esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 4 Autonomous Data Offloading and Price Setting
### Optimal Pricing of the MEC Servers Computing Services  
In this subsection, the optimal pricing of the MEC server's computing services is determined towards maximizing the MEC servers' profit given the offloaded data of the end-users. Combining Equations (7), (10b) and (12), the corresponding optimal pricing problem of the MEC servers can be written as follows.  
\[\mathbf{p}^{(t)*}=argmax_{\mathbf{p}^{(0)}}P_{s}^{(t)}(\mathbf{b}^{(t)}, \mathbf{p}^{(t)})=(1-f_{s}^{(t)})p_{s}^{(t)}\sum_{u\in U}[\frac{B_{u,u}^{(t)}} {B_{u}}[\frac{a_{u}B_{u}}{d_{u}^{(t)}p_{s}^{(t)}}-1]]-c_{s}^{(t)}\sum_{u\in U}[ \frac{B_{u,u}^{(t)}}{B_{u}}[\frac{a_{u}B_{u}}{d_{u}^{(t)}p_{s}^{(t)}}-1]] \tag{13}\]  
Based on Equation (13), it is observed that the optimal pricing problem of the MEC servers' computing services is a function only of their prices \(p_{s}^{(t)}\).  
Theorem 4.1: _The optimal pricing announced by each MEC server for its computing services given the end-users offloaded data and towards maximizing its own profit is given as follows:_  
\[p_{s}^{(t)*}=[\frac{\alpha_{u}\beta_{u}c_{s}^{(t)}\sum_{u\in U}\frac{B_{u}^{(t )}}{d_{u}^{(t)}}}{(1-f_{s}^{(t)})\sum_{u\in U}B_{-u}^{(t)}}]^{1/2} \tag{14}\]  
Proof of Theorem 4.1.: Towards determining the optimal pricing announced by each MEC server, we take the first-order derivative with respect to \(p_{s}^{(t)}\) and determine the critical points.  
\[\frac{\partial p_{s}^{(t)}(\mathbf{b}^{(0)},\mathbf{p}^{(t)})}{\partial p_{s} ^{(t)}}=-\frac{1}{\beta_{u}}(1-f_{s}^{(t)})\sum_{u\in U}B_{-u}^{(t)}+\frac{c_ {s}^{(t)}a_{u}}{p_{s}^{(t)}}\sum_{u\in U}\frac{B_{-u}^{(t)}}{d_{u}^{(t)}}=0\]  
Thus, the critical points are given by the following equation:  
\[p_{s}^{(t)*}=[\frac{a_{u}\beta_{u}c_{s}^{(t)}\sum_{u\in U}\frac{B_{u}^{(t)}}{ d_{u}^{(t)}}}{(1-f_{s}^{(t)})\sum_{u\in U}\frac{B_{-u}^{(t)}}{d_{u}^{(t)}}}]^{1/2}\]  
By checking the second-order derivative of \(p_{s}^{(t)}(\mathbf{b}^{(t)},\mathbf{p}^{(t)})\) with respect to \(p_{s}^{(t)}\), we have:  
\[\frac{\partial^{2}P_{s}^{(t)}(\mathbf{b}^{(t)},\mathbf{p}^{(t)})}{\partial p_ {s}^{(t)^{2}}}=-2c_{s}^{(t)}\frac{a_{u}}{p_{s}^{(t)}}\sum_{u\in U}\frac{B_{-u}^ {(t)}}{d_{u}^{(t)}}<0\]  
Thus, \(p_{s}^{(t)*}\) maximizes the MEC server's profit \(P_{s}^{(t)}(\mathbf{b}^{(t)},\mathbf{p}^{(t)})\).
## 2 CoMP JDT Network  
We consider a CoMP JDT network where two or more CTPs in a JDT cooperating set, each CTP equipped with a single antenna coordinately transmit signals to a targeted UE equipped with a single antenna, as shown in Figure 1a,b. We assume that the CTPs in the cooperating set share data and mean channel quality indicators for the target UE via reliable backhaul connections. In addition, the CTPs in the cooperating set share a TCPP \(P_{S}\) such as \(P_{S}=P_{1}+P_{2}\) for Co2P JDT and \(P_{S}=P_{1}+P_{2}+P_{3}\) for Co3P JDT, where \(P_{1}\), \(P_{2}\), and \(P_{3}\) denote the respective CTP powers allocated to CTP1, CTP2, and CTP3.  
