esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing E-Grocery-Delivery-Network Resilience with Autonomous Delivery Robots
## Abbreviations  
\begin{tabular}{l l} Sets & \\ \(O\) & Set of depots \\ \(S\) & Set of micro hubs \\ \(F\) & Set of customers \\ \(N_{1}\) & Node set consist of \(\{O\cup S\}\) \\ \(N_{2}\) & Node set consist of \(\{F\cup S\}\) \\ \(K_{sun}\) & Set of vans \\ \(K_{\text{ADR}}\) & Set of ADRs \\ Deterministic parameters \\ \(n\) & Initial van number at the depot \\ \(m\) & Initial ADR number at the micro hub \\ \(Q^{sun}\) & Capacity of van \\ \(Q^{ADR}\) & Capacity of ADR \\ \(u^{sun}\) & Average speed for \(wn\) \\ \(u^{ADR}\) & Average speed for \(ADR\) \\ \(w_{s}\) & Capacity of micro hub \(s,s\in S\) \\ \(tl_{f}\) & Service deadline of customer \(f,f\in F\) \\ \(WT\) & Working time \\ \(\beta^{range}\) & Maximum battery electric range of an ADR \\ \(\sigma^{rang}\) & Safety buffer of the battery \\ \(\delta_{f}\) & Loading time per customer, \(f\in F\) \\ \(L\) & A large constant reflecting nonlinear relationships through linear constraints \\ Stochastic parameters \\ \(\rho_{ij}^{sun}\) & Time needed to travel on arc \((i,j),\;\;i,j\in N_{1}\) \\ \(\tau_{ij}^{ADR}\) & Time needed to travel on arc \((i,j),\;\;i,j\in N_{2}\) \\ \(\zeta_{f}\) & Amount of groceries requested by customer \(f,f\in F\) \\ Auxiliary & variables \\ \(T_{sk}^{sun}\) & Visit time at micro hub \(s\) byvehiclevank, \(s\in S\) \\ \(T_{fk}^{ADR}\) & Visit time at node \(f\) byvehiclev, \(f\in F\) \\ \(\delta_{kl}^{sun}\) & Delay incurred at micro hub \(s\) by van \(k,s\in S\) \\ \(\delta_{dk}^{ADR}\) & Delay incurred at customer \(f\) by ADR \(k,f\in F\) \\ Decision variables \\ \(x_{ijk}^{van}\) & \(\left\{\begin{array}{ll}1,\text{if an arc }(i,\;j)\text{ is traversed by van }k,k\in K_{sun},\;i,j\in N_{1},\\ 0,\text{ otherwise}\end{array}\right.\) \\ \(x_{nk}^{van}\) & \(\left\{\begin{array}{ll}1,\text{ if micro hub }s\text{ can be served by van }k,\;k\in K_{ADR},s\in S\\ 0,\text{ otherwise}\end{array}\right.\) \\ \(yl_{ijk}^{ADR}\) & \(\left\{\begin{array}{ll}1,\text{if an arc }(i,\;j)\text{ is traversed by ADR }k,k\in K_{ADR},\;i,j\in N_{2}\\ 0,\text{ otherwise}\end{array}\right.\) \\ \(ym_{fk}^{ADR}\) & \(\left\{\begin{array}{ll}1,\text{if customer }f\text{ can be served by ADR }k,k\in K_{ADR},f\in F\\ 0,\text{ otherwise}\end{array}\right.\) \\ \(y_{sf}\) & \(\left\{\begin{array}{ll}1,\text{if customer }f\text{ is served by micro hub }s,s\in S,f\in F\\ 0,\text{ otherwise}\end{array}\right.\) \\ \(y_{s}\) & \(\left\{\begin{array}{ll}1,\text{if micro hub }s\text{ is open},s\in S\\ 0,\text{ otherwise}\end{array}\right.\) \\ \(\mu_{ijk}^{van}\) & Theamountofgroceriesdelivered onarc\((i,\;j),i,j\in N_{1}\) using van \(k,k\in K_{sun}\) \\ \(\mu_{ijk}^{ADR}\) & Theamountofgroceriesdelivered onarc\((i,\;j),i,j\in N_{2}\) using ADRs \(k,k\in K_{ADR}\) \\ \(u_{f}\) & The amount of unmet demand of customers \(f,f\in F\) \\ \end{tabular}
## 4 Tests
### Test #3  
Test #3 sorts out a hybrid configuration since it accounts for a LoRaWAN Node and an NB-IoT Node as well. Every minute, the LoRaWAN Node sent a packet containing an incremental counter, and such data was forwarded to the NB-IoT Node by the network Cloud Layer. The LoRaWAN Node exploited the same transmission parameters that were employed during Test #1. This setup traces all of those contexts in which an actuator has to perform some actions on the basis of what another sensor sampled: for instance, regulating the flow within a pipeline on the basis of the extent of the transported fluid at the pipeline outlet. In this scenario, the latency between the moment in which the LoRaWAN Node broadcast the packet and the moment in