esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Sensors
#### 3.3.5 Sensors for Waste Monitoring  
Smart waste management consists of resolving the inherent problems of collection and transportation, storage, segregation, and recycling of the waste produced. Use of smartphones, solutions for the Vehicle Routing Problem (VRP) and waste management practices have been reported [41; 118]. The use of smartbins refers to the implementation of different kind of sensors in the bins used to collect waste, which provide quantitative and qualitative information about the bin content [119; 120; 121].  
For the VRP, the proposals are algorithms for optimization of the routes, considering social, environmental, economic factors, peak hours, infrastructure, type and capacity of the collection vehicles and others, in an effort to save resources like money, time, fuel, and labor [122; 123; 121].  
With this, researchers aim for an integrated, real-time management that involves the communities and all the stakeholders [124; 125]. The principal use of sensors in smartbins is monitoring the volume, weight, and content of the bins. For monitoring the filling level of the container, the main approaches that have been used are ultra-sound (US) [43; 119], and in some cases IR sensors [121; 126]. A load cell is also used to detect the weight of the bin [124; 127]. In the literature, various sensors are used to detect harmful gases [126], movements near the container [120], and metal sensors to separate metallic waste [128], and to measure humidity [126; 128], as well as temperature [43; 123]. The My Waste Bin IoT container presented in [43] is shown in Figure 8.  
Figure 8: Front and back view of the My Waste Bin, an IoT smart waste container, enabling real-time, GPS tracking and weight monitoring [43].  
Figure 7: Outer and inner view of an integrated IoT sensor for water quality monitoring applications. The sensor consists of a nitrite and nitrate analyzer based on a novel ion chromatography method, used for detection of toxic substances [112].
# Automated Code-Smell Detection in Microservices Through Static Analysis: A Case Study
## 6 Case Study
### Validity threats
#### 6.3.2 External Threats  
Our application was run on two open-source benchmark applications that were similar to real-world conditions to make our test as applicable as possible. Our analysis utilizes established enterprise standards. Thus, if applications follow the best practice standards, they are analyzable by our system.  
Both of the benchmark applications used in the case study are primarily written in Java. However, microservice architecture usually follows polyglot programming styles. We utilized bytecode and source code analysis in our tool to show that it can support both interpreted and compiled languages. We used Java Parser and Javassist for parsing Java source code and bytecode, respectively. Similar parsing tools are available for other modern languages; for example, Python and Golang have a built in parser package to obtain AST from the source code. Provided that a language has an appropriate parser, our tool can be extended to support a wide variety of languages used in MSA.  
We designed generic interfaces to analyze and detect code smells. In our case study, we chose our first benchmark application (Train Ticket) randomly from a list of exclusively designed applications for benchmarking [78]. Then, we implemented those interfaces for Spring boot and enterprise Java since the chosen benchmark follows these standards. To verify our prototype is not application-specific, we chose our second benchmark application (Teacher Management System) that follows similar standards. However, there might exist different standards in other languages. To address this, we need to implement the interfaces for those specific standards.  
Modern cloud-native microservices are usually packaged as containers and deployed using orchestration platforms such as Kubernetes, Cloud Foundry, Docker Swarm, etc. During containerization, source codes are not typically included; only the compiled artifacts such as JAR or EXE files are added into the containers. It is still possible to perform bytecode analysis for containerized microservices by extracting the bytecode artifacts (e.g., JAR file) from container layers [53]. For this approach, we also need to analyze the deployment configuration files to identify service names associated with each microservices [53]. However, additional hard-coded dependencies in container images might require further research to identify them properly. In addition, for compiled languages, source code analysis is not possible within a containerized environment.
