esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Two-Level Decision Fusion System
#### 3.3.1 Dempster-Shaffer Theory of Evidence  
In the frame of the D-S theory of evidence, \(\Theta\) denotes a finite, non-empty set of exhaustive and mutually exclusive hypotheses. \(\Theta\) is often called the frame of discernment, where each hypothesis denotes a particular state that can be reported by detectors. Let \(\theta_{i}\) denote an element of \(\Theta\), and \(2^{\Theta}\) the powerset of \(\Theta\) such that:  
\[\Theta=\{\theta_{1},\theta_{2},\theta_{3},\ldots\},\] \[2^{\Theta}=\{\mathcal{O},\{\theta_{1}\},\{\theta_{2}\},\ldots, \{\theta_{1},\theta_{2}\},\{\theta_{1},\theta_{3}\},\ldots,\Theta\}. \tag{4}\]  
The basic probability assignment (BPA) function, also known as the mass function \(m\), assigns a belief value to each element of the powerset \(2^{\Theta}\), and is defined as follows:  
\[m(\mathcal{O})=0,m(H)\geq 0,\sum_{H\subseteq\mathbb{Z}^{\Theta}}m(H)=1, \forall H\subseteq 2^{\Theta}. \tag{5}\]  
By leveraging the mass function \(m\), one can assign probabilities to the elements of the powerset. This is a significant advantage of the D-S framework, since other approaches such as the Bayesian, assign probabilities to single elements of \(\Theta\). Subsequently, the D-S theory's flexibility provides an approach to distinguish certain system states, but it also provides the ability to express _ignonance_. For example, we might know that evidence points to hypothesis \(H=\{\theta_{1},\theta_{2}\}\) with a high probability but at the same time, it might provide no information (complete ignorance) whether the system is in \(\theta_{1}\) or \(\theta_{2}\). Furthermore, it is crucial that the \"Theory of Evidence\" calculates the probability that the evidence supports a hypothesis rather than calculating the probability of the hypothesis itself (like the traditional probabilistic approach).  
The D-S theory of evidence provides the means to combine (e.g., fuse) independent evidence (e.g., \(m_{1}\), and \(m_{2}\)) on two hypotheses \(B\) and \(C\) into a single evidence:  
\[m_{1,2}(H)=m_{1}(H)\oplus m_{2}(H)=\frac{1}{1-K}\sum_{B\cap C=H}m_{1}(B)m_{2} (C), \tag{6}\]  
where \(K=\sum_{B\cap C=\emptyset}m_{1}(B)m_{2}(C)\). An obvious extension of the formula above is the combination of multiple hypotheses. That is, \(m_{1}\) and \(m_{2}\) are first combined into \(m_{1,2}\), which is then combined with \(m_{3}\) to obtain \(m_{1,2,3}\), and so on. For a set of mass functions \(m_{1},m_{2},\ldots,m_{N}\), the fusion on hypothesis \(H\) is defined as:  
\[m_{1,2,\ldots,N}(H)=((m_{1}(H)\oplus m_{2}(H))\oplus)\ldots\oplus m_{N}(H)). \tag{7}\]
## 3 Results and Analysis
### Discussion  
This paper analyzes the variations in the AE \(b\)-value and the characteristic parameter \(\gamma\)-value using the flicker noise spectrum method during the process of rock failure under uniaxial compression. Simultaneously, the study investigates the relationship between the AE events and loading time on a time scale. By comparing with existing AE \(b\)-value, the research emphasizes exploring the potential of \(\gamma\) and \(\beta_{t}\) values as indicators for predicting dynamic hazards in rocks. These research findings provide a fresh perspective for understanding the fracture and instability behavior of rocks, offering theoretical support for predicting dynamic hazards in these materials. In this section, an analysis is conducted on the relationships and disparities between the results of this study and existing research, along with the reasons behind these disparities.  
