pumped by a 2.7 \(\upmu\)m laser with a pulse duration of 200 fs. The tapering of ChG fibers can be used as an alternative method to achieve ANDi profiles [153; 170]. A 3-mm-long As\({}_{2}\)S\({}_{3}\) microwire, coated with PMMA, was tapered to a diameter of 0.58 \(\upmu\)m, and exhibited normal dispersion at all wavelengths. When pumped with a 1550 nm laser with a pulse duration of 590 fs, the generated SC covered more than an octave of bandwidth, from 960 nm to \(>\)2500 nm, but the coherence property was not discussed in the literature [69]. Liu et al. [171] numerically investigated the coherence properties of MIR SC generation in tapered AsSe\({}_{2}\)-As\({}_{2}\)S\({}_{5}\) ChG fibers pumped at a wavelength of 4 \(\upmu\)m with a pulse duration of 500 fs. Figure 11 shows the spectrum evolutions and the corresponding degrees of coherence in two tapered fibers with different structures. It can be seen that the coherence of SC was decreased when the fiber ZDW hit the pump wavelength where solitons were generated, implying that soliton dynamics and noise amplification through modulation instability are the major reasons for SC decoherence. Another approach for generating coherent and recomressible SCs is the suppression of soliton fission in fibers with two closely-spaced ZDWs centered near the pump [172]. Diouf et al. [173] designed a solid core MOF consisting of a three-ring, triangular lattice with some air holes in an As\({}_{38.3}\)Se\({}_{61.2}\) glass host. The MOF with an air-hole diameter of 0.9 \(\upmu\)m had two ZDWs: \(\sim\)3.25\(\upmu\)m and \(\sim\)4.25 \(\upmu\)m. The study's numerical results show that a coherent SC with a spectral bandwidth of \(>\)2 \(\upmu\)m was generated when 50 fs pulses were pumped at 3.7 \(\upmu\)m, but the coherence was not as high as that from the fibers with ANDi profiles. This result can be attributed to modulation instability which plays a role in the anomalous dispersion regime. Despite the lack of extremely high coherence, the SCs were successfully applied in coherent anti-Stokes Raman scattering microscopy [174]. Reducing the distance between two or more ZDWs can effectively eliminate modulation instability during nonlinear processes and subsequently generate highly coherent SC sources.
## 3 Proposed Forest Sensors and Communication Technology
### LoRa Communication Technology and Its Respective Sensors  
In most areas without a 3G/4G telecommunication signal, the proposed LoRa signal repeat method will continuously return the sensing data of the LoRa-support solar micro-weather station. Figure 4 presents the proposed LoRa-support solar micro-weather station and the architecture of the LoRa signal repeat method, which is described in detail as  
Figure 3: Architecture of NB-IoT communication technology for returning the sensing data.  
Figure 2: Two communication technologies, NB-IoT and LoRa, combined with their respective sensors. (**a**) NB-IoT communication technology and its respective sensors. (**b**) LoRa communication technology and its respective sensors.  
follows. First, the gateway is set up in a place with electricity, such as a street lamp or public facility at the foot of a mountain, and is supplemented by an external battery that can be charged at any time to achieve uninterrupted power and avoid an unstable power supply in mountainous areas, as shown in Figure 5. Next, the LoRa-support solar micro-weather station is set up in a place that needs a forest resource survey or monitoring, and then the related sensing data are transmitted through LoRa communication technology. Whether the transmitted signal strength is sufficient to be received by the gateway is evaluated by the RSSI. The signal strength \(p_{d}\) according to the distance (\(d\)) can be represented by:  
\[p_{d}=p_{0}-10n\log_{10}(d) \tag{1}\]  
or:  
\[d=10^{\frac{p_{0}-p_{d}}{10n}} \tag{2}\]  
where \(p_{0}\) is the strength of the transmission signal measured at a distance of one meter from the transmitter; \(d\) is the distance between the transmitter and receiver; and \(n\) is a signal attenuation constant that can be obtained from the received signal and the actual distance.  
Different levels of signal strength can reduce the location errors of the RSSI caused by the interferences. In order to determine the signal repeat location of a specified sensor, we defined the indication function by:  
\[I(d)=\left\{\begin{array}{ll}1,&\text{if }d\geq\varepsilon\\ 0,&\text{otherwise}\end{array}\right. \tag{3}\]  
where \(\varepsilon\) denotes the threshold for which the receiver can receive the signal from the transmitter.  
If the RSSI determines that the signal strength is not high enough to send the sensing data back to the gateway using (2) and (3), it is necessary to set up a RoLa signal repeat  
Figure 4: Architecture of LoRa signal repeat method for returning the sensing data of the LoRa-support solar micro-weather station.  
Figure 5: The gateway device (indicated by the yellow arrow) has an external battery (indicated by the red arrow) that can be charged at any time.  
station powered by solar energy. Finally, the gateway sends the received sensing data back to the server or computer through the 3G/4G telecommunication network.