## 1 Introduction
studied the influence of steel slag filler on the self-healing properties of the asphalt mixture using a fatigue-healing-fatigue test. The results show an enhancement in the healing properties of the mixture containing steel slag [2]. [PERSON] et al. used coarse steel slag and steel wool fibers in the asphalt mixture and evaluated the healing rate through three-point bending test [41]. The main result was that the addition of 30% steel slag increased the healing properties of the asphalt mixture. Recently, with the advent of nanotechnology, some nanomaterials were used in asphalt mixtures. Several mechanical properties can be improved by adding some nanomaterials, such as nanosilica, nanoclay and nanorion [46]. Recently, carbon and graphene family nanomaterials have been used for asphalt modification [47], such as graphene nanoplatelets (GNPs). The addition of GNP in the mixture leads to an improvement in flexural strength at low temperatures, better performance at high temperatures [48] and easier compaction [49]. However, only a few studies focused on the microwave heating and healing efficiency of asphalt mixture containing graphene nanomaterials. [PERSON] et al. used graphene to improve the microwave heating and healing properties of bitumen [8]. The results showed that graphene provides benefits in terms of heating and healing performance. The objective of this paper is to evaluate the effect of adding Electric Arc Furnace (EAF) slag and Graphene Nanoplatelets (GNPs) on the microwave heating and healing efficiency of asphalt mixtures. This research is the continuation of the study carried out by [PERSON] et al. [50], where preliminary results of the heating efficiency of these additives were obtained. The graphene nanoplatelets were incorporated as a binder additive, while the EAF slag was added as partial replacement of the natural aggregates. The asphalt mixtures heating efficiency was evaluated using the ratio \({}^{\circ}\)C/kWh/kg and, in addition, the electrical resistivity was measured to understand the effect of conductivity losses in the heat generation process. The healing efficiency was studied by applying microwave energy to damaged specimens and evaluating the healing recovery using the Indirect Tensile Strength (ITS) test.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 9: Command and Control Post  
Command and control posts (CCPs) are establishments where military personnel strategize, oversee, synchronize, and manage forces and operations to achieve mission objectives. These points serve as hubs for extensive information. Neuromorphic processors offer the capacity to handle this substantial volume of data, while trusted computing ensures the integrity of the information. Additionally, the post can be integrated into IoT networks or designated as one of the slices within network slicing.
# Levitating Control System of Maglev Ruler Based on Active Disturbance Rejection Controller
## 2 Analysis Model of Levitating Force
### Dynamic Model of Levitating System
Since the left and right side levitating forces are two multivariable, nonlinear functions, the formula is very complex and requires approximate conversion. The conversion method is: (1) Assuming that the MC is at the zero point of the plane; (2) At the zero point of the levitation position, namely \([z_{l},\,z_{r}]=[0,\,0]\), Taylor expansion is performed, and the high-order terms after the first term are truncated, resulting in simplified equations for the left and right end levitating forces of the MC as follows:  
\[F_{left}=23.11\times i_{a}-13600\times z_{l}-4483\times z_{r} \tag{50}\]  
\[F_{right}=23.11\times i_{b}-4483\times z_{l}-13600\times z_{r} \tag{51}\]  
In Equations (50) and (51), \(i_{a}=i_{3}-i_{1}\), and \(i_{b}=i_{4}-i_{2}\). \(i_{1}\), \(i_{2}\), \(i_{3}\), and \(i_{4}\) are the currents of the four levitating coils, respectively. (3) Perform coordinate system transformation, and the transformation result is as follows:  
\[F_{left}=23.11\times i_{a}-18083\times z_{a}+1162.42\times\psi \tag{52}\]  
\[F_{right}=23.11\times i_{b}-18083\times z_{a}-1162.42\times\psi \tag{53}\]Substituting Equations (52) and (53) into Equation (46), the analysis model of the levitating system of the MR is obtained after sorting out:  
\[ \begin{bmatrix}-36166&0\\ 0&296\end{bmatrix}\begin{bmatrix}z_{a}\\ \psi\end{bmatrix} + \begin{bmatrix}23.11&23.11\\ -2.9465&2.9465\end{bmatrix}\begin{bmatrix}i_{a}\\ i_{b}\end{bmatrix} + \begin{bmatrix}17.93\\ 0\end{bmatrix} = \begin{bmatrix}1.83&0\\ 0&0.0273\end{bmatrix}\begin{bmatrix}\frac{\partial^{2}z_{a}}{\partial t^{2}} \\ \frac{\partial^{2}\psi}{\partial t^{2}}\end{bmatrix}  \tag{54}\]  
From Equation (54), it can be seen that the levitating system is a nonlinear system with two inputs and two outputs. Moreover, the system has strong coupling. Conventional linear algorithms cannot achieve effective control of the system. For the levitating system, ADRC is a more suitable controller.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### Narrowband IoT  
