esa-sceva/satcom-chunk-collection huggingface dataset:
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 4 Results and Analysis
### Optimal Training SNR  
Optimal training SNR was a prerequisite for our subsequent simulations; hence, this part discusses our results for searching for optimal training. Consider the frame-by-frame transmission case, i.e., \(N=6\) and \(K=4\), and we trained the seq2 seq decoder with datasets of different training SNRs to observe the generated MEMAPs (proposed in Section 3.3). In our cases, we defined \(T\) in (6) as a set of 21 equally spaced SNRs points from \(-4\) dB to 6 dB. It can be seen in Figure 4a that a training \(E_{b}/N_{0}\) of 1 dB leads to the lowest MEMAP, with MEMAP \(=0.0016\). Hence, we set our training \(E_{b}/N_{0}\) to 1 dB for our subsequent simulations. To illustrate the learning process at training \(E_{b}/N_{0}=1\) dB, we also plotted in Figure 4b the BER variations of the seq2 seq decoder for each epoch during the training. We observe that the training BER of the decoder decreased as the epochs increased, and after several epochs, the BER converged and achieved the MAP-based decoding performance. This indicates that the proposed decoder can learn the MAP-decoding capability very well.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Seq2 seq Decoder**} \\ \cline{2-3}  & **RNN-Encoder** & **RNN-Decoder** \\ \hline Input Neurons & \(N\) & \(K\) \\ \hline Hidden Layer Units & \([5N,5N]\) & \([5N,5N]\) \\ \hline Output Neurons & 1 & \(K\) \\ \hline Training sets & \(4\times 10^{4}\)\(N\) \\ \hline Batch size & 100 \\ \hline Training epochs & 40 \\ \hline Optimizer & Adam (with learning rate = 0.01) \\ \hline Initializer & Xavier uniform \\ \hline Loss Function & MSE \\ \hline \hline \end{tabular}
\end{table}
Table 2: The settings of seq2 seq decoder.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Cellular Mobile Communications
#### 7.2.2 Long-Term Evolution  
Long-term evolution (LTE) is a major step in cellular mobile communications, giving a high speed and low latency base, with a major positive impact on the evolution of the IoV [100]. As an LTE standard, it provides robust data transmission and seamless connectivity for IoV applications. More specifically, its high-speed capabilities are critical in supporting data services in connected vehicles. The IoV benefits from LTE as a backbone of various applications, in particular for connected car services, such as navigation systems that make full use of (real-time, accurate) data for best routing. The low-latency communication of LTE enables real-time traffic updates that help to create more efficient route planning and avoid congestion. Fundamentally, LTE serves as a cornerstone to IoV by setting up a connectivity infrastructure to run vehicles within an environment that brings together advanced communication technologies to enhance the on-road driving experience, safety, and efficiency.
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 4 P-DQN-Based Approach
### MDP with Parameterized Action Space  
The parameterized action MDP (PAMDP) model is an extension of standard MDP [49]. Note that the MDP is represented by a quadruple \((\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R})\), which are the state space, the action space, the state transition probability set, and the reward function set, separately. In contrast, the PAMDP redefines the discrete-continuous hybrid action space in the MDP, as follows:  
\[\mathcal{A}=\{(v,\boldsymbol{n}_{v})\mid\boldsymbol{n}_{v}\in\mathcal{N}_{v} \text{ for all }v\in\mathcal{V}\}, \tag{12}\]  
where \(\mathcal{V}=\{1,\ldots,V\}\) is the set of discrete actions, and \(\mathcal{N}_{v}\) is the set of continuous parameters of each \(v\in\mathcal{V}\). A high-level action \(v\) is first preserved from \(\mathcal{V}\), and then the low-level parameter \(\boldsymbol{n}_{v}\in\mathcal{N}_{v}\) associated with \(v\) is selected. In particular, the PAMDP model paper can be established as follows:  
* **State space:** For each \(\boldsymbol{s}^{t}\in\mathcal{S}\), define \(\boldsymbol{s}^{t}=\Big{\{}\mathcal{Y}^{t},\mathcal{Z}^{t},\Big{\{}\omega_{k}^ {t,L}\Big{\}}_{k\in\mathcal{K}},\omega^{t,M}\Big{\}}\), where \(\mathcal{Y}^{t}\) and \(\mathcal{Z}^{t}\), respectively, represent the sets of new arrival tasks and being already processed ones.
