esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Validation
### Validation on Hourly Time Scale  
Daily maps with mean absolute bias calculated on an hourly time scale (\(MABH\)) are shown in Figure 13. The maps are similar to the daily mean bias maps (Figure 11), also showing the bias patterns associated with dynamic weather systems, which significantly increase by excluding the early afternoon observation, with the absolute bias regionally exceeding 32 Wm\({}^{-2}\) (Figure 13b).  
The \(MABH\) of each day is plotted in Figure 14 and its annual average is listed in Table 5 (fourth column). The resulting statistics exhibit orbital-dependent performance differences similar to those seen for the \(RMSB\) and \(MAB\) on a daily time scale: the default configuration, using all satellites, is characterized by a relatively stable performance with \(MABH\) between 9-10 Wm\({}^{-2}\). However, excluding the early afternoon orbit (NOAA-18) results in a year-round performance degradation, with \(MABH\) ranging seasonally between 11-14 Wm\({}^{-2}\). Exclusion of mid-morning orbits (NOAA-17 and MetOp-A) has an even worse impact, with \(MABH\) ranging between 12-16 Wm\({}^{-2}\). For the additional orbital configurations listed in Table 5, the configuration-specific performance differences echo those obtained with \(RMSB\) and \(MAB\) on a daily time scale.  
Figure 13: _Cont._  
Figure 14: RSF mean absolute bias calculated on an hourly time scale (\(MABH\)) with respect to CERES-SYN1 deg-Hour, with RSF retrieved using (red) all satellites, (blue) all satellites excluding NOAA-18 (early afternoon), (green) all satellites excluding NOAA-17 and MetOp-A (mid-morning).  
Figure 13: RSF mean absolute bias calculated on an hourly time scale (\(MABH\)) with respect to CERES-SYN1 deg-Hour for 15 January 2008, retrieved using (**a**) all available satellites, and (**b**) excluding early afternoon satellite NOAA-18.
## 4 Results
### Qualitative Evaluation
Notably, the generated LCC defect images differed greatly from the original training set, indicating that the GAN model failed to effectively capture the image features. With respect to the DCGAN model, the generated images displayed considerable instability and were prone to collapsing. Even after 2000 training iterations, the generated defect images remained less effective compared to the original images. In contrast, our proposed SATII-DCGAN model could surpass the above two models by learning local features and also by presenting superior texture details.
## 4 Numerical Model
### Influence of Water-Cement Ratio
#### 4.5.3 Analysis of Surrounding Rock Plastic Zone  
Table 5 shows the area of shear and tensile plastic zone of surrounding rock with different water-cement ratios (\(\eta\) = 0.75, 0.80, 0.85, 0.90, 0.95, 1.00). The ratio of the shear plastic area of grouted surrounding rock (\(\eta\) = 0.75, 0.80, 0.85, 0.90, 0.95, 1.00) to the shear plastic area of surrounding rock without grouting was \(\omega_{1}\). The ratio of the area of grouted surrounding rock tensile plastic zone to the area of surrounding rock tensile plastic zone without grouting was \(\omega_{2}\). When the slurry water-cement ratio \(\eta\) = 0.80, \(\omega_{1}\) and \(\omega_{2}\) were 0.37 and 0.15, respectively. When the slurry water-cement ratio \(\eta\) = 0.90, \(\omega_{1}\) and \(\omega_{2}\) were 0.86 and 0.66, respectively. The variation ranges of the shear plastic zone and tensile plastic zone were 49% and 51%, respectively. The shear plastic zone area increased with the increase of the slurry water-cement ratio. When the slurry water-cement ratio \(\eta\) = 0.75, the shear plastic zone area reached the minimum 126.17m\({}^{2}\). When the water-cement ratio \(\eta\) changed from 0.80 to 0.90, the area of the shear plastic zone changed greatly (\(\omega_{1}\) increased from 0.37 to 0.86), but when the water-cement ratio changed in other ranges, the shear plastic zone area changed little. The area of the tensile plastic zone increased with the increase of the water cement ratio. The singularity was that when \(\eta\) = 0.95, the area of the tensile plastic zone was 277.55 m\({}^{2}\), which was larger than the area obtained in the two adjacent working conditions. The smaller the water-cement ratio, the better the grouting reinforcement effect, the better the self-supporting capacity, and the lower the possibility of yield failure.  