The respective instantaneous diversity-combined signal-to-noise ratios at the UE receiver, for Co2P JDT and Co3P JDT are given by [12]  
\[\gamma_{co2}=P_{1}\gamma_{1}+P_{2}\gamma_{2}, \tag{1}\]  
\[\gamma_{co3}=P_{1}\gamma_{1}+P_{2}\gamma_{2}+P_{3}\gamma_{3}, \tag{2}\]  
where \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\) denote the CTP1-UE, CTP2-UE, and CTP3-UE branch GNRs, respectively. In Rayleigh fading channels, \(\gamma_{1}\), \(\gamma_{2}\), and \(\gamma_{3}\) are independent exponential random variables with the respective means of \(\bar{\gamma}_{1}\), \(\bar{\gamma}_{2}\), and \(\bar{\gamma}_{3}\).
## 4 Common Driving Methods for Rotating Motion Mechanisms
### Pneumatic Drive
#### 4.3.2 Pneumatic Artificial Muscles
Through the above comparative analysis, the core performance requirements and adaptive driving methods of different rotary motion mechanisms need to be analysed case-by-case. For example, cam mechanisms need to balance dynamic response and accuracy, and motor or electro-hydraulic servo drives are preferred, which are typically applied in automation equipment and aviation hydraulic systems; Gear mechanisms focus on torque bearing and transmission efficiency, while hydraulic drives or high power motors are suitable for construction machinery and heavy-duty gearboxes; The linkage mechanism needs to coordinate motion and adapt to the environment, and the pneumatic drive or servo motor is ideal for industrial robots and automotive suspension systems; Ratchet and pawl emphasize the reliability of one-way locking, and pneumatic drive or spring mechanical are often used for lifting equipment and safety braking; Ball screws rely on high positioning accuracy and repeatability, and piezoelectric drive or closed-loop servo motors are suitable for CNC machine tools and semiconductor lithography machines; Universal joints require multi-directional torque transmission capability, and electro-hydraulic hybrid drive is ideal for vehicle transmission shafts and ship propulsion systems; Planetary reducers require high reduction ratios and compact structures, and integrated servo motors are commonly used for robot joints and precision speed regulation; Pneumatic artificial muscles focus on flexible movement and safety, requiring multi valve collaborative pneumatic control, and are mainly used in medical exoskeletons and bionic robots. The driving mode selection must comprehensively balance the mechanism's characteristics and the scenario's requirements.
In Mongolia, herders' daily pastoral decisions are usually made at a nomadic scale, i.e., the nomadic landscape covering their seasonal pastures; compromises between herder groups are made at a village level (_bagh_); and governance and commercial activity are coordinated at county (_soum_) and province (_aimag_) levels [20]. Because the nomadic scale determines the spatial extent of key natural interactions between climate, vegetation, and livestock, the natural interactions inherent in nomadic pastoralism need to be addressed at the landscape level of herders' seasonal pastures [21]. In contrast, the governance at the _soum_ or _aimag_ level can influence the natural interactions in the nomadic landscapes, thus creating a mismatch in scales between the natural processes and the socio-economic activities. The scale inconsistency, however, seems negotiable, taking into account regional similarities in nomadic pastoralism that reflect regional climate and vegetation characteristics [21]. The compromise in scale inconsistency can serve as a conceptual basis for using livestock census data that are collected at the level of the _soum_ administrative district [22]. However, this requires an implicit assumption that the _soum_ district is a collection of nomadic landscapes with identical climates, livestock, vegetation conditions, and their interactions. On this basis, using the _aimag_ administrative district, which has a much larger area than the _soum_, as the basic unit of analysis is problematic in reflecting the scale of natural heterogeneities in climate and vegetation [23]. Nevertheless, most _Dzud_ studies have been conducted at the scale of the _aimag_ district level, which inevitably leads to the underestimation of the effects of natural heterogeneities as well as local governance. This matter is partly due to the lack of climate and biophysical data suitable for the _soum_ scale covering the whole of Mongolia, which is necessary for analyzing the climate-vegetation-livestock relationships. In this context, multi-sensor remote sensing technology can be a good alternative to solve the problem as it provides diverse climate and biophysical datasets with spatial and temporal resolutions that are high enough for the _soum_ district scale across Mongolia [24; 25; 26]. To fill the research gaps mentioned above, this study (1) developed a schematic model of nomadic pastoral landscapes that describes the natural causal relationships between climate, vegetation, and livestock; (2) used the model as a conceptual basis to select variablesthat could be retrieved from multi-sensor satellite data, and (3) investigated how well these variables can explain the changes in livestock numbers and _Dzud_ occurrences in _soums_ across Mongolia. In this study, multi-sensor satellite data were used to produce datasets for the selected variables at the _soum_ district level, and multivariate statistical analyses methods were applied to determine the applicability of data extracted from multi-sensor satellites to answer the question.
