esa-sceva/satcom-chunk-collection huggingface dataset:
# Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks
## 3 System Model
### Energy Consumption Model  
We should incorporate receiving and sending energy for IoT devices when calculating IoT device energy usage. Let \(ETrans(n,d)\) denote the cost of transmitting \(n\) bits of data over a distance of \(d\) meters and \(ERecv(n)\) denote the cost of receiving \(n\) bits of data over the same distance [59].  
For transmitting \(n\) bits:  
\[E_{Trans}(n,d)=\begin{cases}E_{Embb}*n+E_{Amp}*n*d^{2},&d\leq d_{0}\\ E_{Embb}*n+E_{Amp}*n*d^{4},&d>d_{0}\end{cases}\]  
For receiving \(n\) bits:  
\[E_{Recv}(n)=E_{Embb}*d. \tag{1}\]  
The energy spent by IoT device in sleeping state is:  
\[E_{sleep}(t)=E_{low}*t. \tag{2}\]  
where \(E_{low}\) is the energy consumption of any device in sleep mode for one second. The total time consumed in sleep mode is \(t\) seconds.  
Any IoT device in the network consumes a total energy of:  
\[E_{Total}=E_{Trans}(n,d)+E_{Recv}(n)+E_{sleep}(t). \tag{3}\]
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 4 Results
### Simulation Experiments
#### 4.1.2 Convergence of the Algorithm  
As shown in Table 1, the Zubal thorax dataset is divided according to sampling time. The 20 and 30 min scanning datasets belong to the training set, which contains 108 PET images and the corresponding sinogram images. The 40 min scanning data were chosen as the testing set, including 54 image pairs. The entire network was trained for 120 iterations, and the convergence curves are shown in Figure 7. The left curve shows the convergence of the discriminator, and the right curve represents the convergence of the generator. The  
Figure 5: Reconstruction results comparison of whole-image discriminator and Patch-GAN: (**a**) bias; (**b**) variance. The bias and variance of whole-image strategy are much lower than Patch-GAN of the later frames, as shown between the dashed blue line.  
Figure 6: The images generated in training phase under two different strategies. The first column shows the real brain images generated with PatchGAN. The second column shows the brain images generated with the whole-image method. From left to right: the images generated in the 2 nd epoch, 20 th epoch, 40 th epoch, 60 th epoch, 80 th epoch, and 100 th epoch.  
x-axis indicates the loss value, and the y-axis indicates the iteration step. Both the generator and discriminator converge to a value quickly and tend to be stable.
## 4 Experiments and Discussion
### Dataset  
This study conducts experiments on three datasets, including the Amazon Review dataset [43], the multilingual SLU dataset [44], and the XGLUE news classification dataset [45]. These datasets were selected based on criteria including the diversity of language pairs, the variety of application domains, and their extensive usage in previous studies, which collectively enable direct comparisons with state-of-the-art methods. Table 1 summarizes the statistical characteristics of these datasets. The CLTC task is then performed on each dataset.  
**Amazon Review Dataset** (Amazon Reviews: [[https://webis.de/data/webis-cls-10.html](https://webis.de/data/webis-cls-10.html)]([https://webis.de/data/webis-cls-10.html](https://webis.de/data/webis-cls-10.html)) accessed on 12 May 2023). This multilingual sentiment classification dataset comprises customer reviews in four languages (English, German, French, and Japanese) collected across three domains, Books, DVDs, and Music, thereby reflecting authentic real-world customer feedback scenarios. For the sentiment classification task, the original 5-point rating scale has been transformed into binary classification labels (positive/negative) using a threshold of 3.  
**Multilingual SLU Dataset** (Multilingual SLU: [[https://nlpforthai.com/tasks/slu/accessed](https://nlpforthai.com/tasks/slu/accessed)]([https://nlpforthai.com/tasks/slu/accessed](https://nlpforthai.com/tasks/slu/accessed)) on 12 May 2023). This intent classification dataset comprises twelve distinct intent types across three languages (English, Spanish, and Thai). It provides multilingual data that highlight the inherent challenges in accurately interpreting user intent within diverse linguistic contexts.  
**XGLUE News Classification Dataset** (XGLUE News: [[https://microsoft.github.io/XGLUE/accessed](https://microsoft.github.io/XGLUE/accessed)]([https://microsoft.github.io/XGLUE/accessed](https://microsoft.github.io/XGLUE/accessed)) on 12 May 2023). This multilingual news text dataset comprises articles in five languages (English, French, Russian, German, and Spanish), with each article assigned to one of ten distinct content categories. It serves as a challenging benchmark for cross-lingual news classification by encompassing a wide array of languages and diverse topical domains.  