Figure 22: Relationship between the maximum displacement of monitoring points on the vault and water–cement ratio.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Grouting Reinforcement** & \(\eta\) = 0.75 & \(\eta\) = 0.80 & \(\eta\) = 0.85 & \(\eta\) = 0.90 & \(\eta\) = 0.95 & \(\eta\) = 1.00 \\
**Layer Thickness** & & & & & & \\ \hline Shear plastic zone area (m\({}^{2}\)) & 126.17 & 142.21 & 245.67 & 332.10 & 338.17 & 360.69 \\ \(\omega_{1}\) & 0.33 & 0.37 & 0.64 & 0.86 & 0.88 & 0.94 \\ Tensile plastic zone area (m\({}^{2}\)) & 22.41 & 44.02 & 113.13 & 188.00 & 277.55 & 248.25 \\ \(\omega_{2}\) & 0.08 & 0.15 & 0.40 & 0.66 & 0.97 & 0.87 \\ \hline \end{tabular}
\end{table}
Table 5: Shear and tensile plastic zone area of surrounding rock under different water–cement ratios.
# Unary Quantum Finite State Automata with Control Language
## 2 Preliminaries
### Language Recognition Capabilities and Descriptional Power
#### 2.4.2 Isolated Cut Point QFAs Descriptional Power  
Let us now quickly expand on the descriptional power of QFAs. Descriptional complexity (see, e.g., [63; 64; 65; 66; 67] for surveys and examples of results) investigates formal systems on the basis of their _size_. In the case of finite state automata, a natural size measure is represented by the number of states. Studying the descriptional power of models of finite state automata basically means to study their ability to recognize languages using a very limited amount of states, this having practical impacts in the physical construction of classical and quantum finite memory devices (see [6] for a discussion).  
A lot of results in the literature show that the quantum descriptional power may greatly outperform the classical one when it comes to finite state devices. To address this quantum superiority, we provide only a few examples of the size economy for recognizing specific families of regular languages.  
The periodic language family \(L_{d}=\{\sigma^{k}\mid k\bmod d=0\}\) for integers \(d>0\) has been widely studied. In a classical setting, we have that \(d\) states are necessary and sufficient to recognize \(L_{d}\) by deterministic and nondeterministic finite state automata. Moreover, for prime \(d\), we have also that two-way nondeterministic and isolated cut point probabilistic finite state automata require not less than \(d\) states (see [6] for an overview of descriptional complexity issues related to the language family \(L_{d}\)).  
With the quantum paradigm, only two basis states are sufficient to recognize \(L_{d}\) by an isolated cut point mo-qfa. However, it should be stressed that the radius of isolation of this simple mo-qfa tends to zero as \(d\) grows. To fix this accuracy issue, some statistical frameworks were designed in [37; 68] in order to show the existence of mo-qfa recognizing \(L_{d}\) with only \(O(\log d)\) basis states and arbitrarily large isolation around the cut point. Such an exponential size gain has then been extended from the unary case to more general languages on arbitrary alphabets, such as multiperiodic languages [37].  
Other relevant examples of isolated cut point mo-qfa that are significantly smaller than the equivalent classical finite state automata will be tackled in more detail in Section 5. There, we will use such small mo-qfa as quantum components for the design of size efficient isolated cut point qfcs for the _whole_ class of unary regular languages.  