* **Parameterized action space:** Define the parameterized action as \(\mathcal{A}=\Big{\{}\boldsymbol{a}_{k,i}^{t,t}\Big{\}}\), where \(\boldsymbol{a}_{k,i}^{t,t}=\{x_{k,i}^{t,1},x_{k,i}^{t,1}(c_{k,i}^{t,L},P_{k,i} ^{t,L}),x_{k,i}^{t,3}(C_{k,i}^{t,M},P_{k,i}^{t,M})\},\boldsymbol{t}\leq t\). In particular, \(x_{k,i}^{t,1}\), \(x_{k,i}^{t,2}\), and \(x_{k,i}^{t,3}\) are three types of offloading decisions. For \(x_{k,i}^{t,1}\), and the task is processed locally without parameters; for \(x_{k,i}^{t,2}\), the task is offloaded to the LEO satellite, the parameters are \(C_{k,i}^{t,L}\) and \(P_{k,i}^{t,L}\); and for \(x_{k,i}^{t,3}\), the task is offloaded to the cloud server, and the parameters become \(C_{k,i}^{t,M}\) and \(P_{k,i}^{t,M}\).
* **Transition probability:** A model-free RL architecture is used since both state and action spaces are high-dimensional and we cannot give the precise state transfer.
* **Reward function:** To judge all tasks in \(\mathcal{Z}^{t}\) per slot, the temporal reward function per task can be defined as \(r_{k,i}^{t,t}\). In particular, when the task is completed in the current slot, \(r_{k,i}^{t,t}\) takes the large positive value; when the task is judged to be transmitted continuously, \(r_{k,i}^{t,t}\) is temporarily set to be zero; and when the task fails, \(r_{k,i}^{t,t}\) is finally set to be negative.
## 4 In-Depth Analysis and Results
### Qualitative Content Analysis  
The in-depth analysis of the final literature sample was carried out by means of the research technique referred to as qualitative content analysis. In the scientific context, content analysis, in general, is an approach for identifying the most important characteristics of written research studies allowing for valid inferences [87]. It is a very useful technique to gather and organize information for obtaining insights into current trends and patterns in the considered literature sample [88]. The qualitative aspect, in particular, refers to sorting the content into groups and summarizing the key messages [87]. The results of the qualitative content analysis for the groups of (i) production planning and control supported by BIM and (ii) theory of BIM-LPS integration are presented in the next sub-sections.
## 6 Model Validation and Performance Evaluation
### Delay and Energy Performance over the Number of End-Devices  
In this section, we evaluate and compare the performance of RFSA and FSA, in terms of delay and energy consumption, with different numbers of end-devices in dense M2M networks.  
The average delay and the average energy consumed by the coordinator in a data collection round using RFSA and FSA are represented in the left vertical axis of Figure 6, respectively, over the number \(n\) of end-devices (from 25 to 1000 end-devices). We have considered two different cases in both access protocols: (i) when the frame length is lower than the total number of end-devices and close to the optimal point that minimizes the delay and the energy consumption of the coordinator in FSA (_i.e._, \(m=n/2\)); and (ii) when the frame length is close to the optimal in RFSA (_i.e._, \(m=n/5\)).  
As can be observed, the average delay and the average energy consumed by the coordinator using RFSA and FSA increase linearly with the number of end-devices, and as could be expected, the delay and the energy consumption are much higher using FSA than RFSA. The delay reduction and energy saving provided by RFSA with respect to FSA are shown on the right vertical axis of Figure 6, respectively. When \(m=n/2\), RFSA provides savings of 25-30% with respect to FSA, and these savings increase up to a 70% when \(m=n/5\). Indeed, the delay reduction and energy saving provided by RFSA are higher when its optimum frame length is used.  
The average energy consumed per end-device in a data collection round using RFSA and FSA is represented in the left vertical axis of Figure 7 over the number \(n\) of end-devices (from 25 to 1000 end-devices). We have considered two different cases in both access protocols: (i) when the frame length is equal to the number of end-devices (_i.e._, \(m=n\)); and (ii) when the frame length is lower  
Figure 6: (**a**) Average delay (left vertical axis) to terminate a data collection round using \(m=n/2\) and \(m=n/5\) slots per frame; and (**b**) average energy consumption (left vertical axis) of the coordinator in a data collection round using \(m=n/2\) and \(m=n/5\) slots per frame. The delay and energy savings provided by RFSA with respect to FSA are shown in the right vertical axes.  
than the total number of end-devices and close to the minimum value that guarantees that the energy consumed per end-device in RFSA is minimum (_i.e._, \(m=n/2\)).  