It is worth noting that this study uses only the source language English as training data and evaluates the model on target languages, including German, Spanish, Russian, Thai, Japanese, and French.
## 2 Modeling Fault Detection and Fault Correction Processes  
### Basic Assumptions of Existing NHPP SRGMs  
NHPP is used to describe the failure phenomenon during the testing process. The counting process \(\{N(t),t\geq 0\}\) of an NHPP process is given as follows.  
\[\Pr\{N(t)=k\}=\frac{\left[m(t)\right]^{k}}{k!}\mathrm{e}^{-m(t)},k=0,1,2\ldots \tag{1}\]  
The mean value function (MVF) \(m(t)\) can be expressed as follows.  
\[m(t)=\int_{0}^{t}\lambda(s)\mathrm{d}s \tag{2}\]  
where \(\lambda(s)\) is the fault intensity function.  
Most existing SRGMs based on NHPP have the following basic assumptions concerning the software fault detection process:  
1. The software failures' occurrence and faults' removal follow NHPP;
2. The software failure intensity at any time is proportional to the number of remaining faults presented at that time;
3. The detected faults are immediately removed with certainty and correction of faults takes only negligible time.  
According to the above assumptions, the general NHPP model can be proposed by solving the following equation:  
\[\frac{\mathrm{d}m(t)}{\mathrm{d}t}=b(t)[a(t)-m(t)] \tag{3}\]  
where \(a(t)\) is the total fault content function, \(m(t)\) is the mean number of detected faults and \(b(t)\) represents the fault detection rate.
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 4 Discussion  
The PSPSO showed very competitive results with the other known variants of PSO in both search and computational time. The PSPSO outperformed CPSO, WICPSO, ScPSO, and PSO on all function properties except non-scalable functions. Although PSOCO's search for the global optimum was better than the other variants, PSOCO had the worst computational time, which at times was twice the time of a standard PSO due to its multiple crossover operation, which is computationally taxing. The PSPSO, on the other hand, computed faster compared to other variants. This may have been due to the algorithm's freedom to direct the particles. Combining this concept with a crossover with another particle would make the particles move more intelligently on the solution space. This suggests that PSPSO can be an alternative to PSO and its variants.
## 4 Conclusions and Outlook  
This work offers a comprehensive experimental study of the steam-oxygen gasification of sewage sludge for synthesis gas production in a 20 kW fluidized bed plant. The experimental results can be utilized for process design and to set-up and validate a gasifier model, since product gas, char, tar and impurity yields as well as their dependencies on the operation conditions are presented.  
Since the gas yield was enhanced and the tar yield decreased with increasing gasification temperature it can be concluded, that the gasification temperature should be chosen as high as practically feasible (i.e., 850 \({}^{\circ}\)C to 900 \({}^{\circ}\)C) taking into account the ash melting behavior.  
The H\({}_{2}\)/CO-ratio can be controlled by altering the steam to carbon ratio \(n_{\text{SC}}\) as steam promotes the water gas shift reaction. It could be shown that the H\({}_{2}\)/CO-ratio could be tailored to 2 (e.g., for methanol or DME-production) or 3 (for methanation) with a reasonable range of \(n_{\text{SC}}\).  
In the investigated process, the majority of the ash is received as bed ash. Hazardous cadmium, mercury and thallium, contained in the sewage sludge, is evaporated in the gasification process, therefore the bed ash was found to be free of or very lean in those elements, which is beneficial for the ash application for fertilizer production. Since the sewage sludge ash contains calcium, the majority of the sludge's sulfur is bound in the bed ash. By adding limestone to the gasifier this sulfur capture is enhanced and the H\({}_{2}\)S and COS concentrations in the syngas are lowered.  
Overall, the steam-oxygen gasification proved to be an efficient and technically feasible process for sewage sludge treatment. The process can play a role in closing the loop for carbon by converting the organic fraction of sewage sludge to carbon-containing bio-fuels or chemicals as well as in closing the loop for phosphorous that can be recovered from the heavy metal lean ash. Therefore, in future, this process can be considered as an alternative to fluidized bed incineration.  
Conceptualization, experimental work, data evaluation, writing--original draft: M.S.; measurement and analysis methodology: M.S. and S.H.; review and editing: M.S., S.H., G.S. All authors have read and agreed to the published version of the manuscript.  
