esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The current study provides a comprehensive review of the performance of biogas production in submerged and external AnMBRs equipped with polymeric and ceramic membranes. Special attention has been paid to the impact of temperature and hydraulic retention time on the biogas yield. Moreover, possible fouling mitigation strategies were described in detail.  
It was demonstrated that in recent years, significant progress has been made in research focused on the AnMBR applications for biogas production. Indeed, the reliability and stability of this technology have been qualified by investigations carried out with the use of laboratory- and pilot-scale installations. The evidence from this study confirmed that AnMBR technology can be successfully used for biogas production via municipal and domestic wastewater treatment.  
Importantly, the performed literature review indicated that it is not very straightforward to define the impact of HRT on biogas production in AnMBRs. Indeed, there has been some disagreement concerning the effect of this parameter on the process performance. However, data analysis presented in the current study demonstrated that, generally, the highest values of the methane yield are obtained for the shortest hydraulic retention time. In addition, increasing the temperature has a positive impact on the methane yield.  
In addition, the fouling mitigation strategies commonly applied in AnMBR technology were presented and discussed. It was pointed out that, so far, a large number of protocols for membrane cleaning have been presented in the literature. Furthermore, it has been shown that in order to restore the initial permeate flux, a combination of several cleaning methods is often required. This can be explained by the fact that some wastewater compounds form irreversible fouling on the membrane surface and inside its pores, removing of which requires specific conditions.  
Finally, the findings presented in the current study may be particularly important for the determination of operating conditions as well as suitable fouling mitigation strategies for laboratory- and pilot-scale AnMBRs used for biogas production via the treatment of municipal and domestic conditions. It should be pointed out that the present study highlights further research efforts which are essentially required.
## 2 Calculation Model and Solution Method  
### Calculation Model and Basic Equation  
For a single pile set in layered soil, the pile top is assumed to be flush with the ground, and the horizontal load \(H_{0}\), bending moment \(M_{0}\), and vertical load \(N_{0}\) are assumed to be applied to the pile top. The other basic assumptions are enumerated below.  
(1) The pile is regarded as a vertical foundation beam, and the deformation and internal forces of the pile body under the elastic state can be determined according to beam bending theory.  
(2) The pile, and the soil around the pile, are always in contact, and the displacement of the soil around the pile is the same as that of the pile body.  
(3) The soil is an elastic-plastic body, and the relationship between the pile side soil resistance and deformation is described by a \(p\)-\(y\) curve.  
(4) The pile side friction is uniformly distributed along the pile body.  
Since the dead weight of the pile varies linearly with its depth, the axial force, \(N\), of the pile at any depth, z, from the ground is [15]:  
\[N=N_{0}+\xi z \tag{1}\]  
where \(\xi\) is the axial force growth coefficient. For cast-in-place piles, the pile side friction is assumed to be rectangular, \(\xi=A\gamma_{c}-Uq_{z}\). Here, \(q_{z}\) is the side friction per unit area at depth \(z\); \(\gamma_{c}\) is the weight of the pile; \(A\) is the cross-sectional area of the pile, and \(U\) is the circumference of the pile.  
The stress of the micro-element, with a length of \(dz\) at the depth of \(z\), is shown in Figure 1. Based on the above assumptions and the static equilibrium conditions of this element, the horizontal displacement y of the pile body follows the fourth-order differential equation:  
\[EI\frac{d^{4}y}{dz^{4}}+N\frac{d^{2}y}{dz^{2}}+\zeta\frac{dy}{dz}+b_{0}c_{z}y=0 \tag{2}\]  
where \(b_{0}\) is the calculated width of the pile; and \(c_{z}\) is the horizontal resistance coefficient of the foundation. When the \(p\)-\(y\) curve is used to describe the pile side resistance, \(c_{z}\) is the secant modulus of the \(p\)-\(y\) curve, \(c_{z}=p/y\), kN/m\({}^{3}\).
