esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Hydrolysis of Sewage Sludge: A Case Study of a WWTP in Burgos, Spain
## 2 Materials and Methods
### Description of Scenarios  
Five different scenarios were analysed based on electric power generation from biogas, self-consumption of electricity, production of dewatered sludge and polyelectrolyte consumption. Scenario 1 is the reference plant, consisting of conventional mesophilic digestion. Scenarios 2 to 5 evaluate the operation of the thermal hydrolysis reactors.  
Scenario 2 evaluates the WWTP operating with a conventional batch THP (THP-B6.2 Cambi batch cycle), treating primary and secondary sludge. Scenario 3 evaluates hydrolysis exclusively applied to secondary sludge. Scenario 4 studies the performance of the ITHPunit, whereas Scenario 5 analyses the Solidstream(r) process. Figure 1 shows a schematic representation of the five scenarios. The hydrolysis process requires the installation of a post-thickening centrifuge pump before introducing the sludge into hydrolysis reactors. All scenarios included a final dewatering unit for water removal for digested sludge.  
Figure 1: Scheme of the different scenarios. (**a**) Scenario 1: conventional mesophilic digestion process. (**b**) Scenario 2: thermal hydrolysis process (THP) for primary sludge and waste activated sludge (WAS); Scenario 3: THP for WAS. (**c**) Scenario 4: intermediate thermal hydrolysis process (ITHP). (**d**) Scenario 5: the Solidstream® process.  
Dewatering centrifuges produce centrates with high organic content that need this liquid stream for further treatment. Centrates from digested sludge are sent back to the WWTP headline in the case of Scenario 1, where they are treated by nitrification-denitrification via nitrate in the biological reactors. In the case of Scenarios 2, 3 and 4, the centrates are sent to the nitrification-denitrification (via nitrite) section of the plant. In the case of the Solidstream(r) process, the centrates are sent back to the digestion section. This process also requires a barometric egg to control the pressure and temperature of hydrolys sludge, a heat exchanger and an air scrubber for cooling down the sludge stream, and the treatment of the gaseous stream to remove offensive odours.
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
## 3 Research Method
### BLE-Based Method
As the values of cosine similarity have been normalized, the largest value relates to the maximum weight. We calculate the final coordinates with \(k\) candidate points for the current location by WKNN (see Equation (11)). In this way, we can obtain the positioning result from the fingerprinting method. \[ \begin{split} x&=\sum_{i=1}^{k}w_{i}x_{i}\\ y&=\sum_{i=1}^{k}w_{i}y_{i}\\ w_{i}&=\frac{cs_{i}}{\sum_{j=1}^{k}cs_{j}}\end{split}  \tag{11}\]  
**Fusion of the both methods. ** The results derived from trilateration and fingerprinting represent real-time and pre-collected information, respectively. We fuse them to obtain better location estimate to compensate ranging errors of iBeacon RSS. Here, we consider the constraint of a pedestrian's motion scope, which supports us to generate reasonable location estimate. Considering a continuous movement, the last location correlates to the following one. Here, we limit the motion in a reasonable scope in terms of computational frequency of trilateration positioning. For example, 1 m is the scope for the next step for a pedestrian when the frequency of trilateration is 1 Hz. In other words, as the rate of the BLE-derived locations is 1 Hz, we set the threshold of stride as 1 m/s. In this way, after the location estimates of the two methods are generated, we can compute their distances to the last location. Assuming the resulting distance of trilateration is \(d_{1}\) and that of fingerprinting is \(d_{2}\), we list their weights for fusion in Equation (12). A long distance relates to a small weight when the both distances are equal to or larger than 1 m. In contrast, a long distance gains a large weight when \(d_{1}\) and \(d_{2}\) are both shorter than 1 m. Based on the coordinates derived by the both methods, we employ these weights in Equation (12) to obtain the weighted coordinates of the current location. The fused location estimate would be more continuous and involves fewer 'jumps'. \[w_{1}=\frac{d_{2}}{d_{1}+d_{2}},\ \ w_{2}=\frac{d_{1}}{d_{1}+d_{2}},\ \ d_{1} \geq 1,d_{2}\geq 1 \tag{12}\] \[w_{1}=\frac{d_{1}}{d_{1}+d_{2}},\ \ w_{2}=\frac{d_{2}}{d_{1}+d_{2}},\ \ d_{1} <1,d_{2}<1\]
## 4 Genetic Diagnosis of VCP-MSP
### Complementary Testing and Reverse Clinical Correlation  
Our recommendation is to use genetic testing as the means to reach definitive diagnosis. Additional tests such as muscle biopsy, creatine kinase enzyme levels, neurophysiology (nerve conduction testing and electromyography), and skeletal surveys can give supportive information for the diagnosis of VCP-MSP. However, none of these investigations are specific to _VCP_ mutations and, in many cases, typical abnormalities may not be present even in genetically confirmed cases of VCP-MSP [121].  