[PERSON] (2018) and [PERSON] (2019) have observed interesting \(1/f\) (\(\gamma=1\)) scaling in the power spectra of monitoring signals as they transition from a critical state to catastrophic failure. The consistency of these findings with our study demonstrates relatively consistent results across different research domains. However, it is worth noting that this paper also found some other interesting phenomena. In this paper, when the catastrophic failure of rock occurs, \(\gamma\) is not around 1.0 but exceeds 1.0, which seems to indicate that the failure process of rock does not always satisfy the law of \(1/f\) noise (\(\gamma=1\)).  
This is related to the window length used in calculating the \(\gamma\)-value. Similar to the \(b\)-value, calculating the \(\gamma\)-value requires an appropriate window length. This is because the \(\gamma\)-value reflects the relationship between the power spectrum of the signal over a certain time interval and its frequency. If the time window is too small, the calculated results may not effectively represent the frequency-domain characteristics of that time period. On the other hand, if the time window is too large, it cannot accurately reflect the real-time damage process of rocks during the loading process.  
The proposed \(\beta_{t}\)-value in this work can reflect the evolution of rock damage processes over time scales. Previous research ([PERSON] et al., 2017) suggested that the specimen is in a quasi-stable state when the \(\beta_{t}\)-value is 1. Interestingly, the findings of this study indicate that when the \(\beta_{t}\)-value is 1, the specimen is in a stable or quasi-stable state. This is because the \(\beta_{t}\)-value is closely associated with the number of AE events and the loading time. Even if the specimen is in a stable state, there may be a phase where the AE amplitude is small, but the number of AE events and time increase simultaneously, resulting in a \(\beta_{t}\)-value of 1. In such cases, the specimen is considered to be in a stable state.
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 3 Steps of the Proposed Motion Planner  
In this study, we assume that the 3D shape of the object is given. In addition, we assume that a dual-arm robot with ball-shaped EEFs manipulates the object at two contact points. To manipulate the object to the target pose, a hierarchical manipulation planner is built at the task and motion levels, which outputs the planned motions. The proposed motion planner is designed according to the following steps:  
* Before executing the motion planner, the 3D model of the target object is analyzed to obtain the information necessary for the manipulation planner. This information includes the potential rotational vertices used for pivoting, edges used for tumbling, base surfaces for stably contacting the table, and the contact points between the object and EEFs;
* Task level planning then is performed. We discretize the object poses on a table. The object configurations along with their grasp configurations are saved in the graph nodes. In this phase, we consider object poses in both SP and USP;
* Finally, in motion level planning, primitive motions, such as pivoting, tumbling, and regrasping, are planned for moving the object to the target location. In the motion level, the designed motions include pivoting, tumbling, and regrasping. MPC is implemented to generate the motions because it can find the motions required for maintaining contact between the object and environment. If we cannot find any feasible motion in this level, we go back to the task level planning.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Access-Control Systems
#### 4.6.2 On DICE-Free Smart Cities, Particulate Matter, and Feedback-Enabled Access Control [31]  
This study introduces a feedback-enabled access-control mechanism to mitigate particulate matter (PM) emissions in urban areas by regulating vehicle access and promoting ride-sharing schemes. Unlike traditional LEZ policies, which often focus on tailpipe emissions, this system also addresses non-exhaust emissions such as those from tire and brake wear, a growing concern with the increased use of heavier electric vehicles. Key contributions are as follows:  
* PM Emissions from Non-Exhaust Sources: The study highlights that tire-related PM emissions in cities like Dublin might already exceed levels deemed safe by the World Health Organization (WHO), even without internal combustion engine (ICE) vehicles.
* Feedback-Based Regulation: It introduces an adaptive control mechanism to regulate vehicle access based on real-time PM emission levels, ensuring emissions remain below safe thresholds while maximizing city access for vehicles and passengers.
* Ride-Sharing Scheme: It encourages carpooling by integrating a probabilistic selection method for vehicles based on occupancy, prioritizing fully occupied cars for city access.
* Digital Token Compliance System: A digital token system is used as a compliance mechanism to ensure adherence to ride-sharing agreements. Tokens are forfeited for non-compliance, such as failing to pick up passengers or promoting cooperative behavior.
