esa-sceva/satcom-chunk-collection huggingface dataset:
# Digestate Quality Originating from Kitchen Waste
## 6 Quality Concerns--Regulations
On 23 June 2021, additional Commission Delegated Regulation (EU) 2021/1768 [72] entered into force, adapting to Regulation (EU) 2019/1009 of the European Parliament and the Council, to technical progress, amending Annexes I, II, III, and IV to the regulation of the European Parliament and of the Council (EU) 2019/1009. In turn, on 5 May 2022, the Commission Delegated Regulation (EU) 2022/1519 was published, amending Regulation (EU) 2019/1009 of the European Parliament and of the Council regarding the requirements applicable to EU fertilising products and further processing of the digestate. The document refers to the need to define the rules for further processing of the post-fermentation product, e.g., the separation of digestate into a solid and liquid fraction. It was noted that Regulation (EU) 2019/1009 should enable producers to further process the fermentation product or its fractions with methods that effectively close the loop of valuable components and nutrients. Fertilising Product Regulations (FPR) (EU) 2019/1009, among others, define the requirements of CE-marked fertilising products available on the EU market. Compost and digestate as components of fertiliser products may also be present on national markets according to separate regulations that apply in each area, since FPR does not replace national legislation. However, compliance with local marketing regulations does not allow for affixing the CE marking. Member States may have similar but different national rulesfor fertilising products, and hence its producer's choice to sell the fertiliser as 'EC fertiliser' or 'domestic fertiliser'. It is worth emphasising that currently there is no regulation imposing an obligation to assess the plastic content in digestate from kitchen waste. In the UK, the related PAS110 regulation has been introduced to certify digestate, covering the identification of physical contaminants such as plastics. However, despite the regulation being in force, there are no corresponding guidelines regarding methods for analysing physical contaminants [73]. Several peer-reviewed scientific papers on plastic content in digestate from biowaste, including kitchen waste, present a wide range of results, varying from 70 to more than 7000 plastic particles per kilogram [12; 74; 75; 76]. Additionally, some results are presented as a percentage of dry mass per sample [77; 78], thus limiting the possibility of unit conversion and comparisons when the size of an impurity is unknown. The lack of standardisation and uniform methods for determining the presence of plastics in digestate from kitchen waste or biowaste poses difficulties in establishing officially applicable safe limits [78]. In this regard, it is likely that in the upcoming years, concerns about the assessment of the risk of plastic debris in digestate from kitchen waste will rise.
## 7 Conclusions
_ABREVIATIONS_  
3 GPP 3 rd Generation Partnership Project  
4G  Fourth Generation Mobile Networks  
5G  Fifth Generation Mobile Networks  
5 GC 5G Core  
5G-H 5G-Horizontal: Operating Time Dynamic Multi-Provider Orchestrator for  
5G and Future Generations Mobile Networks  
5G-PPP 5G Infrastructure Public Private Partnership  
5 QI 5G QoS Identifier  
6G  Sixth Generation Mobile Networks  
AI  Artificial Intelligence  
API  Application Programming Interface  
AR  Augmented Reality  
CBR  Constant Bit Rate  
CI  Confidence Interval  
CLI  Command Line Interface  
E2E  End-To-End  
ETSI  European Telecommunications Standards Institute  
gNodeB  Radio base station  
GUI  Graphical User Interface  
IMT-2020  International Mobile Telecommunications-2020 (5G)  
IoT  Internet of Things  
ITU  International Telecommunications Union  
JSON  Java Script Object Notation  
LCM  Life-Cycle Management  
LTE  Long-Term Evolution  
MADM  Multiple Attribute Decision Making  
MANO  MANagement and OrchestrationMLMachine Learning MNO Mobile Network Operator MOS Mean Opinion Score NFV Network Function Virtualization NFVO NFV Orchestrator NS Network Slice NSI Network Slice NSI Network Slice Instance ONAP Open Network Automation Platform OSM Open Source Mano PNF Physical Network Function QoE Quality of Experience QoS Quality of Service QoV Quality of Video RAN Radio Access Network SD Standard Deviation SDN Software-Defined Networking SDR Software-Defined Radio SLA Service Level Agreement SNSI Sub-Network Slice Instance SP Service Provider TIP Telecommunications Infrastructure Provider TOPSIS Technique for Order Preference by Similarity to Ideal Solution UE User Equipment UPF User Plane Function V2X Vehicle-to-Everything VAR Variance VIM Virtualized Infrastructure Manager VM Virtual Machine VM Virtual Machine Monitor VNF Virtual Network Function VNF
## 4 Coordinated Control Example and Simulation
### Simulation
#### 4.2.1 Construction of the Simulation Model  
Taking two adjacent intersections with a spacing of 495 m on an arterial road in Shenzhen as an example, a simulation model of arterial road coordination control was established by VISSIM software. There were bus lines and bus stops on both directions of the arterial road, and a travel time detector was set up to collect vehicle delay data. The simulation model is shown in Figure 3. The Chinese characters on the image are the names of the several surrounding places, like libraries and parks, which come with the map originally.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 3 Instrumentation, Results and Discussion
### Instrumentation  
Figure 1 shows the working principle of a GNSS buoy for monitoring the water surface elevation. The GNSS receiver receives signals from satellites and calculates positions. The position data are then transmitted to the VBS-RTK control center by a GPRS modem in a GGA sentence that includes, among other information, the GPS time, latitude, and longitude.  