Muscle biopsy findings associated with VCP-MSP classically show rimmed vacuoles, with ubiquitin positive and tubulofilamentous inclusions on electron microscopy [121]. However, these characteristic findings were seen in only 39% of cases from the largest series of muscle biopsies in VCP-MSP [12]. Nonspecific myopathic changes are frequently identified.  
MRI of the muscles is increasingly used to differentiate underlying causes of myopathy and aid in genetic differential diagnosis [122]. There are a few previously reported cases with muscle MRI findings in VCP-MSP [122; 123; 124] and further study will help to better define the range of findings as well as determining their sensitivity and specificity.  
These tests may be carried out to investigate patient symptoms, but increasingly these may also be performed as screening investigations or as \"reverse clinical correlation\" in patients who have a _VCP_ mutation identified in genetic testing. The former situation is familiar and straightforward, where an undifferentiated patient with myopathy symptoms and signs on examination is referred for a muscle biopsy, or a patient with muscle atrophy and fasciculations is referred for electromyography. When additional testing is requested in a patient already diagnosed with genetic testing, this is often variably based on clinician experience and individual judgement. For example, a patient with myopathy who is found to have a pathogenic _VCP_ mutation on a multi-gene panel may be referred for electromyography, skeletal survey, and MRI of the head, to identify other features in the clinical spectrum of VCP-MSP prior to the development of symptoms. This can have particular value for early identification of PDB, which is responsive to therapy with bisphosphonates [125]. Identification of other features such as ALS may have value for prognostication, or as a baseline investigation that can be a useful reference in case of later clinical deterioration.
## 6 Existing Multi-UAV Communication Architectures
### Multi-Level Communication Architecture  
A multi-layer communication architecture is an extension of the multi-group architecture to a three-tier architecture. It is adapted to support the scalability of UAVs [19]. Unlike the multi-group architecture, the communication between the groups of UAVs is  
Figure 11: Types of intra-UAV communication: (**a**) ring topology, (**b**) star topology, (**c**) meshed topology.  
Figure 12: Multi-UAV communication architecture based on multi-group.  
achieved without using a GCS infrastructure [28]. It is composed of three layers, as shown in Figure 13:  
1. **Layer 1 (A2A)** is adapted for UAV-to-UAV communication. It allows the intra-communication of a group of adjacent UAVs according to the three network topologies seen in Section 6.2.
2. **Layer 2 (G2G)** is suitable for group-to-group communication. It is responsible for the inter-communication of groups of UAVs. Each group of UAVs uses its gateway UAV to communicate with the adjacent groups. Moreover, this communication is done directly, without using the GCS network infrastructure.
3. **Layer 3 (G2A)** is suited to ground-to-air communication. It allows us to relay bidirectional information between the GCS and the UAV groups. Generally, this is done through the UAV gateway closest to the GCS infrastructure.  
Compared to the three other architectures, the multi-level architecture is robust and supports fault tolerance. It is used for complex missions that involve different types of UAVs. In fact, the multi-layer structure of this architecture allows for the massive deployment of UAVs to cover a large airspace. Unlike the multi-group architecture, UAVs with different types of protocols can be deployed in the same group [19; 28].