In conclusion, from the overview provided in this section, we can motivate the design and analysis of hybrid computational devices as follows: although pure qfas do not reach the computational power of classical finite memory devices, they may result in very small quantum components performing specific tasks to be embedded in a classical control environment. This architecture can be modeled by the notion of a qfa with control language, which we are going to present in the next section.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 1 Introduction
An energy conservation state is defined in which the nodes stop participating in routing and stop collecting data to conserve energy for transmitting existing packets to the data sink later. The Threshold strategy enters the conservation state based on a user-defined energy threshold. The Remaining Required Energy (RRE) strategy determines the optimal threshold based on the amount of energy required to transmit data to a sink. The Optimized Remaining Required Energy (ORRE) strategy determines a threshold based on the expected amount of data to be collected before data can be offloaded to a BS. This work is motivated by a specific application of tracking the location of small, highly mobile animals, such as flying foxes [6], with WSN nodes. In this application, network gateways are located at some roosting camps of the flying foxes. During feeding times, all the flying foxes are far from the roosting camps, and there is no connectivity to the gateways. Additionally, some flying foxes do not roost at gateway-equipped camps, and so their data must be relayed by other animals through a DTN network. The nodes have a constrained energy budget including stored energy, as well as energy that is periodically harvested from small solar cells. The Opportunistic Networking Environment (ONE) simulator [7] is used to evaluate performance of Threshold, RRE and ORRE strategies through different scenarios. We simulate two scenarios of animal tracking including flying foxes (active at night) and pigeons (active during the day) to explore different interplay of daily activity patterns and energy-harvesting periods. Our results indicate that performance of energy-aware versions of DTN routing protocols can improve the packet delivery rate by up to 13%. Results also show that ORRE can outperform RRE with up to 4% higher packet delivery rate. This paper is an extension of our previously published conference paper [8]. Our previous work considered the Threshold and RRE strategies in the context of a flying fox strategy. This new paper extends this work by describing an improved algorithm, ORRE, and also by considering a second \"pigeon\" scenario where energy harvesting and animal activity happen at the same time. Additionally, this paper provides details of how the ONE simulator has been extended to support energy-aware forwarding decisions.
# Auxiliary Information-Enhanced Recommendations
## 3 The Proposed SRS Algorithm
### Differentiated Attention Layer  
The differentiated attention layer is designed to extract the cross-modal interaction relations from the three different modal-specific hidden states at each step. Specifically, at the \(t\)th step, we want to extract the interactions over \(\mathbf{h}_{m}^{t}\), \(\mathbf{h}_{f}^{t}\) and \(\mathbf{h}_{g}^{t}\). Since the different subspace in the hidden state may have different cross-modal interaction strength with the hidden states from other modalities, we need to differentiate the importance of the dimensions of the hidden state when extracting cross-modal interaction relations. For each hidden state, we devised an attention mechanism to emphasize the dimensions which have more interactions with other modalities. The hidden state of multi-GRU at the \(t\)th step can be represented as \(\mathbf{h}^{t}=[\mathbf{h}_{m}^{t},\mathbf{h}_{f}^{t},\mathbf{h}_{g}^{t}]\), where \([,]\) indicates the concatenated operation of vectors.  
The differentiated attention layer takes the hidden states from any two adjacent steps as the input to extract the cross-modal relations. At the \(t\)th step, the input is the concatenation of \(\mathbf{h}^{t-1}\) and \(\mathbf{h}^{t}\), denoted as \(\mathbf{h}^{[t-1,t]}\in\mathbf{R}^{6 sd_{h}}\). Such input is imported into a full-connected (FC) layer with softmax as the activation function to output the attention weights:  
\[\mathbf{a}^{[t-1,t]}=FC_{a}(\mathbf{h}^{[t-1,t]}) \tag{4}\]  
\(\mathbf{a}^{[t-1,t]}\in\mathbf{R}^{6 sd_{h}}\) is the element-wise weight vector with the same dimension as \(\mathbf{h}^{[t-1,t]}\). Then, \(\mathbf{a}^{[t-1,t]}\) performs an element-wise multiplication with \(\mathbf{h}^{[t-1,t]}\) to emphasize those important dimensions in the hidden states.  
\[\mathbf{\hat{h}}^{[t-1,t]}=\mathbf{a}^{[t-1,t]}\odot\mathbf{h}^{[t-1,t]} \tag{5}\]  
The obtained \(\mathbf{\hat{h}}^{[t-1,t]}\) can be seen as the latent representation of the cross-modal relations at the current \(t\)th step.  
In Equation (4), by comparing the information embedded in two adjacent hidden states, the attention mechanism can assign the weights accordingly when the hidden state changes from step \(t-1\) to step \(t\).  
The differentiated attention layer takes the hidden state as the input, which usually contains information from the past steps. Therefore, it can capture the interaction relations of different modalities across multiple time steps. This can help our model discover the complex relations embedded in the sequence data.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Goals, Metrics, and Methodology
#### 5.1.3 Packet Drop Attack Models  
We used two types of packet drop attacks in simulations.  
* Blackhole attack: drop all received data packets (attack rate is 100%).
* Grayhole attack: drop received data packets randomly according to various attack rates that range from 20% to 80%.  
