esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mobile Peer-to-Peer Architectures
### Challenges  
As MP2P systems combine MANETs and P2P networks, challenges of both architectures are inherited as shown in Figure 7. For instance, as routing table maintenance and object relocation of P2P networks requires a high amount of bandwidth and MANETs provide only _limited resources_, security algorithms have to be strongly limited in bandwidth. Further, as both architectures are _decentralized_, the availability of a Certification Authority (CA) can not be assumed. Due to this, _trust_ is a major problem as the routing algorithm has to rely on the cooperation of the nodes. As both architectures have a constantly _changing topology_, in the case of MANETs due to node mobility and in the case of P2P systems due to nodes going offline, an increased amount of churn is generated compared to traditional P2P networks. This results in an increased overhead for object relocation.
## 4 Discussion
### Application of the Actual Results  
In the domain of urban planning, precise building extraction outcomes can offer essential foundations for urban zoning and land use planning. By accurately grasping the distribution information of buildings, planners can rationally optimize the urban spatial structure. Regarding disaster management, by contrasting the building extraction results prior to and following a disaster, the damage state of buildings can be swiftly assessed. This assessment offers support for the reasonable distribution of rescue resources and expedites the post-disaster reconstruction process. In the context of urban digitization and the updating of geographical databases, accurate building information serves as the basis for creating high-precision digital city models. Whether the building data in the geographical database are accurate directly affects the accuracy and practicality of map navigation, urban information management systems, and the like.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 4 5G Support for Industrial IoT Applications Utilizing Robotics
### Cyberphysical Systems and Industrial IoT  
When discussing data collection, analysis, and feedback from various industrial endpoints, and less about their enterprise effects, Cyberphysical Systems supporting the Industrial IoT (IIoT) come into focus.  
Cyberphysical-based manufacturing systems (CPMS), or in other terms, cyberphysical production systems (CPPS), which are designed to realize smart management in manufacturing physical space through data modeling, integration, fusion, simulation, analysis, and decision-making are also impacted by 5G. Based on the respective advantages of 5G application scenarios and the basic architecture of CPMS, [29] proposes a 5G-based IIoT architecture. Furthermore, the paper describes implementation methods of manufacturing scenarios and technologies using eMMB, URLLC, and mMTC, and it analyzes the key technologies and challenges of 5G-based IIoT, as well.  
Obviously IIoT is not just for manufacturing; the potential usage of IoT technology for various construction project processes is presented in [30]. This article proposes a construction domain framework, which is used to identify and discuss the barriers raised by connectivity issues, which are then overcome by the influence of 5G technologies. Using an example, the paper presents a comparative  
Figure 4: Benchmarking infrastructure for measuring heterogeneous local automation cloud communications capabilities [26].  
quantitative analysis of sample processes to show the potential advantage that 5G technology will bring over 4G.  
IIoT can also be used to implement positioning using 5G, as shown by the authors of [31], among others. The paper discusses several features of 5G positioning in the context of IIoT applications, which demand high accuracy of position information. It summarizes the main opportunities to come with 5G networks, such as huge available spectrum, small cell networks, Multiple Input Multiple Output (MIMO) antennas and beamforming, and points out the challenges in the context of robot 5G positioning. It presents a case study for the localization of an indoor robot in a multi-wall multi-floor scenario, based on various carrier frequencies and access node densities. The paper finds that sub-meter positioning accuracy required for most of the future industrial applications is theoretically achievable via a combination of small cell networks, mm Wave carriers and antenna arrays, but practical issues such as node synchronization, connectivity and ultra dense network deployment costs have to be tackled.