## 3 Experimental Setup
### Methods  
In order to repeat the experiments conducted by other authors in the literature [21; 22; 23] for direct PBS image classification, several CNN architectures were used. They can be mainly categorised into plain, residual, inception and lightweight networks. Plain networks are very simple since they present just direct connections and are also the earliest CNN architectures. The employed CNNs belonging to this category are AlexNet [4], the VGG-16 and the VGG-19 [5]. They are very similar and differ mostly in the number of layers, 8, 16 and 19, respectively. Residual networks are typically much deeper, but being based on residual learning, they are easier to optimise [48]. We employed ResNet-18, ResNet-50 and ResNet-101, which present 18, 50 and 101 layers, respectively. Inception networks are those based on the inception layer. We employed GoogLeNet [49] and Inception-v3 [50], which differ in the number of layers, 100 and 140 for GoogLeNet and Inception-V3, respectively, but also because the latter exploits a few additional layer types. Lightweight networks are lighter networks specifically designed for mobile devices with minimal computing power and real-time executions. We employed ShuffleNet [51] and MobileNet-v2 [52], which present 50 and 28 layers, respectively. The CNNs were all pre-trained on a well-known natural image data set (ImageNet [53]). Thus, they could not be directly used for classification, but they had to be adapted, by transfer learning, for the new classification problem. Here it was done following an established procedure [54] that consisted of preserving all the pre-trained CNN layers and replacing the final one to match the number of classes of the new classification task.
## 4 Results
### Less Intense Satellite  
In order to further test the ability of the proposed FROG post-processing approach to yield the relative intensity of satellites from the measured trace, we include an additional case for FROG only (no such post-processing appears possible in SPIDER because the measured SPIDER trace of an unstable double-pulse is identical to that of a single pulse). In this case, we chose the second-pulse relative energy and intensity to be 0.43 and allowed its separation from the main pulse to vary from 600 to 900 fs. The range of pulse separations in this case is larger than in the other cases considered in this paper. The averaged FROG trace and retrieval is shown in Figure 6. FROG retrieves two low-intensity satellite pulses, but this is clearly not the correct pulse because there are no fringes in the side lobes of the measured trace. Indeed, the measured trace is clearly that of a double pulse, except that the central-lobe fringes are missing--indicating the presence of instability in the relative phase. Post-processing of the trace is then in order, and it correctly yields a relative energy of the second pulse is 0.43, despite the confusion of our FROG algorithm. As an aside, we should mention that, in principle, a new, improved FROG algorithm, trained to recognize such unstable pulse trains, should be possible.  
Figure 6: FROG measurement of a train of pulses with an unstable lower intensity satellite pulse. (**a**) Average FROG measurement of the whole pulse train; (**b**) Retrieved FROG trace with G error 0.0115; (**c**) Temporal intensity and phase of the pulse retrieved by FROG; (**d**) Spectral intensity and phase of the pulse retrieved by FROG.