In our simulations, the attacker drops only data packets and forwards other types of packets, such as control packets. As shown in Figure 4, we implemented attack models at the network layer of a wireless node in the OPNET Modeler, since a node can determine whether a received packet is either a data packet or a control packet once the packet reaches the network layer. When an attacker receives a data packet from a node, the attacker sends an ACK message back to the node and then decides whether it drops a data packet based on a pre-defined attack rate. For example, if the attack rate is 100%, the (blackhole) attacker drops all received packets. If the attack rate is 50%, the (grayhole) attacker randomly drops half of the received data packets. If an attacker decides to forward a data packet, it simply transmits the packet to the next hop node (a relay node or the BS).
### Smart Logistic Applications  
Smart logistics applications, like intelligent trucks, containers, or cargo, rely on integrating advanced Information and Communication Technologies (ICT) to consistently and continuously exchange relevant information in real time [7; 8; 9]. The transmitted information can include position, speed, and condition of the vehicle [10; 11], environmental data like temperature or weather conditions, or certain special emergency events that require immediate action by the operator. A container can transmit similar information, which is relevant for multi-modal transportation involving different carriers [12]. Finally, monitoring the cargo condition allows a delivery in the best possible condition [13], which is specifically relevant for the transportation of food [14; 15] or medical goods. Thus, the IoTL enables functionalities like the dynamic and exact prediction of the time of arrival for a perfect unloading/loading operation and handover of goods in interconnected fleets. The collection and transmission of technical conditions and errors would allow predictive and preventive maintenance with minimal impact on operating times. Recharging of Battery Electric Vehicles (BEVs) can be optimized for both cost and time by transmitting their current energy status. By assessing logistic Key Performance Indicators (KPIs) [16], like \"full and operational logistics cost\" or the \"length of logistics cycles\", the IoTL can significantly improve transparency by making these automatically measurable and at the same time deliver the toolset for improvement. In 2021 more than 80% of all goods have been transported on the roads in Germany [17]. Since transportation on roads is strongly dependent on e.g., traffic and weather conditions, the opportunities for ICT-based smart logistics are significant and worth assessing as part of this study.
## 2 Mathematical Models of an S-Shaped Tooth Profile Gear
### Tooth Surface Equations of the Gear  
As shown in Figure 8a, there is a coordinate system \(O_{2}-X_{2}Y_{2}Z_{2}\) fixed on the gear, and the negative semi-axis of the \(O_{2}X_{2}\) axis coincides with the symmetric line of its adjacent tooth profiles. The \(O_{2}Z_{2}\) axis coincides with that of the gear. Then, the tooth profile equations of the gear in the plane of \(O_{2}-X_{2}Y_{2}\) are given by  
\[\begin{array}{l}x_{2}(u_{2})=-u_{2}\sin\,(\alpha+\varphi_{2})+\lambda\sin\, \omega u_{2}\cos\,(\alpha+\varphi_{2})-r_{2}(\sin\,\varphi_{2}-\varphi_{2}\cos \,\varphi_{2})\\ y_{2}(u_{2})=-u_{2}\cos\,(\alpha+\varphi_{2})-\lambda\sin\,\omega u_{2}\sin\,( \alpha+\varphi_{2})-r_{2}(\cos\,\varphi_{2}+\varphi_{2}\sin\,\varphi_{2})\\ \end{array} \tag{19}\]  
\[f(u_{2},\varphi_{2})=u_{2}+\lambda\omega r_{2}\varphi_{2}\cos\,\alpha\cos\, \omega u_{2}-r_{2}\varphi_{2}\sin\,\alpha-\lambda^{2}\omega\sin\,2\omega u_{2} /2=0 \tag{20}\]  
According to the forming theory of a helicoid, the equations of the gear tooth profile are given by  
\[\left\{\begin{array}{l}r_{2}^{(\rm g)}(u_{2},v_{2})=[x_{2}\cos\kappa v_{2}- y_{2}\sin\kappa v_{2}]i+[x_{2}\sin\kappa v_{2}+y_{2}\cos\kappa v_{2}]j-p_{2}v_{2} \boldsymbol{k}\\ f(u_{2},\varphi_{2})=0\end{array}\right. \tag{21}\]  
Figure 8: Tooth surface of the gear and visualization models. (**a**) One tooth surface of the gear; (**b**) accurate three-dimensional models.  
where the helix parameter \(p_{2}=r_{2}\tan\left(\pi/2-\beta\right)\), \(r_{2}=mz_{2}/2\) is the radius of the reference circle, and \(z_{2}\) is the number of gear teeth.  