## 3 Results
### Inline Measurements  
The functionality of the coaxially integrated OCT-system was also tested during the process. No significant differences could be found in the OCT images of the welds taken in offline mode when compared to the images taken in inline mode. This means that the process laser has no influence on the inline measuring system or the quality of the measurement images. By varying the process parameters (laser power, feed rate, and wire feed speed), the influence of these parameters on the measured OCT-signal was evaluated. Two additional measurement systems were used to validate the data acquired with the coaxially integrated OCT-system: A high-speed camera was integrated into the measurement setup to observe the melting process (Figure 9b) and a commercial offline tabletop OCT system (Thorlabs, Newton, NJ, USA) was used to qualitatively verify the measured weld dimensions after the process (Figure 9a).  
Once the wire height and focus distance were set, welds were generated on a substrate surface as test samples. During the laser process, OCT data were acquired with the coaxially integrated OCT-system and post-processed both as 2D data (B-Scans) and 3D data (C-Scans). The data then were compared with the data acquired after the process with the offline OCT system and through simple visual observation of macroscopic shape deviations. The OCT images, which were acquired with the coaxially integrated OCT-system, show shape deviations that typically occur in the LMD-w process. Figure 10A,B show clear deviations from an optimum weld bead in the form of ripples at various positions. At the starting point of the process, a uniform and smooth weld bead was generated (Figure 10B left). In the course of the process, the distance (observed in the OCT B-Scan, Figure 10C) from the workpiece surface to the end of the process head changed, resulting in the afore-mentioned shape deviations. The OCT images (Figure 10) show these deviations, which are clearly visible and quantifiable in the volume Scan acquired by the coaxially integrated OCT-system. Within the inline measurements, numerous welds with different wire diameters were tested up to a maximum width of the resulting welds of 3 mm.  
Figure 9: (a) OCT B-Scan of a weld bead acquired with a commercial tabletop system. (b) High speed camera image of the weld bead acquired in the process.
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 2 Related Work
### Zoom-Out Geographic-Location Service (ZGLS)  
The ZGLS [15] is a quorum-based location service. A quorum is a subset of nodes in the network [16]. In the scope of the ZGLS, each node is associated with two quora, the update quorum that is used to update the location information, and the query quorum that is used to look for the location of another node. For a single node, these two quora can be different. However, they are designed so that the intersection of the query quorum of each node with the update quorum of any of the other nodes is not empty. This ensures that the update quorum always satisfies the query. The specificity of ZGLS, according to the authors, is that location updates and answers are done through single-hop communications. In order to provide quora for each node, a one-hop location server called the parent is found. All parents are included in a chain of parents. This leads to a very good response time for location queries at the expense of a very high cost for setting up the infrastructure, especially regarding the identification of all quora.
# Joint Energy Supply and Routing Path Selection for
### Related Work
These researchers' works already can get extra network lifetime, however, when determining the charging nodes, they need to set an energy threshold directly or indirectly, which means that sensor nodes only will be charged when energy consumption reaches a certain level, and it is possible to shorten the actual network lifetime due to belated energy replenishment. It is noticed that all abovementioned works simultaneously consider the routing and energy replenishment. However, most of these studies mainly focus on one aspect of the routing or energy replenishment, and some problems still exist in the energy supply process. Therefore, in this paper, we propose a new algorithm based on a joint consideration of routing and initiative energy replenishment, and considering practical problems, we study the limited energy supply capacity of WCD.
## 3 Data and Methods
### Deep Seismic Reflection Profile  
The seismic reflection profile can reveal deep complex geological structures; in 2012, a deep seismic reflection profile was acquired in the southern part of the Linfen Basin (profile A-A' in Figures 3 and 4), with a length of approximately 55 km [49]. Data collection employed a 30 m spacing between lines, 240 m spacing between shots, 800 receiver lines, and 50-fold coverage. Seismic waves were generated using borehole blasting sources at depths of 25-30 m and an average main frequency of 10 Hz [22].