# Angle-Regulating Rule of Guide Vanes of Variable Geometry Turbine Adjusting Mechanism
## 3 Results and Discussion
### Influence of Different Driving Modes on Adjusting Mechanism's Different Guide Vane Angles  
In our research group, another paper studies a rigid body calculation model at room temperature. The paper designs and researches the swing rod guide vane adjusting mechanism [30]. The effects of one driver, two drivers, and three drivers on different guide vanes angles are compared. Figure 11 shows the variation law of different guide vanes angles on variable geometry turbine adjusting mechanisms with three driving modes. It can be seen from Figure 11 that the angles among different guide vanes of the adjusting mechanisms with one driver and two drivers vary greatly. The angle differences can reach 2.3\({}^{\circ}\) and 1.9\({}^{\circ}\), respectively. The angle changes among the three drivers' adjusting mechanism's different guide vanes are tiny. They are basically a curve track. The guide vanes' angles are adjusted at the same turbine stage. This ensures the consistency of angle changes among different guide vanes.  
Figure 10: Rigid–flexible coupling calculation models of two adjusting mechanisms with multiflexible bodies. (**a**) Paddle rod adjusting mechanism; (**b**) Push–pull rod adjusting mechanism.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Thermal** & **At 473.15K** & **At 573.15K** & **At 673.15K** \\ \hline Paddle & 0.10207 & 0.16592 & 0.23051 \\ Pull rod I & 0.19873 & 0.31913 & 0.44125 \\ Linkage ring & 0.08071 & 0.12971 & 0.17914 \\ Pin & 0.03503 & 0.05705 & 0.07918 \\ Arm I & 0.04547 & 0.07374 & 0.10225 \\ Arm II & 0.04415 & 0.06949 & 0.09513 \\ Inner casing & 4.3872 & 5.0007 & 5.5658 \\ Push rod & 0.21952 & 0.36659 & 0.5137 \\ Pull rod II & 0.25979 & 0.41566 & 0.57294 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermal deformation distribution of components at different temperatures.
## 3 Result and Discussion
### Particle Size Analysis of Radial Fiber Media Filtration Process  
In this study, particle size analysis was performed to determine the characteristics of particulate matter treatment throughout the fiber media filter. Figure 4 shows that the particle size range was 2.18-143.4 \(\upmu\)m (average 21.4 \(\upmu\)m) for raw water, 2.73-148.4 \(\upmu\)m (average 24.8 \(\upmu\)m) after pretreatment, and 1.08-19.8 \(\upmu\)m (average 4.45 \(\upmu\)m) after the RMF process. Particle sizes up to 20.0 \(\upmu\)m can be removed using the fiber media filter. In addition, the particle size distribution after pretreatment was slightly higher than that of raw water, as its lower retention time reduced the sedimentation effect for micro -substances with sizes less than 150 \(\upmu\)m in the pretreatment process.  
Table 4 summarizes the average particle size in each process for CSO treatment and the cumulative distribution according to the standard deviation. Ninety percentage of the cumulative particle size distributions for raw water, after pretreatment, and after the RMS process were 140.8, 148.4, and 19.7 \(\upmu\)m, respectively; 60% of the cumulative particle size distributions for raw water, after pretreatment, and after the RMS process were 37.9, 44.1, and 5.79 \(\upmu\)m, respectively. Meanwhile, 10% of the cumulative particle size distributions for raw water, after pretreatment, and after the RMS process were 2.19, 2.73, and 1.08 \(\upmu\)m, respectively. These results indicate that particle size shows a decreasing trend for the RMF process.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Process**} & \multicolumn{8}{c}{**Particle Size (Diameter, \(\upmu\)m)**} \\ \cline{2-10}  & **D10** & **D20** & **D30** & **D40** & **D50** & **D60** & **D70** & **D80** & **D90** \\ \hline Raw water & 2.18 & 5.20 & 10.8 & 17.9 & 26.2 & 37.9 & 56.2 & 87.9 & 140.8 \\ Pretreatment & 2.73 & 6.51 & 12.9 & 20.6 & 30.6 & 44.1 & 64.4 & 96.8 & 148.4 \\ RMF & 1.08 & 1.57 & 2.21 & 3.08 & 4.22 & 5.79 & 8.13 & 12.1 & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Particle size distribution of each process.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 4 Proposed Methodology
### Architectural Overview
#### 4.1.1 Centralized Computation Framework  
In QoS-aware WSNs, path computation is a multi-constrained optimization problem. The classical RPL cannot efficiently support this process as it relies on distributed in-network computations. Such computations are unfeasible under tight QoS constraints due to sensor nodes' limited processing and memory capacities, as well as their lack of global network visibility. To address these limitations, we propose a centralized offloading approach in which edge servers compute feasible conflict-free DODAGs and disseminate them to the network. The proposed system is implemented using a custom C++ framework built on the LEDA library. This framework enables efficient dominance checks, Pareto-optimal path enumeration, and scalable DODAG construction--capabilities not natively supported in NS-3 or Cooja. The design explicitly separates the route computation phase from runtime operation, facilitating modularity and protocol compliance.The framework consists of the following key components:  
1. [label=()]
2. _QoS-aware route computation_ on the topology graph under given constraints.