IoT will be a crucial part of future cellular networks. It is estimated that 212 billion IoT devices will be connected in the near future. Therefore, the aggregated traffic from these devices would account for almost half of all internet traffic [72]. Few studies have investigated C-RAN resource management-integrating IoT devices. A new RAN technology introduced by 3 GPP, called narrowband Internet of Things (NB-IoT), is implemented on a flexible software-defined radio-based C-RAN in [71]. NB-IoT has the benefits of less complexity in transceiver design, low power consumption, reduced cost for the radio chip, and increased coverage. Relaxed latency requirements along with simplified baseband processing complexity make NB-IoT attractive for C-RAN platform. However, there exist some challenges for this technology that requires further investigation, and it can be a potential research area for future researchers. Another work presented in [73] utilized C-RAN architecture in ambient backscatter communication, to jointly cope with direct-link interference suppression and imperfect channel estimation. In this architecture, secondary edge nodes give network access to ambient backscatter passive and semi-passive sensors with communication capabilities, and the channel estimation and direct-link interference suppression are managed by the cloud processor.  
Although these studies were able to manage the IoT support in cellular networks from the perspective of centralized IoT transmission coding/decoding, further investigation is needed considering IoT optimized control procedure. In addition, as RRHs are densely deployed in C-RAN network, they may become congested when a small number of RRHs are overburdened with IoT devices. Moreover, the mobility of IoT devices would deteriorate the situation [74]. It is essential to develop techniques to support massive connections as well as ensure low-latency and highly reliable data transmission. To address these issues, an H-CRAN can be developed as an effective solution in conjunction with edge computing capability. Low latency can be provided by shifting data analysis and decision making to the network edge.
## 3 Methodology  
PV power generation substantially fluctuates with meteorological factors, such as solar irradiance, temperature, wind speed/direction, and cloud changes. For an accurate PV power prediction, we propose a novel dual-encoder transformer (DualET) to capture context features from these factors. As shown in Figure 1, DualET has an encoder-decoder architecture like most transformers but with dual encoders. One encoder, i.e., local seasonal information encoder (LSI encoder), is to model temporal dynamics from in situ measurements. The other, i.e., remote-sensing information encoder (RSI encoder), is to learn the spatial and temporal features of clouds from satellite images. The output of the LSI encoder and that of the RSI encoder contain fluctuations in the local measurements and clouds, respectively. Both of them can be combined to provide comprehensive fluctuation information for modeling the changes in the PV power series. Further, we design a joint feature decoder to predict future short-term PV power. The details of DualET will be introduced in the following subsections.
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
## 1 Introduction
### Evidence from Collaborative Memory Tasks
With regard to the effect of prior collaboration on subsequent individual recall, findings of the study mentioned above [17] showed that only participants who collaborated in turn taking (high pressure group) later recalled comparable levels of critical lures in an individual trial. More recently, [PERSON] et al. [7], using word lists, assessed the recall accuracy (items recalled in error) and recall completeness (study material accurately recalled) in turn-taking and consensus groups (during and after collaboration). Regarding the recall accuracy, results showed that only consensus groups, compared to the nominal and rotational groups, brought advantages, both during and after collaboration. Authors believed that this benefit in consensus groups was due to a group error-checking process. As regards the recall completeness, findings indicated that consensus and turn-taking groups resulted in collaborative inhibition (i.e., both recalled lesser words from the list compared to nominal groups). After the collaboration, both groups increased individual recall. Authors argued that this benefit in post-collaboration could be due to the reexposure to list items. Individual variables, such as stress levels and sex, have also been studied using the DRM paradigm. For instance, the two studies of [PERSON] et al. (2018) were addressed to analyse the effect of stress in the recall of false memories in the DRM paradigm, and whether such effect might be modulated by sex. Results highlighted that neither the \"stress group\" nor the high cortisol responders showed a greater number of false recalls and false recognition of critical lures than the control group and the low cortisol responders. Moreover, while sex did not modulate the effect of stress on the performance, studies suggested that, regardless stress, men evoked more extra list words than women.