The work was funded by the federal state of Baden-Wurttemberg in the framework of the BW-PLUS program (BWB 15004).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The experiments in this work were carried out within the Res2 CNG project at IFK University of Stuttgart. The authors gratefully acknowledge the cooperation with the project partners and the financial support from the federal state of Baden-Wurttemberg. Furthermore the practical support of lab engineer [PERSON] and the whole IFK-DEU department is acknowledged.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Exponential-Saturation Model for \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\)  
To quantify how \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\) varies with lay length, we employ an exponential saturation model of the form:  
\[y\ =\ c\ +\ a\left(1-e^{-b\,x}\right), \tag{5}\]  
where \(y\) denotes \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\), \(x\) is the lay length \(\lambda\), and \(c\), \(a\), and \(b\) are constants. Physically, we require \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\to 1\) as \(\lambda\to 0\), so \(c=1\). Fitting our simulation data yields (6).  
\[\frac{R_{\mathrm{AC}}}{R_{\mathrm{DC}}}\ =\ 1+0.7157\left(1-e^{-6.3873\,\lambda}\right) \tag{6}\]  
Equation (6) implies that as \(\lambda\) becomes infinitely large, \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\) asymptotically approaches a value of 1.7157.  
Figure 9 illustrates this model fit. The green curve represents the exponential saturation relationship, while the markers show the finite-element simulation data. The near-complete overlap between the curve and the data points indicates a high coefficient of determination (\(R^{2}\approx 0.9964\)). A noteworthy verification step involved excluding the \(\lambda=0.4\,\mathrm{m}\) data point during the curve-fitting process used to obtain the exponential-saturation relationship in (6). After determining the best-fit parameters without relying on that data point, we then evaluated how well the resulting model predicted the omitted \(\lambda=0.4\,\mathrm{m}\) case. Specifically, the finite-element simulation yielded \(R_{\mathrm{AC}}/R_{\mathrm{DC}}=1.659\), while the fitted equation gave a value of 1.6604. The absolute difference is thus 0.0014, which translates to a discrepancy of only 0.084%. Such a small error, even with the intentional exclusion of that point from the fitting procedure, illustrates the robustness and strong predictive capability of the model.  
Figure 9: Ratio \(R_{\mathrm{AC}}/R_{\mathrm{DC}}\) vs. lay length (\(\lambda\)) with exponential-saturation fit (green curve). The data points are extracted from the 3D FE simulations.
# Application of Simultaneous Symmetric and Cambered Airfoils in Novel Vertical Axis Wind Turbines
## 4 Results and Discussion
### Overall Performance Analysis  
The performance analysis is summarized in Table 2. HT-S815 with the highest self-starting ability has a self-starting of 40% higher than that of HT-NACA0018. On the other hand, HT-NACA0018 has a power coefficient of about 506% higher than the power coefficient of HT-S815. A preferred turbine is one with a high-power extraction and self-starting capability. NSA-2 has a self-starting ability the same as HT-S815 in about 66.7% of one cycle. For the self-starting ability, if the wind turbine can be held at suitable angles (\(-100^{\circ}<0<140^{\circ}\)), NSA-2 is a desirable turbine. Moreover, this novel VAWT, NSA-2, in one cycle (360\({}^{\circ}\)) has a self-starting of 13.3% higher than HT-NACA0018.  
Although NSA-1 and NSA-2 can increase the power coefficient up to 329.1% and 347.9% compared to the power coefficient of HT-S815, in design TSR, their power coefficient is only 29.2% and 26.1% lower than the power coefficient of HT-NACA0018, respectively, while the power coefficient of HT-S815 is 83.5% lower than that of HT-NACA0018. On the other hand, [PERSON] et al. [34] showed 16.4% power improvement without considering the self-starting ability by modifying the trailing edge to a wave-like form. Since the current study is simple and more cost-effective and its improvement is also higher than the study of [PERSON] et al., it can be concluded that the current improvement is a remarkable achievement.  
The results can be compared with a hybrid rotor as another novel work, such as a combination of Savonius and Darrieus rotors that has become popular recently. However, although these hybrid rotors can increase the self-starting ability compared with the Darrieus rotor, their static torque coefficient is less than that of Savonius rotors. Additionally, the main drawback of the hybrid rotors is their power coefficient, which is even less than that of drag-based VAWTs by approximately a factor of 4.  