The meshing surfaces can be constructed from Equations (11) and (21) by using solid modelling software and a parametric method; a visualization three-dimensional solid model of the S-shaped tooth profile gear pair with three pinion teeth is established as shown in Figure 8b.  
The normal vector of the gear tooth is given by  
\[\mathbf{n}_{2}^{\rm(g)}=\frac{\mathbf{r}_{u2}\times\mathbf{r}_{v2}}{|\mathbf{r}_{u2}\times\bm {r}_{v2}|} \tag{22}\]  
\[\mathbf{r}_{u2}^{\rm(g)}=\left[\frac{\partial x_{2}(u_{2},v_{2})}{\partial u_{2}}, \frac{\partial y_{2}(u_{2},v_{2})}{\partial u_{2}},\frac{\partial z_{2}(u_{2},v_{2})}{\partial u_{2}}\right] \tag{23}\]  
\[\mathbf{r}_{v2}^{\rm(g)}=\left[\frac{\partial x_{2}(u_{2},v_{2})}{\partial v_{2}},\frac{\partial y_{2}(u_{2},v_{2})}{\partial v_{2}},\frac{\partial z_{2}(u_{2 },v_{2})}{\partial v_{2}}\right] \tag{24}\]
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Time-Slotted Protocols
#### 3.2.6 Cognitive MAC  
Another distributed multi-channel protocol is Cognitive MAC (C-MAC) [78], which uses multi-transceivers, synchronization and time slots. In this protocol, channels are logically organized into superframes that include a Beacon Period (BP) and a Data Transfer Period (DTP); see Figure 8. To avoid interference, each BP is additionally divided on the basis of time; thus, nodes avoid interference and exchange information by using the negotiation of channel usage.  
For node coordination in different channels, among all available channel, a Rendezvous Channel (RC) is chosen dynamically and in a way that channels can be utilized for a very long time all through the network, without intrusion. The RC channel is also used for PU detection, for the interchange of the programs for the BP, as well as neighbor discovery and network-wide communication. C-MAC also uses the concept of Backup Channel (BC), i.e., if there should arise an occurrence of the presence of a PU, it instantly makes a decision of substitute spectrum bands.  
Figure 8: C-MAC superframe structure. RC, Rendezvous Channel; DTP, Data Transfer Period; BP, Beacon Period.  
The CR user starts with identifying vacant spectrum resources. In the case of hearing a beacon in any of the available bands, CR may adapt to global RC specified in the beacon and join that specific band. Moreover, the CR node informs its neighbor about other devices by re-broadcasting the beacon packet. Before using any band, and for resynchronization, the CR user needs to tune to RC periodically and transmit its beacon information and all of the changes that occurred in the spectrum. To differentiate PUs from the CR user in each spectrum, C-MAC employs non-overlapping Quiet Periods (QP). In addition, C-MAC uses node traffic reservation information by collecting the load measurements from the analysis of beacons to balance the load in the network.
## 1 Introduction
Indeed, as an example, the Australian _Department of Industry, Innovation, Climate Change, Science, Research, and Tertiary Education_ (DICSRTE) has verified that the placement of CORS networks is capable of increasing the economic benefits of an entire nation, depending also on the Gross Domestic Product (GDP); the estimated increase in Australia's is from $2.3 and $3.7 billion in the year 2012 to $7.8 and $13.7 billion more recently in 2020 [11]. On the other hand, scientific studies concerning GNSS CORS are very extensive and cover many fields of research; therefore, a detailed and systematic review of all the CORS topics appears not to be developable within a single paper. This work proposes a selected review of some applications in the field of GNSS CORS, including _network monitoring, reference frame, structure monitoring, and reachable accuracy_, within which the University of Palermo (Italy) has conducted some experiences over the last 10-15 years. The review is structured as follows: in Section 2, the literature review and state of the art are discussed, including the main studies on the GNSS CORS network; in Section 3, the results obtained from UNIPA GNSS CORS are reported. Finally, Section 4 provides some concluding remarks and possible developments of the research.