## 2 Related Works
### Algorithms to improve the data packet delivery ratio  
Hybrid routing strategies, which draw upon the most advantageous features of both proactive and reactive mechanisms, are designed to improve the communication quality for active routes with lower overhead. As a hybrid routing strategy, ZRP has been proposed to provide a hybrid routing framework for a locally proactive and globally reactive approach so as to minimize the sum of the proactive and reactive control overhead. For the zone radius, the main idea of ZRP, each node proactively advertises its link state over a fixed number of hops. These local advertisements over nodes offer an updated view of the routing zone collection of all nodes (or links) that are reachable within the zone radius. The nodes, termed peripheral nodes, are located on the boundary of the routing zone and play an important intermediate role in reactive zone-based route discovery.  
The other means of improving the data packet delivery ratio is via a Clustering algorithm [11-13]. Cluster head-Gateway Switch Routing (CGSR) is a typical cluster-based hierarchical routing method. A stable clustering algorithm, Least Cluster head Change (LCC), is used to partition the entire network into several clusters with the selection of a cluster head for each cluster. A mobile node that belongs to two or more clusters is a gateway connecting them, and data packets are routed through paths with the format of a \"Cluster head-Gateway\" between every source and destination pair.  
Integrated Services (IntServ) [14] and Differentiated Services (DiffServ) [15] also guarantee a good quality of packet delivery ratio. An IntServ protocol such as the Bandwidth Reservation over _Ad Hoc_ Wireless Networks (BRAWN) [16] mechanism not only guarantees certain QoS levels but also naturally distributes the traffic more evenly among network nodes, similar to load balancing through a rich end-to-end QoS solution mechanism. On the other hand, DiffServ such as a Stateless Wireless _Ad Hoc_ Network (SWAN) [17] can be used to classify network traffic into different priority levels and apply priority scheduling and queuing management mechanisms to obtain QoS guarantees.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 6 Application of Multi-Agent Deep Reinforcement Learning: State of the Art
### SADRL Applied to Distributed Agents in a Multi-Agent Environment
#### 6.1.8. [PERSON]'s SADRL Approach in a Multi-Agent Environment  
In [74], [PERSON] et al. embedded SADRL in a centralized agent, which is the centralized controller, to coordinate the trajectories of distributed agents, which are autonomous surface vehicles (ASV), based on whether an area has been visited or not within a time period in order to monitor the contamination level of an area of water resource (A.4). The agents maximize the global reward in a collaborative (X.2.2) manner. The ASVs select the next movement and report it to the centralized controller (X.3.1). The centralized controller guides the ASVs by rejecting possible disturbances using the experiences of all ASVs. The key feature is that distributed agents observe local states and learn from common experiences rather than exchanging knowledge with neighboring agents, which reduces the signaling overhead (O.4), and hence increasing scalability (O.5). The proposed approach addresses the challenges of high dimension (C.3) due to the large state-action spaces with a large number of ASVs in the multi-agent environment, and the dynamic condition of the operating channels, and the large state spaces (C.3) with a large number of ASVs in a heterogeneous environment.  
For each ASV \(i\), the state \(s_{t}^{i}\) represents a 3-channel (RGB) image of the area of water resource. The action \(a_{t}^{i}\) represents the ASV movements, which include perpendicular (i.e., N, S, E, W) and diagonal (i.e., NE, SE, SW, NW) movements. The reward \(r_{t+1}^{i}(s_{t+1}^{i})\) represents a positive value when the trajectory of the ASV covers the area of water resource. Higher reward is received by the agent \(i\) when it chooses action \(a_{t}^{i}\) that: (a) visits areas of water resource which have not been visited within a long time period; (b) does not cause collision with other ASVs; and (c) does not move out of the monitoring area.  
The proposed approach extends the traditional SADRL approach so that the decoupled and fully connected output layer of the DNN of the centralized agent can represent the action \(a_{t}^{i}\in A^{i}\) where \(i\in A^{N}\). Each distributed agent has the same sets of actions and rules (or constraints), and each of its experience \((s_{t}^{i},a_{t}^{i},r_{t+1}^{i}(s_{t+1}^{i}),s_{t+1}^{i})\) is shared with the centralized agent, contributing to the training process equally. The centralized agent uses batches of experiences shared by all agents during memory reply. The proposed approach has been shown to increase accumulated reward (P.2).