## 4 Design of the Equalizers
### Nonlinear Equalizer  
Although nonlinear equalizers are not the subject of this paper, it can be mentioned that it is considered another type of equalizer architecture. An example of a nonlinear equalizer is the decision feedback equalizer (DFE). This consists of a transverse filter and a recursive one [42].  
The input of this filter is represented by the symbols determined previously, and which are used to eliminate the ISI from the current estimate. A block diagram of the decision feedback equalizer is given in Figure 5.  
In this structure, C(z) mainly serves to minimize the ISI on the current symbol caused by the future symbols, while the recursive part Q(z) synthesizes the ISI caused by the symbols passed and subtracts it from the signal before the decision.  
The DFE equalizer output is expressed as  
\[\hat{x}(n)=\sum_{k=-\infty}^{0}c_{k}y(n-k)+\sum_{k=1}^{+\infty}q_{k}\widetilde {x}(n-k), \tag{8}\]  
There are two rules for determining the optimal settings for these equalizers: the forcing criterion to zero, and the minimum mean square error [15].
# An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
## 3 Results
### Simulation of Sensor Effectiveness with Dynamic Changes  
This section focuses on integrated INS/GPS navigation within Scenario 1, assuming a synchronous output frequency between INS and GPS, with the GPS measurements being unavailable during the period from 90 s to 160 s. The objective is to compare the fusion accuracy of the Kalman filtering (KF) and factor-graph-based (FG) method without time delay in measurement. The evaluation specifically examines the factor graph algorithm's capacity to accommodate dynamic changes in sensor effectiveness. Figure 9 shows the real-time position error of both KF and FG.  
In this experiment, GPS measurements were not available from 90 to 160 s, and the efficacy of sensors in the integrated navigation system underwent two changes. The integrated navigation system transitions from INS/GPS mode to INS individual mode when the GPS measurements are unavailable at 90 s. The inclusion of factor node \(f^{GPS}\) is inhibited in the factor graph model, resulting in a rapid accumulation of position errors due to the lack of INS correction. Subsequently, as the GPS signal becomes effective again at 160 s, the integrated navigation system shifts from INS individual mode to INS/GPS mode, and the factor node \(f^{GPS}\) is reintroduced to the factor graph model, correcting the position error.  
Upon analysis, it is evident that during transitions between INS and INS/GPS modes prompted by changes in sensor effectiveness, only factor nodes associated with invalidated sensors need to be suppressed or new factor nodes related to effective sensors need to be added to the factor graph model. No additional adjustments are required, ensuring a seamless switch between different modes of the integrated navigation system and enhancing its overall robustness.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 4 Implementation and Evaluation
### Comparison to Other Approaches
#### 4.3.2 Lightweight Certificates  
Certificate-based solutions, such as the Simple Public Key Infrastructure (SPKI) [21], can be used as a mechanism alternative to VCs in our system. The advantage of VCs is that it is easier to extend their data model to include new features. For example, our system uses such a data model extension to include information that can be used for determining the revocation status of a VC. Furthermore, VCs allow using DIDs for describing the VC subject, and when a VC is combined with a DID method, such as _did:self_, many entities can securely share the same VC. Using a certificate-based approach with a certificate that includes the public key of the endpoint owner would require the endpoint owner to issue another certificate for each IoT endpoint. Therefore, if an owner manages \(x\) IoT endpoints, each of which participates in the same \(y\) groups, using our approach, the owner has to generate \(x\) public key pairs and copy \(y\) VCs to each IoT endpoint, whereas in another approach, the owner would have to generate \(x\) public key pairs, sign \(x\times y\) certificates and install \(y\) certificates to each endpoint.