The simplicity of the novel turbine, NSA-2, keeps the capital cost of the turbine low while the overall performance of the turbine is improved. Since no advanced control system is added, the turbine is user-friendly. Then, this novel turbine is recommended for remote areas as well as for disadvantaged communities. It can be used for either off-grid or on-grid systems.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 3 CDL: Cooperative Downlink Listening
### Partitioning of Device Address Block  
In order to address the downlink limitation of LoRa standard, cooperative downlink listening (CDL) is proposed. First, we modify the DevAddr used in the standard, as shown in Figure 6. During the 'join' process, DevEUI is used but the network server assigns a logical network address (DevAddr) and after the join procedure, all communications are based on the assigned DevAddr.  
Figure 5: Device time synchronization in LoRa standard.  
As shown in Figure 6, the standard DevAddr is divided into Nwkdd field and NwkAddr field. Without changes in the NwkId field, we partition the NwkAddr field into Group Addr field and NwkAddr field. It is important to note that unlike the other id field, the Group Addr field is represented not by a numeric number but by a bitmap. The Group Addr field is variable from 8 to 13 bits and the rule is defined by network server. The Group Addr field can also be divided into _Type of Sensors_ (TOS) field and _Region_ field. TOS is used to define the type of sensors equipped in an end device. For instance, Temperature, Humidity, Infrared, Light sensors can be represented respectively as follows: '1000', '0100', '0010', '0001'. Representation of multiple sensors is also available. The bitmap '1100' means temperature and humidity sensors. In addition, since LoRa can have long coverage, a single LoRa BS or LoRa Gateway can cover more than a few kilometers. Therefore, the divided subareas can be represented in the region field in the Group Addr. For example, as shown in Figure 6, the area which is covered by a single gateway is divided into four subareas such as A, B, C, and D. So, the information of subareas can be represented as follows: '1000', '0100', '0010', and '0001'. Based on the type of sensors and regional information, Group Addr is determined and then NwkAddr is arbitrarily assigned by network server.  
Compared with DevAddr address system, since the NwkAddr field is reduced by adding the Group Addr field, the address space to be assigned in CDL is smaller than those of standard. That is, the address space is calculated by  
\[\text{Address space}\ =\ 2^{32-(N_{n}+N_{t}+N_{t})} \tag{1}\]  
where \(N_{n}\) is NwkId size, \(N_{t}\) is sensor type size, and \(N_{r}\) is region size.  
Let us suppose that a device transmits its sensed data to the network server once an hour. Then, each node needs at least three seconds' slot duration (including uplink, ACK, and downlink data slots), and thus only 3600 s/3 s = 1200 nodes can be covered by a single gateway in the area. So, the maximum number of address space of CDL is 4096 and the space is sufficient for a single gateway.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 2 System Model
### Signal Model  
Denote by \(a_{u}\) and \(b_{k}\) the data symbol that legacy satellite user \(u\) transmits and the data symbol that IoT user \(k\) transmits, respectively. Symbol \(a_{u}\) is spread by the spreading code \(\mathbf{s}_{u}\), and symbol \(b_{k}\) is spread by the spreading code \(\mathbf{t}_{k}\). The spreading codes \(\mathbf{s}_{u}=[s_{u,1},s_{u,2},\cdots,s_{u,N}]^{T}\) and \(\mathbf{t}_{k}=[t_{k,1},t_{k,2},\cdots,t_{k,N}]^{T}\) satisfy the power constraints, i.e., \(\mathbb{E}[\|\mathbf{s}_{u}\|^{2}]=P_{u}\), \(\mathbb{E}[\|\mathbf{t}_{k}\|^{2}]=\tilde{P}_{k}\) with the covariance of \(\mathbb{E}[\mathbf{s}_{u}\mathbf{s}_{u}^{H}]=\frac{P_{u}}{N}\mathbf{I}_{N}\), \(\mathbb{E}[\mathbf{t}_{k}\mathbf{t}_{k}^{H}]=\frac{P_{k}}{N}\mathbf{I}_{N}\), where \(P_{u}\) and \(\tilde{P}_{k}\) are the maximum transmit power of the \(u\)-th legacy satellite user and the \(k\)-th IoT device, respectively.  