## 7 Mapping and the Morphological Interpretation of Deposits
### The Lateral Distribution and Morphological Effects of Mineralization  
The spatial distribution model for this copper mineralization case is based on a regional mapping approach. It includes a compilation of available data for the major copper deposits in the Anti-Atlas [7; 21; 23; 90; 93; 94; 95; 96; 97; 98; 99]. This approach revealed significant differences in deposit density and its lateral distribution, manifested by the presence of three corridors with the most pronounced concentrations of mineralization (Figure 1). These corridors qualified as rich ridges by [PERSON] et al. [94], narrow southwest-northeast corridors oriented obliquely on the ENE-WSW direction of the Anti-Atlas. One of their particularities is that they are all longitudinally bisected by normal fault and traversed by CAMP doleritic dykes (Igherm dyke, Foum-Zguid dyke, and Zagora El Fetch dyke), from which these rich corridors are named. Therefore, we can distinguish the western ridge of Igherm, the central ridge of Foum-Zguid, and the eastern ridge of Zagora-El Fecht (Figure 1). These tectonomagmatic corridors appear to control copper deposits within the Lower Paleozoic cover and must have influenced the genetic process of the mineralization. The association  
Figure 10: (**a**) Mineralized veins of Timarighine, south of Saghro (N 30\({}^{\circ}\)52\({}^{\prime}\)39, 33\({}^{\prime\prime}\); W 5\({}^{\circ}\)36\({}^{\prime}\)49, 83\({}^{\prime\prime}\)). (**b**) Sectional view illustrating the morphological variations (thickening, thinning, branching) of the veins. (**c**) Chalcocite, malachite, and dolomite stockwork (core sample from [PERSON], Bourque [21]).  
of these extensive tectonic structures with CAMP magmatism implicitly suggests that the copper hosted in the Lower Paleozoic cover of the Anti-Atlas is post-Hercynian. In the vision we defend in this note, crustal extension occurring concurrently with the Central Atlantic opening would provide a favorable geodynamic and thermal environment for the reconcentrations of copper mineralization. Nevertheless, this new hypothesis must be supported by other arguments before it can be tested.
# A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control
###### Abstract  
Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.  
a _algorithms_
## 5 Discussion  
The accuracy of the abstraction model indicates how an abstraction model approaches the behavior of RNNs. In Figure 8, it is evident that PSO4 RNN performs better than BSL1. This is partly because the model learned by BSL1 only contains state transitions with maximum frequency, while PSO4 RNN can preserve almost all state transitions through probability distribution. In addition, it can be observed that BSL2 uses unproperied parameters to merge FPT in most cases. The complexity metric answers how many resources the learned model takes to mimic an RNN. Remarkably, abstraction models learned by PSO4 RNN are the most straightforward.  
The restoration degree indicates how a model keeps the original RNN patterns on the testing set. It stands for the generalizing ability of an abstraction model. PSO4 RNN performs much better in generalizing than BSL1 and BSL2. This means that PSO4 RNN learned more effective rules when analyzing unknown data. This might be attributed to the random strategy of PSO4 RNN to search for BIC parameters. For the abstraction error curve and time consumption results, PSO4 RNN shows the most efficient technique for learning from an RNN. Because BSL1 takes a lot of time to execute requirements from an RNN, it needs to maintain a huge observation table. BSL2 is also weaker than PSO4 RNN because BSL2 applies a trial-and-error strategy to learn PFA from the RNN. In contrast, PSO4 RNN uses PSO to search BIC parameters. It does not require testing all possible BIC parameters from a wide range. Guided by fitness, it can self-adaptively update these parameters towards a clearer goal.  
Concerning the usefulness of explanation, this work provides two forms tailored for different users: qualitative results and quantitative scores. Common users can directly, according to the color, discriminate the importance of features. Professional researchers may need a quantitative score to analyze in their subsequent studies. In part (2) of Section 4.2.2, we introduce a text adversarial case to prove the usefulness of learned PFA. The usefulness study of this work shows that such a quantitative explanation can serve well in real-world tasks.  