* Simulation Results: It demonstrated through simulations that the system can maintain PM levels within safe boundaries while ensuring fair access for drivers and passengers over extended periods.  
The feedback mechanism and compliance-driven ride-sharing model provide an innovative pathway for reducing urban PM emissions while balancing mobility needs and environmental safety.
## 3 Proposed Method
### Loss Function  
The discriminator in the traditional CGAN method [20] is used to judge the authenticity of sonar samples in the generator. The model is prone to problems such as mode collapse and training instability during the training process. In this paper, the method of combining the Wasserstein distance and gradient penalty mechanism is used to enhance the stability of model training and improve the quality of the generated samples. The Wasserstein distance between the real sample distribution and the fake sample distribution is defined as follows:  
\[W\big{(}P_{r},P_{g}\big{)}=\inf_{\gamma\in\prod\big{(}P_{r},P_{g}\big{)}}\mathrm{ E}_{(x,y)\sim\gamma}\|x-y\| \tag{13}\]  
In the formula, \(x\) and \(y\) represent the real sonar sample and the generated sample, respectively; \(\prod\big{(}P_{r},P_{g}\big{)}\) represents all possible joint distributions \(\gamma(x,y)\), where the marginal distributions of each joint distribution are \(P_{r}\) and \(P_{g}\).  
According to the distance loss function, the loss functions of the discriminator and generator networks, \(L_{D}\) and \(L_{G}\), can be expressed as the following forms, where \(c\) represents the category condition information of the sonar image:  
\[L_{D}=\mathrm{E}_{x\sim P_{g}}[D(x|c)]-\mathrm{E}_{x\sim P_{g}}[D(x|c)] \tag{14}\]  
\[L_{G}=-\mathrm{E}_{x\sim P_{g}}[D(x|c)] \tag{15}\]  
The overall model training process can be interpreted as a minimax confrontation game between the generator and the discriminator network. The training results are balanced by optimizing the discriminator and generator losses. The overall model training process is expressed as  
\[\min_{G}\max_{D}\mathrm{E}_{x\sim P_{g}}[D(x|c)]-\mathrm{E}_{x\sim P_{g}}[D( x|c)] \tag{16}\]  
On the basis of this loss function, the gradient of the discriminator is limited to a fixed range by introducing a gradient penalty mechanism. The improved loss function is expressed as follows:  
\[L_{D}=\mathrm{E}_{x\sim P_{g}}[D(x|c)]-\mathrm{E}_{x\sim P_{r}}[D(x|c)] \tag{17}\]  
\[L_{G}=-\mathrm{E}_{x\sim P_{g}}[D(x|c)] \tag{18}\]Here, \(\widehat{x}=\varepsilon x_{r}+(1-\varepsilon)x_{g}\), \(\lambda\) is the gradient penalty coefficient, and \(\varepsilon\) is a random number that obeys a uniform distribution, that is, \(\varepsilon\sim U[0,1]\).
## 3 Hotline-Based Reliable WSN Topology
### Border Nodes  
_Definition 1: Border nodes_ are the nodes that lie on the overlapping region of two or more clusters.  
The nodes that lie in the communication range of one or more gateways can associate with either of the gateways depending on different factors (_i.e.,_ signal strength, shortest path etc [6]). Moreover, in a scenario where the source to sink communication is only a few hops but source-gateway-gateway-sink communication has more number of hops then it would be expensive to communicate using the longer path (_i.e.,_ hotline assisted). It would be expensive in terms of overall latency and energy consumption. This case is likely to be true if both the source and the sink are on the border (or overlapping region) of two different clusters. We call these nodes as border nodes and propose a slightly different routing and gateway association mechanism between them. This mechanism would not only help towards less latency between the source and the sink but also would provide an efficient gateway association. The first task is therefore to identify the border nodes.  
For simplicity, we take the example of two clusters and two associated default gateways. It can be seen from Figure 4 that nodes in the overlapping region of two clusters are defined as border nodes. The sample node lies in the overlapping region of cluster 1 and cluster 2 because it receives two RA  
Figure 4: Identifying Border nodes.  