3. _Network (re)configuration_, where DODAGs are deployed and adjusted across nodes.
4. _Monitoring_, where nodes report status changes that may trigger reconfiguration.  
Once deployed, the network uses a standard RPL over precomputed DODAGs, preserving protocol compliance while enabling enhanced QoS guarantees.
# Reinforcement-Learning-Based Path Planning: A Reward Function Strategy
## 2 Proposed Methodology
### Controller Design for Path Tracking
#### 2.3.2 Lyapunov-Based Controller Design  
The Lyapunov-based controller design involves the selection of a suitable Lyapunov function, the analysis of its derivatives in order to guarantee its stability, and the design of a control law that uses this function to lead the system towards a desired behavior [33]. In a \(\dot{x}=f(x,u)\) system, a control Lyapunov function, \(V\), is a positive-defined function for which the following is the case [34]:  
\[\forall x\
eq 0,\exists u\quad\dot{V}(x,u)=\frac{\partial V}{\partial x}f(x,u)<0 \;\;\text{and}\;\;\exists u\;\;\dot{V}(0,u)=0 \tag{20}\]  
It assumes that for the whole value of \(x\), there is a control variable that minimizes.  
Assuming that there is a trajectory of movement, \(r(t)\), the point of interest is based on its velocity, \(\dot{r}(t)\), and its conditions in the initial position as well as its orientation. It is possible to obtain the velocity of each point of the trajectory through a Jacobian matrix, \(J(q(t))\), under the assumption that \(J(q(t))\) is regular and therefore that the inverse exists. The vector of the joint velocities is calculated as follows [19; 33]:  
\[\dot{q}(t)=J(q(t))^{-1}.r(t) \tag{21}\]  
where \(\dot{q}(t)\) is the velocities conjoined through a given trajectory. The path error can be included in the inverse kinematics calculation. Equation (21) is rewritten as follows:  
\[\dot{q}[k]=J(q[k])^{-1}.(r_{d}[k]-r_{e}[k]) \tag{22}\]  
where \(r_{d}\) is the desired state and \(r_{e}\) is the actual state. To prove the stability of the closed-loop system, we propose the root mean square error as a Lyapunov function in its matrix form:  
\[V(r_{e})=\frac{r_{e}^{T}.r_{e}}{2} \tag{23}\]  
The function \(V(r_{e})\) is locally positive in \(D\). That is, \(V(0)=0\) and \(V(r_{e})>0\) in \(D-\{0\}\). for all \(r_{e}\) in \(D\). Its derivation can be represented as follows:  
\[\dot{V}(r_{e})=r_{e}^{T}.\dot{r_{e}} \tag{24}\]  
\(\dot{V}(r_{e})\leq\ 0
## 1 Introduction
[10] showed one-class model AE is better at distinguishing imitated signatures than two-class model. However, AE is also greatly dependent on the accuracy of input data [14]. The _S-vector_ is valuable as the input of AE, and consequentially could lead to improved accuracy for dynamic signature verification. Even though the major issue is distinguishing forged signature, the proposed method should take into account other issues, i.e., time difference issue and subject/others classification. They are also important issues for the signature verification service and a model for only a single purpose could degrade other issues' performances. The proposed method obtains the better performance for time-differed signatures and subject/others classification by experimental approach. The main contributions of this paper are as follows. * To the best of our knowledge, the proposed scheme is the first CNN-AE model for hand drawn signature verification in mobile environments. * Experiments using users' real signatures show that the _S-vector_ achieves better accuracy for dynamic signature verification. The proposed scheme decreases the equal error rate (EER) by 1.1% (subject/others), 3.2% (time difference), and 13.7% (skilled forgery) compared to the previous work. This paper is organized as follows. Section 2 introduced the proposed method is introduced. In Section 3, the proposed model is evaluated based on experimental results, wherein the CNN was trained with four different classification datasets to determine the most effective _S-vector_. Section 4 concludes the paper.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 2 Preliminaries  
Polar code is currently the only encoding method that enables channel capacity to reach the Shannon Limit. Consider the B-DMC transition probability shown in Figure 1. \(W:X\to Y\), \(X\in\{0,1\}\), \(P=W(y|x)\), \(x\in X,y\in Y\).  