# 6G Goal-Oriented Communications: How to Coexist with Legacy Systems?
## 1 Introduction
Therefore, while it is true, in our view, that 6G will enable new classes of GO and semantic services, it is also certain that it will keep serving classical users and/or _verticals_[33] (i.e., data-oriented services), which, in the sequel, we refer to as _legacy communication or, data-oriented_ systems and services (in this paper, we will refer to them as _DO systems_ for brevity). For this reason, at some point in the near future (e.g., in the next 10 years), a natural question will arise on the coexistence of GO and DO systems, to understand how one system affects the other, when they share the same network infrastructure and wireless resources. A possible question in this context is:  
_How does the interference of a DO system affect GO performance, and at which cost?_  
A similar question is considered in [34], where the authors analyze the performance of a system in which a semantic and a DO service coexist. In this paper, we aim to answer the question of coexistence of GO and DO systems, based on a formal definition of a goal, entailing effectiveness and costs that jointly take into account GO and DO system requirements. After a general yet formal definition of goal and GO optimization problems, we will consider the case of MEC-enabled edge inference as a use case for the GO system. In the proposed setting, a GO system aims to classify data collected by an end device and transmitted to a nearby Mobile Edge Host (MEH), while a DO system aims to upload data with the highest possible data rate. The fundamental question to answer is then the following:  
_What is the highest DO system data rate that can be achieved while, at the same time, guaranteeing a target goal effectiveness of the GO system, assuming full spectrum sharing (i.e., in the presence of co-channel interference)?_  
We believe that this and other related questions will arise in the future for several use cases. Among the others, due to the bonding between communication and computing, we can mention the edge intelligence paradigm for robot remote control in factory plans, environment (e.g., factory floor) digital twins for manufacturing, identification of faulty parts in a production line, vehicular communications for cooperative perception and the coexistence of ground and aerial communications [4]. For the latter, the coexistence pertains not only to GO and DO services but also to terrestrial and non-terrestrial communications, which makes system optimization even more challenging. Of course, for such new services and use cases, different metrics beyond the data rate can be taken into account and traded off with the goal effectiveness of coexisting GO services.
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Characterization of Catalysts
#### 3.2.5 H\({}_{2}\)-Tpr  
H\({}_{2}\)-TPR can effectively determine the reducing capacity of the catalyst. Figure 7 shows the H\({}_{2}\)-TPR profiles of the catalyst before the HDO reaction. All the samples have no peak at the high temperature (>600 \({}^{\circ}\)C); this temperature corresponds to the reduction peak of nickel spinel [21], indicating no nickel spinel was produced in the experimental catalysts. Figure 4 of the XRD plots indicates the absence of the spinel phase for Ni. For example, NiO is shown in 20 of 37.8, 44.7, and 62.3 (PDF# 78-0429) and NiAl\({}_{2}\)O\({}_{4}\) in 20 of 37.8, 42.3, and 68.4 (PDF# 78-0552) as reported by [PERSON] and [PERSON] [22]--these 20 data are not shown in the Figure 4 XRD spectra, albeit 44.7\({}^{\circ}\) indicates a slight peak. The slight difference in TPR profiles (with respect to peak pattern and corresponding temperatures) can be ascribed to the variation in Ni dispersion and active metal-HZSM-5 interaction. Compared with the reduction peaks of the catalyst with a 20% CeO\({}_{2}\) content, the catalyst with a 15% CeO\({}_{2}\) content exhibited a sharper reduction peak, indicating the presence of more uniform and highly dispersed metal particles [23]. Therefore, different CeO\({}_{2}\) contents have slight effects on the reduction temperature of the catalyst. In particular, the reduction temperature reached the lowest at the catalyst with a 15% CeO\({}_{2}\) content. Usually, the rapid deactivation of the catalyst is caused by the carbon deposition at a high HDO temperature. A low reduction indicates a low-temperature HDO process, hence, the less degree of carbon deposition on the catalysts during HDO. The catalyst with 15% CeO\({}_{2}\) can promote the hydrogenation process at low temperatures, and the degree of carbon deposition on the catalysts can be effectively slacked during HDO.