Figure 3: Traditional unreliable route and hotline assisted route.  
messages from two routers. This node is now responsible for two decisions one is to join a particular gateway and the other is to broadcast or forward the RA messages of the gateways. This decision depends on the value of Cur TTL field in the RA message. Let us take an example scenario. If the node gets a RA message from gateway 1 with Cur TTL value of 252 and another RA message from gateway 2 with Cur TTL of 250, the node would join the gateway with a higher value of Cur TTL and at the same time would broadcast the RA message. Note that the Cur TTL field represents the hop count towards the gateway.  
In a scenario where a node receives two RA messages from two different gateways with the same value of Cur TTL it can join either of the gateways. The node would forward both the RA messages to its neighboring nodes.  
If border nodes have to send a packet, for instance, they would check from the neighbor table whether the packet is destined to their first hop neighbor. If the packet is destined to the first hop neighbor they would deliver it directly no matter the neighbor belongs to other cluster. This way the latency would reduce and for the applications requiring prompt response, this communication would be more reliable. The comparison of routes is shown in the figure below. However, this approach is focused only on the special case of the border nodes. In order to cover the complete network for the problem of increased end-to-end latency, we present a generic routing scheme SBR in section 6.
# Study on Low Temperature Drying Characteristics of Chicken Manure in a Humid Environment
###### Abstract  
Drying chicken manure with exhaust air from a chicken house is a new manure management method developed in recent years. The majority of the heat for this drying method comes from the waste heat of ventilation in a poultry house or ambient air, so the drying process is carried out at a low temperature. When met with continuous rain or when the wet curtain in the chicken house is turned on, the relative humidity of the exhaust air from the chicken house will rise to high levels, and the drying process will be under the condition of high relative humidity. In order to explore the low-temperature drying characteristics of chicken manure in a humid environment, drying experiments were carried out in an experimental cross-flow drying system. The experiment mainly studied the effect of process parameters such as drying temperature, air velocity, and manure layer thickness on the low-temperature drying characteristics of chicken manure in a humid environment. The results showed that the low-temperature drying process of chicken manure in a humid environment only had a falling rate period, with no accelerated or constant rate period. Additionally, five common thin-layer drying models ([PERSON] model, [PERSON] model, [PERSON] model, [PERSON] model, and Exponential model) were applied to simulate the performance of the manure drying process. The exponential model was considered to be more suitable for describing the low temperature drying process in a humid environment, and the effective diffusivity changed between 6.37 \(\times\) 10\({}^{-7}\) and 2.17 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/h.  
chicken manure; low temperature drying; drying model 2023
## 3 Results and Discussion
### Biological Evaluations
#### 3.2.2 Anticholinesterase Activity  
The results for acetylcholinesterasecholinesteraseterase (AChE) inhibitory activity showed that compound **7** is the only one which presents some activity at the maximum concentration tested (47.1 \(\pm\) 3.6%, at 150 \(\upmu\)g/mL). This activity is less pronounced than the inhibition obtained for control compound donepezil (95.2 \(\pm\) 0.4% at 50 \(\upmu\)g/mL), a pure competitive inhibitor of AChE used clinically in early stages of [PERSON]'s disease. The results suggest that the cyclization seems to increase the inhibitory activity of the compound, since compound **7**, which is a flavanone, is more active than compound **6**, the respective chalcone. Also, the existence of free hydroxyl groups in both A and B rings seems to be important because, out of the three compounds with a flavanone structure (compounds **4**, **5** and **7**), only the one with a free hydroxyl group in both C-7 and C-4' (compound **7**) shows activity. This is in agreement with the strong activity revealed by quercetin, whose structure also possesses free hydroxyl groups in both rings A and B, like compound **7**. So, it can be concluded that the presence of hydroxyl groups in rings A and B of the flavanone scaffold is crucial for the inhibition of AChE activity (Figure 4).  