When \(X\) takes 0 and 1 according to the same probability, the information capacity is defined as the mutual information of the input and output [17]:  
\[C(W)=I(X;Y)=\sum_{y\in Y}\sum_{x\in X}\frac{1}{2}W(y|x)\log_{\frac{1}{2}W(y|0)+ \frac{1}{2}W(y|1)} \tag{1}\]  
At this time, \(0\leq C(W)\leq 1\); when \(C(W)=1\), \(W\) is an excellent channel, and when \(C(W)=0\), \(W\) is a useless channel. The channel polarization phenomenon is an important guarantee for the realization of reliable information transmission, which turns most sub-channels with \(0\leq C(W)\leq 1\) into sub-channels for transmitting information bits with \(C(W)=1\), or sub-channels for transmitting frozen bits with \(C(W)=0\). The B-DMC channel capacity is measured by the Bhattacharyya parameter [17]:  
\[Z(W)=\sum_{y\in Y}\sqrt{W(y|0)W(y|1)}, \tag{2}\]  
\[\log\frac{2}{1+Z(W)}\leq C(W)\leq\sqrt{1-Z(W)\cdot Z(W)}. \tag{3}\]  
The larger the Bhattacharyya parameter, the smaller the channel capacity. The capacity of the channel can be quantitatively described with the Bhattacharyya parameter, so the polarization effect of the channel--that is, the process of channel combining and separating--can be quantitatively described as well. The schematic of combining and separating \(n\) channels is presented in Figure 2.
# Construction Schedule versus Various Constraints and Risks
## 4 Probabilistic Time Coupling Methods--PTCM  
This section presents the original computational method--the Probabilistic Time Coupling Method (PTCM). The PTCM involves using probabilistic data to schedule tasks using the TCM I, TCM II, and TCM III methods. The aim of this work is to obtain probabilistic implementation times for a construction project that reflect real conditions and enable decision-makers to consider their preferences. When planning the implementation of construction investments, it is necessary to consider the occurrence of various random events that may affect the documents being prepared. Calculation methods for the time and cost of the construction process that do not take into account random factors do not fully reflect real values. The PTCM was proposed based on the Project Evaluation and Review Technique (PERT) [58; 59] and the TCM schedules. The TCM schedules used probabilistic input data, which were calculated using the Multivariate Method of Statistical Models. The PTCM modifies the TCM scheduling computational algorithms by introducing probabilistic data and data standard deviations [60; 61]. The PTCM is a novel scheduling approach described in detail in the doctoral thesis [62], which was highly regarded by the reviewers. In the past year, efforts have been made to use PTCM to schedule current investments. Although the research results are time consuming, the experimental work carried out so far has yielded promising results. The author notes the limitations of the PTCM, particularly in the labor-intensive stage of collecting historical data and performing calculations. Additionally, the method's automation of calculations and graphical presentation is limited. A computational application was created in Microsoft Excel (2016 software), which works well. However, the graphical presentation of the results requires further improvements. A grant application is being prepared to obtain financial resources for the further development of PTCMs and the creation of a dedicated computational program. The PTCM burdens the process time \(P_{1}\) on the \(S_{1}\) sector--(\(t_{11}\)) with risk and uncertainty \(\sigma(t_{11})\). Subsequent processes on subsequent plots also carry risks and uncertainties resulting from the current process as well as from earlier processes. The implementation time of the \(P_{3}\) process in sector \(S_{2}\)--\(t_{23}\) is affected by the uncertainties and risks (\(\Sigma_{23}\)) of the work that precedes the current activity as well as \(\sigma(t_{23})\) derived from the current activity. The PTCM's single computational segment has been modified in relation to the computational scheme of the TCM. Instead of the late start time and late end time, new data have been introduced: standard deviation of the current process, the sum of standard deviations of all processes preceding the current process and the current process, forecast of the minimum completion time of a given process--optimistic forecast, forecast of the maximum time completion of a given process--pessimistic forecast.