which the NB-IoT Node received the command from the Cloud Layer was measured, along with the packet loss. In order to limit the number of involved variables within the experiment, the exploited LoRaWAN Gateway was a standard one (i.e., Internet connectivity was provided via Ethernet rather than NB-IoT). Moreover, the LoRaWAN Node and the Gateway were positioned 2 m apart so that packet losses could be only ascribed to abnormal functioning of the NB-IoT Node or due to a lack of cellular coverage. This can be considered reliable since data loss within LoRa links is especially unlikely when just a 2 m length has to be covered. Latency was measured in a similar way with respect to the RTTs of Test #2. Indeed, a PC was still exploited. Moreover, both the nodes were connected to as many PC USB ports that were monitored by the PC itself. Similar to Test#2, the nodes logged through their serial ports events related to their functioning. In particular, the LoRaWAN node logged the fact that it sent a packet, along with the incremental counter forming the packet payload, while the NB-IoT node logged the occurrence related to the reception of a packet, still combined with the incremental counter within the payload. Then, the PC recorded the timestamps associated with such events as well as the logs coming from each node. The USB ports monitoring and the recording of the timestamps were accomplished via a Python script, while latency was identified, calculated and analysed by exploiting MATLAB. Latency had to be identified by making use of the incremental counter in the packet payload, and then, it was calculated by performing a subtraction.
## 4 Algorithm Description
### Overview
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & **Definition** \\ \hline \(s_{i},v_{i}\) & ordinary node, proxy node \\ \hline \(ar_{i,\Delta}\) & activity range of \(s_{i}\) within duration \(\Delta\) \\ \hline \(ss,sd\) & stationary stay, stationary duration; \(ss=(sd,C_{sd})\) \\ \hline \(C_{sd}\) & the central location within \(sd\) for a node \\ \hline \(kss,ksd\) & key stationary stay, key stationary duration; \(kss=(ksd,\Omega)\) \\ \hline \(\Omega\) & possible locations and their weights during \(ksd\) for a node \\ \hline \(R\) & communication range \\ \hline \(W\) & observing window \\ \hline \(V\) & set of proxy nodes \\ \hline \(E\) & time line within an epoch \\ \hline \(e_{i}\) & the \(i\)th epoch \\ \hline \(w_{i}\) & weight of node for proxy selection \\ \hline \(\mu_{i}\) & accumulated weight of stationary stay for \(s_{i}\) \\ \hline \(\eta_{i}\) & number of distinct encounters for \(s_{i}\) \\ \hline \(\aleph_{i}\) & set of key stationary stays of node \(s_{i}\) \\ \hline \(\Phi\) & recorded location of node \\ \hline \(\Upsilon\) & data gathering period \\ \hline \(\Psi\) & schedule of data gathering for \(MC\) \\ \hline \(\rho(v_{i})\) & expected amount of data stored at \(v_{i}\) \\ \hline \(T_{slot}\) & the minimal data gathering duration of a slot \\ \hline \(th\) & predefined threshold for proxy node selection \\ \hline \(e(q_{k})\) & index of epoch from which the central point \(q_{k}\) is extracted \\ \hline \(p1(x)\) & expected probability that x is within a stationary duration \\ \hline \(p2(s_{i},s_{j})\) & encounter probability between \(s_{i}\) and \(s_{j}\) \\ \hline \(p3(C_{Q})\) & encounter probability of \(C_{Q}\) \\ \hline \(C_{Q},C_{Q^{*}}\) & centroid of the set of points in \(Q\), centroid point that has the largest encounter probability \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation table. Table 1 lists the Notations and their definitions in the paper. In the following subsections, we present the detailed description of main phases of the algorithm.