# Rheological Approaches of Wheat Flour Dough Enriched with Germinated Soybean and Lentil
## 3 Results
### Optimization of LGF and SGF Addition Levels  
#### 3.3.1 Diagnostic Checking of the Models  
The data for falling number (FN), dough tolerance to kneading (Tol), dough consistency after 250 s (D250), dough consistency after 450 s (D450), baking strength (W), and curve configuration ratio (P/L) properties were successfully fitted (\(p\) < 0.05) to the quadratic model which explained 96, 74, 66, 75, 86 and 74% respectively of the variations, as the ANOVA results showed (Table 5). The 2 FI mathematical model chosen for water absorption (WA) and dough extensibility (L) data fitting explained 74 and 87% respectively of the variation and it was significant at \(p\) < 0.05 in both cases. Dough tenacity (P) alveolaring results were fitted to the cubic model which was significant at \(p\) < 0.05 and explained 77% of data variation. The quadratic model successfully fitted (\(p\) < 0.05) the data for the maximum height of gaseous production (H'm), the volume of gas retained in the dough at the end of the test (VR), elastic modulus (G'), loss tangent (tan \(\delta\)) and initial gelatinization temperature (T\({}_{\rm i}\)), the variations were explained in proportions of 62 to 98% (Table 6). For total CO\({}_{2}\) volume production (VT) and maximum gelatinization temperature (T\({}_{\rm max}\)) data prediction, the cubic model was found to be adequate (\(p\) < 0.05) with an explained variation of 78 and 96% respectively, while the retention coefficient (CR) values were fitted to the modified cubic model which was significant at \(p\) < 0.05 and explained 61% of data variation (Table 6). \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multicolumn{8}{c}{**Parameters**} \\ \cline{2-10}  & **H’m (mL)** & **VT (mL)** & **VR (mL)** & **CR (\%)** & **G’ (Pa)** & **G’ (Pa)** & **tan \(\delta\) (adim.)** & **T\({}_{\rm i}\) (°C)** & **T\({}_{\rm max}\) (°C)** \\ \hline Constant & 69.57 & 1609.22 & 1283.41 & 79.77 & 49460.86 & 20268.40 & 0.4143 & 50.77 & 75.57 \\ A & \(-3.22\)*** & \(-151.59\) & \(-68.28\)*** & \(-0.3634\) & 16590.80*** & 7117.60*** & 0.0104 & \(-1.65\)*** & 1.94*** \\ B & 0.50 & \(-104.84\) & \(-19.32\) & 3.84 & 4064.80*** & 3569.60*** & 0.0391*** & 0.92 *** & 0.51 * \\ A \(\times\) B & \(-1.06\) & \(-66.40\) & \(-3.52\) & 2.55 & \(-688.80\) & 1040.00** & 0.0115 & 0.15 & \(-0.44\)*** \\ A\({}^{2}\) & 0.21 & 41.77 & \(-50.57\)* & 79.77* & 1932.00* & & \(-0.0288\)* & \(-0.33\) & 0.45* \\ B\({}^{2}\) & \(-3.29\)* & \(-101.66\)* & \(-43.60\) & \(-0.3634\) & 194.29 & & \(-0.0008\) & 0.12 & \(-0.09\) \\ A\({}^{2}\)B & & 185.14** & & 3.84*** & & & & & 0.39 * \\ AB\({}^{2}\) & & \(-39.43\) & & 2.55 & & & & & 75.57 \\ A\({}^{3}\) & & 36.40 & & & & & & & 1.94 *** \\ B\({}^{3}\) & & 11.47 & & & & & & & 0.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ANOVA results of the mathematic models fitted for dough empirical and dynamic rheological properties data.