As can be observed, the average energy consumed per end-device using RFSA and FSA increases linearly with the number of end-devices, and as could be expected, the energy consumption is much higher using FSA than RFSA. The energy savings provided by RFSA is shown on the right vertical axis of Figure 7. When \(m=n\), RFSA provides an energy savings of a 40% with respect to FSA, and it increases up to a 55% when \(m=n/2\).
## 3 Results
For the 40 mm diameter sphere the DVH shows a slow fall-off referred to the spill-out effect, while for the 20 mm and the 40 mm hollow sphere the DVH drops more rapidly due to intrinsic limitation of the SPECT system. Of note, our results compare with varying degrees of accuracy with those reported by [PERSON] et al. [22]. The DVH related to the 40 hollow sphere is in excellent agreement with (rescaled) MC-based DVH (Figure 6a), assumed to be the reference. As expected, the 20 mm sphere results in lower dose values when compared with MC data (Figure 6b) obtained for the same sphere. Along the same lines, the DVH of the 40 mm hollow sphere differs from DVH assessed by MC calculation (Figure 6c). As already discussed, most likely such differences are due to the intrinsic limitation in resolution of the SPECT system and the presence of partial volume effects (PVE) which result in a systematic underestimation of dose values. Finally, for the liver compartment the same results found between liver and MC DVHs in [22] have been confirmed in this study (Figure 6d). \begin{table}
\begin{tabular}{c c c c} \hline
**AbdoMan** & **D\({}_{mean}\)-LDM** & **D\({}_{mean}\)-KDM** & **\(\Delta\) (\%)** \\ \hline Liver & 4 Gy & 4 Gy & 0\% \\
40 mm sphere & 80 Gy & 87 Gy & 8.7\% \\
20 mm sphere & 76 Gy & 79 Gy & 4\% \\
40 mm hollow sphere & 70 Gy & 72 Gy & 2.4\% \\ \hline \end{tabular}
\end{table} 
Table 2: Lesions and liver mean activity within the AbdoMan phantom obtained by means of the LDM and DKM. Figure 5: Cumulative dose-volume histograms for the three lesions and the entire liver volume of AbdoMan phantom using local energy deposition method (LDM, (**left**)) and Dose kernel method (DKM, (**right**)).
# An Enhanced Neural Word Embedding Model for Transfer Learning
## 4 Methodology
### Model Architecture  
In order to train a FastText model, two popular methods, Skip-gram and cbow, were used. An explanation of the continuous Skip-gram model for generating FastText word embedding methods is given below: Let \(W\) be the length of the token vocabulary, and the index is the only identifier of every token \(w\), where \(w\in 1,\ldots,W\). The aim of training a model is to learn the vector representation of every token w. According to the distributional hypothesis, word embedding is trained in order to forecast a word that remains in its context by its index. Mathematically, for a large training data, which is a sequence of words \(w_{1},\ldots,w_{T}\), then, the objective function of the Skip-gram method with maximization is represented by the following equation:  
\[\sum_{t=1}^{T}\sum_{c\in C_{t}}logp(w_{c}|w_{t}) \tag{1}\]  
Here, the notation \(C_{t}\) represents the indices of tokens surrounding the word \(w_{t}\). The probability of observing a context word \(w_{c}\) given wt will be parameterized using the aforementioned word vectors. Let \(s\) be a scoring function that maps the word and context to scores in \(\mathbb{R}\).  