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 3 The Proposed Safety Message Dissemination Scheme
### V2V Communication
#### 3.3.2 Intercluster Communication  
Intercluster communication refers to message dissemination between the CHs of different clusters. There are two main situations where intercluster communication is necessary. First, when disseminating emergency safety messages, CHs must perform message dissemination as representatives. Second, when a general safety message is requested and the requested data do not exist within the cluster, it is necessary to forward the request to another cluster.  
Algorithm 3 shows the intercluster communication algorithm, which is divided into disseminating emergency safety messages and forwarding request messages for general safety messages. When a CH has an emergency safety message with remaining lifetime, it disseminates the message to the CH of another cluster. If a request for a general safety message needs to be disseminated to another CH, the CH first stores the request message in the cluster request message table (CRD). When the CM that requested the general safety message receives the message or departs from the cluster, the request message is deleted from the CRD. The CH periodically disseminates the CRD to CHs in nearby clusters. CHs receiving the CRD from another cluster check if the requested general safety message exists within their cluster. If the requested safety message exists within the cluster, a connection is established between the two CHs, and the message is delivered. If the requested safety message does not exist within the cluster, the CH ignores the request.  
```
1:Intercluster communication algorithm
2:Notation
3:CRD: Cluster request message table
4:CRD: Total messages of CRD
5:CRD: Cluster request message
6:EM: Emergency safety message
7:lifetime= Survival time that must be guaranteed for EM
8:RM: Request message for general safety message
9:N: Current vehicle
10:Input:RM, EM
11:Output:CRD message request
12:defToOther_Cluster(CRD):
13:ififlen(CRD) == 0
14:send all messages of the CRD to other nearby clusters
15:ifthe CM that requested CRD_i departed from the cluster or received CRD_i:
16:CRD_total\(\_\)CRD_i
17:ifCRD_i within CRD is received from another nearby cluster:
18:CRD_total\(\_\)CRD_i
19:send CRD_i to the CM that made the request
20:whileN == CH and if not departed from cluster:
21:ifholds EM and the EM's lifetime == 0
22:ifdissemine emergency safety message // disseminate emergency safety message to nearby CHs
23:ifthere is no safety message of RM in the cluster:
24:insert RM in CRD
25:ToOther_Cluster(CRD)
```  
**Algorithm 3** Intercluster communication algorithm.
# Investigations on Interface Shear Fatigue of Semi-Precast Slabs with Lattice Girders
## 2 Lattice Girders for Use in Cyclically Loaded Structures  
### General  
Whereas many types of lattice girders exist for monotonic loading, only four types of lattice girders are approved for fatigue loading in Germany (Figure 1). These types are covered by technical approvals and offered by a small number of manufacturers [41; 42; 44; 45; 46; 47; 48; 49].  
The type of lattice girder shown in Figure 1a is approved to resist interface shear and vertical shear in semi-precast slabs and element walls. Due to the 90\({}^{\circ}\) inclination of every second bar (vertical) and the other bar being inclined (diagonal) towards the support (45\({}^{\circ}\)\(\leq\)\(\alpha\)\(\leq\) 90\({}^{\circ}\), Figure 2), all bars can be utilized for interface shear design. Additionally, in cases where such a lattice girder complies with the construction rules of shear reinforcement and has sufficient depth for adequate anchorage of all vertical and inclined bars within the compression zone, these bars can be considered for vertical shear design.  
Due to its wide range of applications, Type (a) has been chosen for the following theoretical and experimental investigations. In addition, the small mandrel diameter of this lattice girder type allowed easily triggering and observing a fatigue failure within the experimental study.  