Figure 13: Adversarial text detection evaluation process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Adversarial Sample Detection Accuracy MSE** \\ \hline Tomita & 93\% \\ IMDB & 92\% \\ DLRV & 89\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Adversarial text detection accuracy based on PSO4 RNN.  
Based on the experimental analysis, it can be found that the performance of different techniques varies because of their advantages and disadvantages. The comparisons are compiled in Table 6, which offers a clear and systematic evaluation.
## 4 Self-Healing Techniques
### Polymers  
The aforementioned approaches outperform the self-healing action when they undergo distinct curing regimes wherein oily water, sea water, and water submersion constitute these regimes. An effective closing of the cracks only occurs when their sizes are below 50 \(\upmu\)m, thus, demonstrating the limits of this form of autonomous healing. Rather than using still water, a constant water leakage has been used by researchers to heal the crack formations in concrete, demonstrating an enhanced autonomous healing effect [41]. Shrinkable polymers have been utilized to induce post-tensioning of concrete, which results in improved self-healing efficacy [42]. Researchers have focused on the engineered cementitious composites, discovering their enhanced autonomous healing performance as a potential autonomous healing material [43]. Other studies found that, when the engineered cementitious composites are combined with the limestone powder, the functionality was almost entirely restored [44]. Further research was conducted on the carbonated steel slag to examine whether it can effectively act as a self-healing agent in concrete, wherein an optimum healed dimension of 20 \(\upmu\)m (wide) \(\times\) 5 mm (long) was achieved using this method. The width of the filled crack was evaluated using diverse techniques such as X-ray computed tomography, light microscopy, ultrasound scanning, and camera photography [45; 46].  
Few studies were conducted to extend the service life and autonomous healing of the concrete structures via the integration of polymers into the concrete mix [47]. Encouraging findings were achieved through this method. Nonetheless, the polymer types determined the efficacy of the self-healing products [48]. Additionally, the efficacy of the autonomous healing process was dependent upon the water-to-cement ratio, type of cement, and polymer dosages. Enhanced autonomous healing was also induced via the amalgamation of separate materials with the polymer [47]. It was concluded that a complete restoration in the CS can possibly be achieved by certain polymer-based self-healing agents [49]. Almost two thirds of the formed crack width was filled through the autonomous healing catalyzed by a polymer [50]. High values of ultrasonic pulse velocity (UPV) and improved flexural strength (FS) restoration were obtained [51; 52]. Additionally, performance of diverse methods for autonomously healing concrete were compared [53]. The method of utilizing a super-absorbent polymer was found to be better than the enacting encapsulation technique for the autonomously healing concrete. It is established that, because the autonomous healing can be induced without using water, the encapsulation techniques can be implemented in a greater range of applications.
###### Abstract  
A methodology is presented for the quantitative assessment of soil biomass productivity at 100 m spatial resolution on a national scale. The traditional land evaluation approach--where crop yield is the dependent variable--was followed using measured yield and net primary productivity data derived from satellite images, together with digital soil and climate maps. In addition to characterizing of soil biomass productivity based on measured data, the weight of soil properties on productivity was also quantified to provide measured soil health and soil quality indicators as an information base for designing sustainable land management practices. To produce these results, we used only the Random Forest method for our calculations. The study considers high-input agriculture, which is predominant in the country. Biomass productivity indices for the main crops (wheat, maize and sunflowers) and general productivity indices were calculated for the whole agricultural area of Hungary. Results can be implemented in cadastral systems, in applied in agricultural and rural development programs. The assessment can be repeated for monitoring purposes to support general monitoring objectives as well as for reporting in relation to the United Nations Sustainable Development Goals. However, on the basis of the results, we also propose a method for periodically updating the assessment, which can also be used for monitoring biomass productivity in the context of climate change, land degradation and the development of cultivation technology.  
random forest; land evaluation; soil; biomass; Hungary; gross primary productivity; soil health; soil quality +
Footnote †: journal:  
1 Centre for Agricultural Research, Institute for Soil Sciences, Hermann Otto 15, H-1022 Budapest, Hungary 1