The probability function using a softmax layer for a context word is as follows:  
\[p(w_{c}|w_{t})=\frac{e^{s(w_{t},w_{c})}}{\sum_{j=1}^{W}e^{s(w_{t},j)}} \tag{2}\]  
Here, all the context words are considered as positive examples, and negatives at random from the dictionary for the position of \(t\). Considering a context index of \(c\), according to the binary logistic loss, the negative log-likelihood can be obtained as follows:  
\[log(1+e^{-s(w_{t},w_{c})})+\sum_{n\in N_{t,c}}log(1+e^{s(w_{t},n)}) \tag{3}\]  
Here, \(N_{t,c}\) represents a list of negative examples, which is sampled from the lexicons. Mathematically, the logistic loss function \(\ell:x\mapsto log(1+e^{-x})\) can be re-written as the objective:  
\[\sum_{t=1}^{T}\left[\sum_{c\in C_{t}}\ell(s(w_{t},w_{c}))+\sum_{n\in N_{t,c}} \ell(-s(w_{t},n))\right] \tag{4}\]  
As a natural parameterization, the scoring distribution \(s\) between a context word \(w_{c}\) and a word \(w_{t}\) is to utilize the word vectors. Let \(w\) be the word in the dictionary with two vectors \(u_{w}\) and \(v_{w}\) in \(\mathbb{R}^{d}\). The two vectors are considered as input and output vectors in many works. Consider that the vectors are \(\mathbf{u}_{wt}\) and \(\mathbf{v}_{wc}\), whose corresponding words are \(w_{t}\) and \(w_{c}\), respectively. Then, the score can be calculated as the scalar product between the word and context vectors represented by \(s(w_{t},w_{c})=\mathbf{u}_{w_{t}}^{\top}\mathbf{v}_{w_{c}}\). The model illustrated in this section is the Skip-gram model with negative sampling, introduced by [PERSON] et al. [6]
## 3 Results and Discussion
### Evolution of VOOs' Overall Quality during 12 Months of Light Exposure
On the contrary, in VOOsmhp (Figure 2B), only S1 exceeded the limit between the fourth and sixth month of shelf life, and only at the end of the experimentation did all the other VOOsmhp (except S4) no longer belong to the extra-virgin category [34]. These results confirmed that a lack of polyphenols promotes a quick increase in those volatile substances produced at the end of the oxidation phase, which are absorbed at 270 nm [34]. Figure 1: Score plot (t1/u1) (**A**) and loading plot (**B**) (pq1/posol) of the first latent variable of the OPLS model built with all the analytical evaluations (independent variable X) for the 20 VOOs exposed to light for 12 months (latent variable Y representing the TIME). The model, explained 61% of the total variance of X (R2X) with five components (1 predictive and 4 orthogonal, each explaining 24%, 14%, 12%, 7%, and 4%, respectively) and 98% of the overall explained variance (R2). Moreover, the usefulness of K\({}_{270}\) as a simple and cheap parameter for evaluating the oxidative status of a VOO [6; 7; 8; 9; 40], was confirmed. In regard to \(\Delta\)K evolution (Figure 3A,B; Table S6) a similar tendency to increase over storage was revealed: the legal limit (0.01) for the extra-virgin category [34] was first reached by VOOlmp samples starting from the period ranging between the second and the third month (samples S8 and S11) and between the fifth and the sixth month of conservation for almost all the 10 VOOslmp, S7 and S18 samples excluded. Figure 2: Evolution (reported every two months) of K\({}_{270}\) over 12-month storage with light exposure in VOOlmp (**A**) and VOOmhp (**B**) samples. The dashed red line indicates the K\({}_{270}\) legal limit (0.20) for the extra-virgin olive oil category [34]. Legend: VOOlmp: Virgin olive oils with low-medium polyphenol content; VOOmhp: Virgin olive oils with medium-high polyphenol content. On the contrary, higher stability was observed in VOOmhp samples since four of ten samples (respectively S5, S4, S9, and S6) never exceeded the legal limit, whereas the other six VOOs (respectively, S1, S3, S6, S10, S14, and S15) showed value higher than 0.01 by the eighth month of storage. However, between \(\Delta\)K and K\({}_{270}\), K\({}_{270}\) showed higher sensibility in revealing the oxidative status of the samples: S5, S6, and S9, which remained within the extra-virgin marketable definition according to the \(\Delta\)K limit, were not according to the K\({}_{270}\) parameter by the ninth month of storage.
## 5 Higher Layer Protocol Evaluation
### Routing Protocols  
In this subsection we perform a simple simulation experiment with a particular MAC protocol in combination with different routing policies, in order to assess their behaviour under different scenario environmental conditions. We propose the DACAP MAC protocol since it defines some crosslayer support for routing protocols, and we want to quantify the benefits of crosslayer approaches when the scenario environmental conditions change. So we will test the behaviour of DACAP MAC protocol with two routing protocols, a static routing protocol (always supplies the neighbour with the nearest node to gateway) and the FBR routing protocol. Also we will include in our experiments two different propagation models, Thorp and Bellhop. For the simulation parameter we will use the ones in Table 1 except for the propagation models, we only use Thorp and Bellhop, the global network load is fixed to 3 packets/s, and we define an MH scenario configuration.  
Figure 15 depicts and interesting behavior of DACAP protocol in terms of goodput performance. In addition there is a clear indication that also, at the routing layers, the environmental conditions of the network scenario may considerably impact in the results of network performance.  