In detail, the geometrical properties of lattice girders of Type (a) that have been used for the experimental investigations are shown in Figure 2. The experimental study  
Figure 1: Lattice girder types approved for fatigue in semi-precast slabs (**a**–**c**) and element walls (**a**,**d**).  
consists of KTS from Baustahlgewebe GmbH [41] and EQ from Filigran [42] with heights of 10-16 and 17-30 cm, respectively.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 3 Information Dissemination Model Based on RP Strategy  
### Model Description  
The agglomeration phenomenon is common in social systems and friendship networks, and scientific cooperation hypernetworks are no exception. Nodes representing individuals and hyperedges represent social groups formed by those individuals. In the reactive process (RP) strategy, a node is initially selected as an I-state node, which can transmit information to S-state nodes in all its neighbors, and its neighbors obtain information with a certain probability. The dynamic transmission process of information is as follows:  
1. Initialization: at \(t=0\), a node is selected as the I-state node, while the remaining nodes are classified as S-state nodes.
2. Propagation: at each subsequent time step, the I-state node transmits information to all neighbor nodes in the S-state. S-state nodes change to I-state nodes with a probability of \(\beta\). Conversely, I-state nodes may transition back to the S-state node with a probability of \(\gamma\) due to factors such as forgetfulness.
3. Steady state: with the continuous spread of information, the density of nodes in the I-state gradually stabilizes and fluctuates slightly around this stable density, signifying that information propagation in the network has reached a steady state.  
The description of the information transmission process is shown in Figure 2.
# Secure Cooperative Routing in Wireless Sensor Networks
## 2 Materials and Methods
### Mathematical Model for MIDS Algorithm  
To verify the effectiveness of the algorithm, a simple mathematical model is constructed. In this deployment, distance \"D\" between the estimated location of nodes in the coordinates system is calculated using the distance formula:  
\[\text{D}=\sqrt{((\text{x2}-\text{x1})+(\text{y2}-\text{y1}))^{2}} \tag{1}\]  
The energy model used in the communication phase is adopted from [34]. To transmit \"1\" bit data packets to and from distance \"d\", the energy used is calculated by using Equations (2) and (3), respectively.  
\[\text{E}_{\text{Tx}}(\text{l},\text{d})=\{\begin{array}{ll}\left(\text{le}_{ \text{elec}}+\epsilon_{\text{fs}}\times\text{d}^{2}\right),&\text{ifd}\leq \text{d}_{\text{th}}\\ \left(\text{le}_{\text{elec}}+\epsilon_{\text{amp}}\times\text{d}^{4}\right),& \text{ifd}>\text{d}_{\text{th}}\end{array} \tag{2}\]  
\[\text{E}_{\text{Rx}}(\text{l})=1\times\text{e}_{\text{elec}} \tag{3}\]  
Here free space (d\({}^{2}\)) and multipath fading (d\({}^{4}\)) are engaged. The amplification factors are \(\epsilon_{\text{fs}}\) and \(\epsilon_{\text{amp}}\) for these two models, respectively. Signal processing energy consumption is denoted by \"\(\text{e}_{\text{elec}}\)\".  
Using FCM in the first round based on the nodes' location and layer information, the network is divided into nodes which are grouped into different clusters. The main objective of the FCM is to minimize the objective function \"O\" given below in Equation (4).  
\[\text{O}(\alpha,\beta)=\sum_{\text{i}=1}^{n}\sum_{\text{k}=1}^{c}\left(\text{ \text{\text{\text{\text{\text{\text{a}}}}}}}\right)_{\text{m}}=\parallel \text{xi}-\beta\text{j}\parallel^{2} \tag{4}\]  
where \"\(\text{\text{\text{\text{\text{a}}}}}\)i\" represents the membership of the node at \"i\" to the cluster center at the position \"j\" and the cluster center at the jth position is denoted by \"\(\beta\)j\".  
The packets received Ri and packets transmitted Ti values of a CH(Hi) are processed by the IDS agent in the BS. The IDS agent has a ratio gauge which calculates the intrusion ratio (IR). The formula for the intrusion ratio is given below in Equation (5).  
\[\text{IRi}=\text{Ri}/\text{Ti} \tag{5}\]  
where Ri are the packets received and Ti are the packets transmitted values of a CH(Hi). The RTT (round trip time) is calculated by using Equation (6).  
\[\text{RTT}=\text{t}_{\text{2}\_\text{i}}-\text{t}_{\text{1}} \tag{6}\]  
where t\({}_{\text{1}}\) is the route request time by the sender and t\({}_{\text{2}\_\text{i}}\) is the route reply time by the receiver.