## 3 Problem Statement  
The problem considered in this study corresponds to the design of a controller that can simultaneously enforce the tracking of reference trajectories for the robotic arm and the microgripper. The set of reference trajectories is provided by applying inverse kinematics based on the motions needed for the arm's articulations and the end-effector. The corresponding time-dependent vector for the arm is characterized by the variable \(q_{a}^{*}\), which is twice differentiable. Each trajectory should correspond to the motion requested for the arm and the parallel configuration of the gripper using the piezoelectric actuators, the trajectories of which are defined by \(x^{*}=[x_{1}^{*},V_{1}^{*},x_{2}^{*},V_{2}^{*}]^{\top}\). The temporal evolution of these reference angles is calculated considering a motion based on path planning that allows the gripper to approach the position where the object must be manipulated. It is desirable that these motions be completed in a predefined period to at least an invariant region depending on non-matched perturbations. Hence, asymptotic convergence to an invariant zone is expected, considering that reference trajectories must be tracked as long as the time tends to infinity. Formally, the control design must solve the following tracking problem for both, which is defined as \(\Phi=\left[q_{a}^{\top},q_{b}^{\top},x^{\top}\right]^{\top}\).  
\[\limsup_{t\rightarrow\infty}\|\Phi_{t}^{*}-\Phi_{t}\|=\beta, \tag{5}\]  
where \(\beta\) is a non-negative scalar proportional to the level of non-matched uncertainties and noises affecting the dynamics of the robotic arm and the gripper and \(\Phi^{*}=\left[(q_{a}^{*})^{\top},(q_{b}^{*})^{\top},(x^{*})^{\top}\right]^{\top}\) is a reference variable.  
This is a proposed contribution to the control design presented in this study regarding the application of adaptive automatic controllers able to regulate the motion of the micro-gripper attached to the robotic arm while satisfying the state restrictions for both the robotic arm and piezoelectric actuator. Moreover, the feasible control design must consider that only the position measurements can be used, as velocity data are not available.
## 5 Empirical Study
### Empirical Study Setup  
The empirical experiment split the dataset into 80% training and 20% testing to run the predictions. This study employs several regression models with default and explicit parameters.  
* We applied a Linear Regression model with its default parameters, where fit_intercept = True, to ensure that the model calculated the intercept.
* Other parameters at their default include n_estimators = 100 and bootstrap = True for Random Forest.
* The Support Vector Machine was employed with default settings, such as Kernel = 'rbf', c = 1.0, and epsilon = 0.1.
* The Decision Tree Regressor had random_state = 42, with default values for the criterion ='squared_error' and splitter = 'best'.
* The K-nearest neighbor regressor was used with default n_neighbors = 5 and weights = \"uniform\".  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{\begin{tabular}{c} **ENTRY** \\ **Movements** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **READ** \\ **Movements** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **WRITE** \\ **Movements** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **EXIT** \\ **Movements** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **COSMIC** \\ **Functional** \\ **Size (CFP)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Actual** \\ **Effort** \\ **(Hours)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Functional** \\ **Processes** \\ \end{tabular} } \\ \hline P38 & 1 & 3 & 1 & 2 & 7 & 14.7 & 3 \\ \hline P39 & 1 & 5 & 2 & 4 & 12 & 24.2 & 5 \\ \hline P40 & 2 & 4 & 1 & 3 & 10 & 19.6 & 4 \\ \hline P41 & 1 & 3 & 2 & 2 & 8 & 15.8 & 3 \\ \hline P42 & 1 & 5 & 1 & 4 & 11 & 22.8 & 5 \\ \hline P43 & 2 & 4 & 2 & 3 & 11 & 22.0 & 4 \\ \hline P44 & 1 & 3 & 2 & 4 & 10 & 20.3* The Gradient Boosting Regressor utilized AdaBoost Regressor was set with n_estimators = 50, keeping its default learning_rate = 1.0 and loss = 'linear'.