The total received signal at the satellite receiver corresponding to the \(n\)-th spreading code can be written as,  
\[y_{n}=\sum_{u=1}^{U}h_{u}s_{u,n}a_{u}+\sum_{k=1}^{K}f_{k}t_{k,n}b_{k}+z_{n}, \tag{4}\]  
for \(n=1,\cdots,N\), where \(z_{n}\) is the additive white Gaussian noise with \(z_{n}\sim\mathcal{CN}(0,\sigma^{2})\). For simplification, the received signal (4) can be rewritten as  
\[\mathbf{y}=\mathbf{H}_{1}\mathbf{h}\circ\mathbf{c}_{1}+\mathbf{H}_{2}\mathbf{ f}\circ\mathbf{c}_{2}+\mathbf{z}, \tag{5}\]  
where \(\mathbf{H}_{1}=[\mathbf{s}_{1},\mathbf{s}_{2},\cdots,\mathbf{s}_{U}]\) is \(N\times U\) matrix formulated by the spreading codes of legacy satellite users, and \(\mathbf{H}_{2}=[\mathbf{t}_{1},\mathbf{t}_{2},\cdots,\mathbf{t}_{K}]\) is \(N\times K\) matrix formulated by the spreading codes of IoT users, \(\mathbf{h}=[h_{1},h_{2},\cdots,h_{U}]^{T}\) is \(U\times 1\) vector whose entries are the channel response of the legacy satellite users, \(\mathbf{f}=[f_{1},f_{2},\cdots,f_{K}]^{T}\) is \(K\times 1\) vector composed by the channel responses of the IoT users, \(\mathbf{c}_{1}=[a_{1},a_{2},\cdots,a_{U}]^{T}\) is \(U\times 1\) vector whose entries are the data symbols the legacy satellite users transmit, \(\mathbf{c}_{2}=[b_{1},b_{2},\cdots,b_{K}]^{T}\) is \(K\times 1\) vector composed by the data symbols the IoT users transmit, \(\mathbf{z}=[z_{1},z_{2},\cdots,z_{N}]^{T}\) is \(N\times 1\) additive white Gaussian noise vector, i.e., \(\mathbf{z}\sim\mathcal{CN}(0,\sigma^{2}\mathbf{I}_{N})\).
## 3 Composite Separator Design
### Preliminarily Design of Basic Dimensions  
The application site for the designed composite hydrocyclone separator was a shallow seabed natural gas hydrate solid-fluidization mining tool, which has stringent size requirements: The outer diameter of the separator body must not exceed 70 mm. Therefore, the separator body was designed with an outer diameter of 70 mm. Its basic structure is shown in Figure 3. To reduce energy consumption and mitigate the turbulence impact, a tangential inlet was chosen for the separator. The inlet size, based on the processing capacity, was selected as a rectangular inlet measuring 13 mm by 9 mm.  
Given that the target separation particles were sediment particles with diameters ranging from 1 to 100 \(\upmu\)m, which belong to the fine-grain separation category, the diameter of the overflow pipe would typically be chosen to be 0.2-0.3 times the separator diameter. In this study, the preliminary design of the composite separator specified an overflow pipe diameter of 14 mm. According to findings in the literature [10], when the cylindrical section of a separator is about 120 mm, it can maintain high-stress values on the cementing bonds for a longer duration while ensuring good separation efficiency. This configuration yields the best de-bonding effect for GHBS particles in a classic hydrocyclone separator. Therefore, the cylindrical section length of the preliminarily designed composite separator was set to 120 mm.  
With the length of the cylindrical section determined, the main parameter of the helical flow channel was the pitch. Since the helical flow channel converted the axial force of the fluid into tangential velocity, a smaller pitch resulted in a smaller flow channel cross-sectional area and a higher fluid tangential velocity, thereby increasing the centrifugal  
Figure 3: Structure diagram of the new composite separator.  
Figure 2: Structure of the composite separator. (**a**) Diagram of the structure of the composite separator and the internal particle motion. (**b**) Diagram of the helical flow channel structure in the composite separator.  
force on the particles. Typically, the pitch of the helical flow channel is chosen to be 0.133-0.25 times the diameter of the separator. In this study, the pitch of the helical flow channel for the preliminarily designed composite separator was set to 20 mm.  
Based on research results from the literature [10], a classic hydrocyclone separator can achieve the maximum de-bonding effect when the cone angle is 15\({}^{\circ}\). At this angle, the stress value on the cementing bonds at the separator inlet is maximized, while the axial velocities of the particles do not suffer significant losses. Therefore, the preliminary design of the composite separator adopted a cone angle of 15\({}^{\circ}\). The preliminarily selected structural parameters for the tangential inlet composite hydrocyclone separator are shown in Table 2.