# Investigation on the Effect of Femtosecond Laser Induced Spin Transfer Torque of GdFeCo Alloy
## 1 Introduction
In the X-ray magnetic circular dichroism (XMCD) experiment observed by [PERSON] et al., the sublattice spins of Fe and the sublattice spins of Gd showed a transient ferromagnetic-like state and this means that the spins of the two different elements remain magnetized in the same direction. The occurrence of this state needed to overcome the exchange interaction between Fe and Gd, but the thermal perturbation has no direct impact on the magnetization of Fe. Therefore, the mechanism of HI-AOS in ferromagnetic materials with different sublattices remains to be studied further. Variations in the flow of spin-angular momentum occur when spin-polarized electrons pass through a magnetic domain wall or any other spatially non-uniform magnetization distribution. In this process, the spins of the charge carriers rotate to follow the local magnetization, so the spin vector of the angular momentum flow changes as a function of position. The magnetization of the magnetic medium changes the flow of spin angular momentum by exerting a torque on the flowing spins to reorient them, and therefore the flowing electrons must exert an equal and opposite torque on the magnetic medium. This torque applied by non-equilibrium conductive electrons onto a magnetic medium is called spin transfer torque (STT) [21; 22; 23; 24]. Ultrafast diffraction experiments using X-ray were performed by [PERSON] et al. to investigate the nanoscale spin dynamics of GdFeCo amorphous alloy stimulated by femtosecond lasers [25; 26]. In this nanoscale chemical and magnetic inhomogeneous alloy, the experiment results show in the first picosecond, and the angular momentum is transferred from the larger Fe-rich region to the smaller Gd-rich region, resulting in the spin reversal of the Gd. They deemed that this local transfer of angular momentum might be caused by spin current. Spin-polarized current is often measured by magnetic tunnel junction (MTJ) structure in experiments, and thus the researchers are usually focused on the multi-layer structure materials containing the self-selecting valve, but the spin current effect in alloy materials is rarely studied. In fact, the physical mechanism of spin transfer torque varies. Researchers have shown that the optical spin transfer torque [27; 28], spin-dependent Seebeck effect [29; 30; 31] and laser pulse excite superdiffusive spin current [32; 33] can all cause the spin transfer torque effect. In order to better clarify the ultrafast laser single pulse induced spin transfer torque effect in GdFeCo amorphous films, we take both ultrafast thermal effect and spin transfer torque effect into account and calculate the magnetization dynamics for Gd\({}_{25}\)Fe\({}_{75}\) amorphous alloy films. The results show that the spin transfer torque may be the critical factor that causes reversal of spin magnetization of Fe sublattice prior to Gd sublattice during helicity, independent of photomagnetization of GdFeCo amorphous alloy films.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
## 2 Materials and Methods  
We propose a different approach and overcome the demodulators synchronization vulnerability described in Section 1.2 by analyzing the sum of both matched filter outputs for up and down chirps. The resulting superposed signal always features an autocorrelation peak for each symbol, which is then used to retrieve the signal differences between the matched filter outputs to determine the symbol state. We discuss this aspect in detail in Section 2.4. In the following we describe the structure of our communication signal chain in detail, to embed our contribution to the demodulation process properly and fully disclose our method for ease of comparison.
## 2 Manoeuvre Prediction Model for Moving Targets at Sea
### Manoeuvring Prediction Model of Moving Target in Three-Dimensional Space Coordinate System  
Refer to the formula in [3]. The initial state of the moving target is known as initial position \(\gamma(\text{lon}_{\gamma},\text{lat}_{\gamma})\), target velocity \(V\), course angle \(\beta\), velocity range \(\pm\Delta V\), and course angle \(\pm\Delta\beta\). The possible movement area is a fan-shaped area in which movement time is \(\Delta t\), with the initial position as the center of the circle; with \((V-\Delta V)\cdot\Delta t\) and \((V+\Delta V)\cdot\Delta t\) as the upper and lower boundary; and \((\beta-\Delta\beta,\beta+\Delta\beta)\) as the azimuth range. Since the target velocity \(v\) and course angle \(\alpha\) follow the Gaussian distribution:  