* The Bagging regressor was applied using the following default values: estimator = Linear Regression (), n_estimator = 10, and random_state = 42.
* The Voting Regressor combines the ExtraTreesRegressor, AdaBoost Regressor, and Linear Regression. All individual models were run at their default settings.
* Stacking-employed base models (ExtraTreesRegressor, AdaBoost, and Linear Regression) used Linear Regression as the final estimator.
## 3 Model and Background
### Attack Model  
A malicious attacker can launch a wide variety of attacks to break the privacy and integrity of aggregation results. In general, it is impossible to prevent all kinds of attacks. This paper only considers the following categories of attacks in WSNs:  
* Eavesdropping: It is the most common and easiest form of attack on data confidentiality. An attacker attempts to obtain private information by overhearing the transmissions over its neighboring wireless links. We assume the attacker can eavesdrop on the entire network.
* False data injection: This can possibly occur during data aggregation or data forwarding. A compromised node can distort data integrity by injecting false data and then drain the limited energy resources of the network. A joint data aggregation and false data detection technique has to ensure that data are changed by data aggregation only.
* Sybil attack: It is a type of attacks where the attacker is able to present more than one identity within the network. An adversary can launch a Sybil attack and generate \(n\) or more witness identities to make the base station accept the aggregation results.  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Meaning** \\ \hline \(T\) & Network security factor \\ \(A_{b}\) & Backward data aggregator \\ \(A_{c}\) & Current data aggregator \\ \(A_{n}\) & Next data aggregator \\ \(N_{i}\) & Neighboring node \(i\) of aggregator \\ \(F_{j}\) & Forwarding node \(j\) of aggregator \\ \(M_{l}\) & Monitoring node \(l\) of aggregator \\ \(K_{group}\) & Group key of MFN-group \\ FH(\(X\)) & Fully homomorphic value of node \(X\) \\ subMAC(FH(\(X\))) & subMAC of node \(X\)’s fully homomorphic value \\ \hline \end{tabular}
\end{table}
Table 2: Notations used in FESA, where the value of \(T\) depends on security requirements, node density, packet size, and the amount of tolerable overhead.
## 2 Data Source and Experimental Methodology
### Experimental Comparison Models  
#### 2.4.1 BP Neural Network Model  
Back propagation neural network (BPNN) is a forward-propagation neural network model trained based on the back error propagation algorithm. Through training, it learns the inherent feature relationship between the input vector and the output vector, and continuously updates the model weights through the gradient descent algorithm to achieve non-linear mapping between input features and output values [14]. The BPNN model in this experiment consisted of an input layer, a hidden layer, and an output layer. The neuron node of input layer was set to 55, the number of hidden layer neurons was 200, the output layer neuron node was set to 1, and the learning rate was set to 0.005. The Relu(Rectified Linear Units) function is used as the activation function of the hidden layer. The maximum number of training iterations was 1000 times.
## 3 Security Issues Facing CVs and Solutions
### Vanet  
VANET (vehicular ad hoc network) is built on Wireless Access for Vehicle Environment (WAVE) and specifies a 5.85-5.925 GHz frequency band dedicated for vehicle communication. VANET natively supports V2V and V2I communications in the ad hoc mode, and enables efficient information exchange among vehicles, other end devices and public networks, and thus it plays a critical role in road safety and infotainment, self-driving systems, and intelligent transportation systems [58]. However, the drawbacks of VANET also include the prohibitive cost to construct new infrastructures, scalability issues, and the lack of deterministic quality of service guarantees, etc. Due to the high mobility of vehicles, there is a short communication time between a vehicle and the other entity. Therefore, VANET requires low communication delay and low tolerance for errors. Much work has been dedicated to the specific security vulnerabilities of VANET and possible solutions due to its importance to the improvement in traffic safety [59; 60; 61], which is shown in Figure 4. Thus, we will focus on the remaining issues.