As also shown in previous results, the Thorp propagation model does not take into account environmental conditions, so it plots a constant goodput value. As, expected the static routing protocol gets better goodput results because FBR has an extra waiting time in order to accept more than one CTS, but as every node has always the same reachable nodes in its neighborhood it always choses the  
Figure 15: Goodput results with DACAP + Routing using two different propagation models.  
same node to reach to the gateway, that is the reason why using static routing in ideal conditions is a better option. If we use Bellhop the static alternative loses performance as the attenuation grows due to the physical changes, meanwhile FBR performance is not so affected in worse conditions as it can dynamically change the routing paths when a connection link is lost.
## 3 Methodology
### Construction of the Spatio-Temporal Network  
The spatio-temporal network is a three-dimensional network composed of time, space, and state dimensions. Different trains correspond to different routes and arrival and departure times, resulting in various sub-spatio-temporal networks. High-speed rail express, which relies on different trains for transportation, can be viewed as different states of express cargo utilizing these trains. Therefore, this study constructs train sub-spatio-temporal networks for different trains, with each corresponding to different state layers, including the initial layer, storage layer, dedicated train layer, and train state layer. The departure, arrival, transportation on different trains, and transfer between trains can all be represented by this spatio-temporal network.  
This model mainly consists of two parts: train modeling and express cargo modeling. Hence, problem representation is achieved by constructing train sub-spatio-temporal networks and express cargo time-space state networks in the following steps.
## 2 Experimental Setup  
### Autonomous Heavy Lift Drone  
The LIBIS is an electrically powered UAV designed for versatile aerial operations (Figure 1). This UAV has a maximum takeoff weight of 25 kg and a wingspan of 2 m, achieved through its unique tandem wing configuration. Each semi-wing is equipped with a brushless motor, enabling the UAV to perform vertical takeoffs and landings by operating as a rotary-wing aircraft.  
The UAV is powered by four batteries positioned at its center of mass. Although the tip motors enable fixed-point flight, this mode is highly inefficient and leads to rapid battery depletion. To extend its flight range, the LIBIS switches to a fixed-wing configuration, where a tail motor provides forward thrust, allowing for longer flight durations on a single charge.  
The UAV is controlled by a Pixhawk flight controller, which incorporates GPS, compass, and accelerometer-gyroscope modules. This flight controller is capable of managing the  
Figure 1: LIBIS—electrically powered UAV designed for versatile aerial operations with a maximum takeoff weight of 25 kg and a wingspan of 2 m.  
five motors, actuators, and batteries without encountering pin limitations. Additionally, it enables autonomous flight operations, eliminating the need for manual piloting.  
The LIBIS structure is composed of three primary materials. Load-bearing components, including the wings and central fuselage box, are constructed from carbon fiber and aluminum. The central box, made from extruded aluminum profiles, allows for the easy integration of experimental equipment. Non-load-bearing components are 3D printed from PLA and PETG filament, providing flexibility to modify their geometry and accommodate various payloads. In this experiment, the upper fuselage section was adapted to house the device under test (DUT).
###### Abstract  
Device-to-device communications in underlay mode has emerged as a promising way to enhance spectrum efficiency in cellular networks. Recently, relay selection in D2D communications underlaying cellular networks is gaining more research interest. In this paper, we propose two relay selection schemes for D2D communications underlaying cellular networks, Midpoint Relay Selection using Social Trust and Battery Level (MRS-ST-BL) and Midpoint Relay Selection using Social Distance and Battery Level (MRS-SD-BL). These proposed schemes utilize battery power level information of devices together with social trust information of users in the network for relay selection. For performance evaluation, initially we show that the throughput of state-of-the-art schemes Hybrid Relay Selection (HRS) and our previously proposed schemes Midpoint Relay Selection using Social Trust (MRS-ST) and Midpoint Relay Selection Using Social Distance (MRS-SD) decrease, when relays have varying battery power. Then, we compare the performance of our proposed schemes against existing schemes including HRS, MRS-ST and MRS-SD. The performance comparison is done at various social trust scenarios and device densities. We show that our proposed schemes can significantly improve the throughput of D2D communications, particularly when relays have different battery power levels in weak social trust scenarios. Finally, we show that the performance of our proposed scheme MRS-ST-BL varies with the change in battery power threshold.  
c 2020202  
of D2D communications in underlay mode, utilizing social information of users and battery power information of devices.  
In this paper, we present the design of two midpoint relay selection schemes for D2D communication which utilizes social trust information of users and battery power of devices for relay selection. The design of two proposed schemes varies in the way they filter devices to select as a relay. We show that, when D2D communications using relays have different battery power levels, our proposed relay selection schemes significantly increase throughput.