## 6 Open Challenges, Future Prospects and Research Directions
### Adaptive Modulation and Coding  
Advances in AMC techniques will be pivotal. Algorithms that can switch between different modulation formats and ECC based on real-time channel assessments will enhance the resilience of FSO systems. For instance, during periods of high turbulence, the system could switch to a more robust modulation scheme to ensure data integrity, while reverting to higher data rate schemes when conditions are favorable.
# Machine Learning Techniques for Soil Characterization Using Cone Penetration Test Data
## 4 Machine Learning Models  
### Random Forests  
RF is an ML algorithm that has been widely used for classification and regression tasks [66; 67]. It is an ensemble method that combines multiple decision trees to improve predictive accuracy and reduce overfitting. RF has several advantages over other machine learning algorithms, including its ability to handle high-dimensional data, nonlinear relationships between variables, and missing values [68]. In addition, it provides measures of variable importance that can be used for feature selection and interpretation [69].  
RF classification and regression can be implemented in R using various packages such as randomForest [68] and ranger [70]. The randomForest package is one of the most widely used packages for RF classification and regression in R. It provides a simple interface for building and evaluating RF models and supports both classification and regression tasks. The ranger package is another popular package for RF classification and regression in R. It is designed to be faster and more memory-efficient than the randomForest package, and supports parallel processing [70].  
The ranger package provides several hyperparameters that can be tuned to improve the performance and robustness of RF models. To tune these hyperparameters, one common approach is to use cross-validation. This involves splitting the data into training and test sets, fitting the model on the training set with different combinations of hyperparameters, and evaluating the performance on the validation set. Bayesian optimization is one of the most efficient methods for hyperparameter tuning. It uses a probabilistic model to predict the performance of different hyperparameter configurations based on previous evaluations [71].
## 3 Research Methods
### Subsidence Zone  
Previous researchers have utilized integrated PS, SBAS, and DS InSAR techniques to monitor subsidence, which reveals multiple subsidence centers in Kunming's urban  
Figure 7: Flowchart of GWO-LSTM model prediction.  
area. In recent years, new subsidence funnels have continually emerged along the northern and eastern shores of Dianchi Lake. Although these funnels are small in volume, they cover a broad area and exhibit an outward expansion trend. Based on the preliminary identification of subsidence zones and the distribution characteristics of the subsidence centers, we have divided the severely affected areas into six sub-study zones by regular rectangles to facilitate more in-depth research. The specific subsidence zones are as follows.  
Subsidence zone A: Primarily located in the Rongchuang-Xinhe Community-Wangjiadui area in Xishan District, Kunming.  
Subsidence zone B: A newly identified subsidence-sensitive area, situated along Metro Line 5, the South Connection Line Expressway, Qianwei Road, Hairun Road to Ziyuan Road, Guangfu Road, and Haize Road.  
Subsidence zone C: The largest known subsidence area in Kunming, encompassing multiple locations in Guandu District, including Liujia Village, Caojiaqiao, Niujia Village, Dianchi Convention and Exhibition Center, Sijia Community, Luoya Community, Wangjia Village, and Longma Community.  
Subsidence zone D: Extending from Xiaobanchaio to Guangwei Village, Hewei Village, and Fubao Village.  
Subsidence zone E: Situated in the Daloyang Community and Xiaoloyang Community of Luoyang Street in Chenggong District.  
Subsidence zone F: Includes Zhonghe Village and Luojia Village in Dayu Street, Chenggong District, and areas along the East Ring Road.  
Subsidence zones A, B, and C are located along the northern and eastern shores of Dianchi Lake. These areas have experienced rapid urban development in Kunming in recent years, and contain a significant number of industrial parks. Subsidence zones D and E are further from the lakeshoe, with more concentrated subsidence centers that began earlier. Subsidence zone F is located in a wetland park along the Dianchi Lake shore, where subsidence has occurred rapidly in recent years, displaying distribution characteristics similar to zones D and E.