In terms of butylcholinesteraseerase inhibition, compound **6** presented excellent activity, with a percentage of inhibition of 96.0 \(\pm\) 1.1% and IC\({}_{50}\) of 26.55 \(\pm\) 0.55 \(\upmu\)g/mL, which are similar values to the ones presented by donepezil (IC\({}_{50}\) of 28.94 \(\pm\) 1.76 \(\upmu\)g/mL) and better than quercetin at the same concentrations. This shows that, unlike what happens with acetylcholinesterasecholinesterase, the presence of three hydroxyl groups in the chalcone scaffold highly favors the inhibition of butylcholinesteraseerase activity. The results presented by chalcones **1** (12.51 \(\pm\) 0.82%) and **8** (0%) also confirm that, like with acetylcholinesterasecholinesteraseerase, the presence of methoxyl groups does not confer inhibitory activity to chalcones.  
Compound **7** presents an activity very similar to the one observed for acetylcholinesterasecholinesteraseerase inhibition (46.26 \(\pm\) 1.27%), meaning that it is a dual inhibitor, a much-appreciated feature in the search for compounds with therapeutic potential towards [PERSON]'s disease. The presence of a hydroxyl group in ring B of the flavanone scaffold increases its inhibitory effect, since flavanones **5** (36.30 \(\pm\) 0.20%) and **7** present higher activity than flavanone **4** (9.00 \(\pm\) 1.30%), which has a methoxyl group in said position (Figure 4). The compounds inhibited butylcholinesteraseerase in a dose-dependent manner (Figure S12, supplementary material).
## 2 Instrument Design
### Monitoring and Control Systems
#### 2.3.4 Switch Control  
As mentioned above, the switches and latching circulators select the polarization and the radiometric input (antenna or matched load) for the different frequency bands. A microcontroller with Ethernet connection allows to control from the host computer the 6 switches and the 10 latching circulators that determine the radiometric input, and the 2 switches that select the polarization (see Figure 1).
# Generation and Distribution of Quantum Oblivious Keys for Secure Multiparty Computation
### Secure Multiparty Computation and Oblivious Transfer  
Consider a set of \(N\) agents and \(f(x_{1},x_{2},\ldots,x_{N})=(y_{1},y_{2},\ldots,y_{N})\) a multivariate function. For \(i\in\{1,\ldots,N\}\), a SMC service (see Figure 1) receives the input \(x_{i}\) from the \(i\)-th agent and outputs back the value \(y_{i}\) in such a way that no additional information is revealed about the remaining \(x_{j},y_{j}\), for \(j\
eq i\). Additionally, this definition can be strengthened by requiring that for some number \(M<N\) of corrupt agents working together, no information about the remaining agents gets revealed (secrecy). It can also be imposed that if at most \(M^{\prime}<N\) agents do not compute the function correctly, the protocol identifies it and aborts (authenticity).  
Some of the most promising approaches towards implementing SMC are based on oblivious circuit evaluation techniques such as [PERSON]'s garbled circuits for the two party case [5] and the GMW or BMR protocols for the general case [2; 6; 7; 8]. It has been shown that to achieve SMC it is enough to implement the Oblivious Transfer (OT) primitive and, without additional assumptions, the security of the resulting SMC depends only on that of the OT [9]. In the worst case, this requires each party to perform one OT with every other party for each gate of the circuit being evaluated. This number can be reduced by weakening the security or by increasing the amount of exchanged data [10]. Either way, the OT cost of SMC represents a major bottleneck for its practical implementation. Finding fast and secure OT protocols, hence, is a very relevant task in the context of implementing SMC.  
Let [PERSON] and [PERSON] be two agents. A 1-out-of-2 OT service receives bits \(b_{0}\), \(b_{1}\) as input from [PERSON] and a bit \(c\) as input from [PERSON], then outputs \(b_{c}\) to [PERSON]. This is done in a way that [PERSON] gets no information about the other message, i.e., \(b_{\overline{c}}\), and [PERSON] gets no information about [PERSON]'s choice, i.e., the value of \(c\)[11].