\[v\sim N\Big{(}V,\sigma_{V}^{2}\Big{)},\alpha\sim N\Big{(}\beta,\sigma_{V}^{2} \Big{)} \tag{1}\]  
the target speed and course angle probability density can be expressed as:  
\[\left\{\begin{array}{l}f(v)=\frac{1}{\sqrt{2\pi\sigma_{V}}}\text{e}^{-\frac{ (v-1)^{2}}{2\sigma_{V}^{2}}}\\  
Set the target position is (\(x_{n}\), \(y_{n}\)) at \(t_{n}\). After \(\Delta t\), the motion distribution probability density function of moving to the \(R\) region at \(t_{n+1}\) is established as follows:  
\[P((x_{n},y_{n}),R,t_{n+1}-t_{n})=\frac{1}{2\pi\sigma_{r}\sigma_{\beta}}\int \limits_{R}\int\limits_{R}\exp(-\frac{\Delta l^{2}}{2\sigma_{r}^{2}}-\frac{ \Delta\theta^{2}}{2\sigma_{\beta}^{2}})dx_{n+1}dy_{n+1} \tag{6}\]
## 4 Results and Discussion
All algorithms agree that the most significant feature is _f\({}_{\omega}\)_, i.e., the unconfined concrete strength. The two more accurate tree-based algorithms according to Tables 6 and 7, RF and GBR (in Figure 7c,d, respectively), also consider the number of FRP layers, \(L\), and the FRP ultimate axial strength, _f\({}_{\prime}\)_, of increased importance. These findings are very important, as they may be a valuable tool for application engineers, as it can depict the proper material to use in relevant applications. The unconfined concrete strength, being the basis for the calculations before the layered composite material is applied around the cylindrical specimen, is of outmost importance, followed by the number of layers, \(L\), (carbon, glass or aramid) to be wrapped around it. To reach even more accurate predictions on the obtained confined compressive strength, one has to further take in mind the effect of the FRP thickness, \(t\), and the FRP Young's modulus, _E\({}_{f}\)_. The remaining input parameters, such as the specimen diameter, \(D\), and height, \(H\), the ultimate tensile strength of the FRP, _f\({}_{\prime}\)_, and the ultimate FRP strain, _e\({}_{\mu}\)_, are of small importance. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** & **AAD** & **AIC** \\ \hline MLR & 2.35\% & 5.32\% & 2.14\% & 7.64\% & 0.52\% \\ Lasso & 2.22\% & 3.56\% & 2.49\% & 7.48\% & 0.53\% \\ Ridge & 2.35\% & 5.32\% & 2.15\% & 7.64\% & 0.52\% \\ SVR-lin & 6.70\% & 2.04\% & 12.72\% & 1.42\% & 2.55\% \\ SVR-rbf & 4.24\% & 3.98\% & 0.95\% & 5.41\% & 0.90\% \\ SVR-poly & 3.81\% & 28.97\% & 14.90\% & 33.42\% & 3.61\% \\ GP & 1.32\% & 0.55\% & 2.52\% & 5.79\% & 0.58\% \\ k-NN & 4.13\% & 1.46\% & 19.89\% & 0.89\% & 4.29\% \\ DT & 5.68\% & 16.37\% & 30.57\% & 16.33\% & 6.46\% \\ RF & 5.64\% & 29.26\% & 47.95\% & 35.03\% & 10.11\% \\ GBR & 5.46\% & 10.04\% & 31.37\% & 10.33\% & 6.74\% \\ MLP & 3.70\% & 15.43\% & 17.74\% & 21.25\% & 3.91\% \\ \hline \end{tabular}
\end{table} 
Table 8: Measures of accuracy comparison. Values for each measure correspond to the percentage of loss when the all-input case is substituted by the six PCA inputs case.
# Semantic Component Association within Object Classes Based on Convex Polyhedrons
## 7 Experimental Evaluation
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method/Class** & **Mug** & **Knife** & **Bag** & **Cap** & **Laptop** \\ \hline SyncSpecCNN [22] & 92.73 & 86.10 & 81.72 & 81.94 & 95.61 \\ Pd-Network [23] & 94.00 & 87.25 & 82.42 & 87.04 & 95.44 \\ SSCN [24] & 95.23 & 89.10 & 82.99 & 83.97 & 95.78 \\ SpiderCNN [25] & 93.50 & 87.30 & 81.00 & 87.20 & 95.80 \\ SO-Net [26] & 94.20 & 83.90 & 77.80 & 88.00 & 94.80 \\ PCNN [27] & 94.80 & 86.00 & 80.10 & 85.50 & 95.70 \\ KCNet [28] & 94.40 & 87.20 & 81.50 & 86.40 & 95.50 \\ Kd-Net [23] & 86.70 & 87.20 & 74.60 & 74.30 & 94.90 \\
3 DmFV-Net [29] & 94.00 & 85.70 & 84.30 & 86.00 & 95.20 \\ RSNet [30] & 92.60 & 87.00 & 86.40 & 84.10 & 95.40 \\ DGCNN [31] & 93.30 & 87.30 & 83.70 & 84.40 & 96.00 \\ PointNet [32] & 93.00 & 85.90 & 78.70 & 82.50 & 95.30 \\ PointNet++ [33] & 94.10 & 85.90 & 79.00 & 87.70 & 95.30 \\ SGPN [34] & 93.80 & 83.00 & 78.60 & 78.80 & 95.80 \\ PointCNN [6] & 95.28 & 88.44 & 86.47 & 86.04 & 96.11 \\ \hline \multirow{2}{*}{DSA} & \(w_{n}=0\) & 82.14 & 75.53 & 39.67 & 69.59 & 75.54 \\  & \(w_{n}=1\) & 76.65 & 73.42 & 41.66 & 74.76 & 74.53 \\ \hline \multirow{2}{*}{OCA} & \(w_{n}=0\) & 75.74 & 56.35 & 36.24 & 81.45 & 59.67 \\  & \(w_{n}=1\) & 76.47 & 59.18 & 40.34 & 80.28 & 59.95 \\ \hline \multirow{2}{*}{MST} & \(w_{n}=0\) & 77.68 & 75.59 & 34.92 & 75.57 & 76.74 \\  & \(w_{n}=1\) & 75.80 & 64.78 & 44.01 & 72.32 & 65.91 \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy (%) comparison with the state-of-the-art methods. the SCLM are reported in Table 4. The average execution time of every step per object is presented. Furthermore, the total execution time of all steps required for expanding the model database with a new query object is provided. It was assumed that before the expansion, the database contained the total number of objects given in Table 4 minus one. The average running time per model is reported for Steps 1-8, implemented in C++, and for Steps 11 and 12, implemented in MATLAB. In the case of the MST method, the execution time of Step 11 included computation of MST implemented in C++. The representative selection time, Step 9, was required only when the representative models were selected. In the case where the database was extended without annotating a new representative object, this step was not performed. Thus, the execution times of this step are presented separately from the other steps. Furthermore, Step 10, which was performed by a human expert, is not considered in Table 4. The experiments were executed on a PC with an Intel Core i7-4790 3.60 GHz processor and 16 GB of installed RAM memory running Windows 10 64-bit OS. In order to investigate how the component association accuracy depended on the number of representative objects, we performed a series of experiments, where we varied the number of representatives from one to the value shown in Table 2. The results of this analysis are shown in Figure 6. The experiments were performed for all three component association methods with \(w_{N}=1\).
# Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks
## 3 Methods
### Energy Harvesting System
#### 3.1.1 Overall Scheme  
Energy harvesting technology can convert the ubiquitous energy such as thermal energy, solar energy, wind energy, and electromagnetic energy into electrical energy through the appropriate power electronic equipment and the voltage conversion circuit. At present, there are many more developed energy harvesting technologies, and the main source of their energy is generally mechanical vibration energy, electromagnetic radiation energy, solar energy, heat energy, and other energy. In order to realize the self-power supply demand of each sensor node in a WSN, we use solar energy and mechanical energy as the  
Figure 1: Framework diagram of technical route in a WSN.  
energy source of energy harvesting technology to realize the conversion to electric energy. The framework diagram of node energy supply in a WSN is shown in Figure 2. In this system, the organic combination of piezoelectric power generation technology and solar power generation technology is used to create a complementary effect and fully guarantee the energy supply of the energy harvesting module to the sensor nodes.
## 3 The 6G Technology  
### Features of 6G Networks  
The 6G network will bring about ubiquitous network connectivity and services by integrating satellite networks, aerial networks, and terrestrial networks into a potent heterogenous wireless communication network [69]. The integrated SAGIN network, when deployed, will actualize the creation of new application scenarios in the future. Some of these practical applications include smart city applications, autonomous cars, globally enhanced network coverage, etc. The 6G wireless network is undeniably going to be a powerful innovation when launched in 2030. It promises to revolutionize wireless communications and bring about new innovations and business opportunities [70; 71]. It is designed to be user-centric and will provide ubiquitous connectivity globally as well as support highly accurate localization and high-resolution sensing services [18; 52]. The upcoming 6G network, when deployed, will provide a reliable go-to technology for the real-time monitoring of industrial processes and automation [72]. It has shown improved communication features as well as localization and sensing [52], and enables high-frequency communication applications such as wireless cognition, imaging, positioning, and sensing [70].
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
###### Abstract  
In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VolLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%.  
5G NR; EMF exposure; duty cycle; uplink; smartphone +
Footnote †: journal: _Sensors_**2024**, 24, 3012. [[https://doi.org/10.3390/s24103012](https://doi.org/10.3390/s24103012)]([https://doi.org/10.3390/s24103012](https://doi.org/10.3390/s24103012))