VBS data for RTK positioning are calculated in the control center and transmitted back to the GNSS receiver. Before 1 September 2014, only RTCM ver. 2.3 was transmitted. RTCM ver. 2.3 includes differential GPS corrections, GPS reference station parameters, RTK uncorrected carrier phases, and RTK uncorrected pseudo-ranges. RTCM ver. 3.1 includes GPS extended RTK (L1 and L2), stationary antenna reference points without height information, antenna descriptors, GLONASS extended RTK (L1 and L2), Helmert/Abridged Molodenski transformation parameters, residuals (ellipsoidal grid representation), and receiver and antenna descriptors [23]. In the experiments conducted for this work, the rover station used RTCM ver. 2.3 for RTK positioning. The output rate is 1 Hz. Data, including the altitude above the geoid and the quality index, are transmitted to the receiving system for recordingUsing the quality index, we can identify whether the solution type is a phase differential RTK solution with fixed ambiguities, which represent good data and will be used for further analysis.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 4 Channel Sum-Capacity in LoS and Favorable Propagation Conditions
### Instantaneous Sum-Capacity PDF in Low-SNR Regime and Favorable Propagation Condition  
The instantaneous sum-capacity in the low-SNR regime and favorable propagation condition is given by \[C_{\text{inst.}}=\sum_{k=1}^{K}\log_{2}\left(1+\frac{\rho M\eta}{d_{k}^{2}}\right) \tag{27}\] \[\stackrel{{\frac{\rho\text{dist.}}{d_{k}^{2}}<1}}{{ \approx}}\rho M\eta\log_{2}\left(e\right)\sum_{k=1}^{K}\frac{1}{d_{k}^{2}},\]  
which is a random variable that depends on the distance of the \(k\)-th device to the BS, denoted by \(d_{k}\).  
**Lemma 1**.: _By empirically comparing the normalized histogram of the random variable given by Equation (27) against the theoretical PDF of a Gamma random variable we notice that as \(K\) increases that \(C_{\text{inst.}}\) can be approximated by the Gamma PDF with parameters \(\kappa\) and \(\theta\) given by_  
\[\kappa=\frac{3 KRR_{min}}{(R-R_{min})^{2}}, \tag{28}\]  
\[\theta=\frac{\rho M\eta\log_{2}\left(e\right)(R-R_{min})^{2}}{3R^{2}R_{min}^{ 2}}. \tag{29}\]  
_This comparison is shown in Section 6. The parameters \(\kappa\) and \(\theta\) are found following the same rationale used in Appendix D._
## 3 Results
### Calculation and Accuracy of Digital Elevation Model (DEM)  
To obtain a digital elevation model (DEM), we interpolate the calculated ground points using ArcGIS by ESRI Inc. (380 New York Street, Redlands, CA 92373, USA). According to ESRI [22], the Natural Neighbour method is also well suited for distributed point clusters, for example from terrestrial laser scan recordings.  
The equation for the Natural Neighbour (NN) interpolation is:  
\[G(x,y)=\sum_{i=1}^{n}w_{i}f(x_{i},y_{i}) \tag{9}\]  
where \(G(x,y)\) is the NN estimation at (\(x,y\)); \(n\) is the number of nearest neighbours used for interpolation; \(f(x_{i},y_{i})\) is the observed value at (\(x_{i}\),\(y_{i}\)); and \(w_{i}\) is the weight associated with \(f(x_{i},y_{i})\).  
Based on the computed results, we calculated a digital elevation model (DEM) with a cell size of one meter. For the evaluation of the results, we used a DEM of the test areas, where the non-ground points had been filtered out manually by optical valuation before. We calculated the difference between the automated filtered DEM and the manually filtered DEM.  
In addition to the results of the new filter approach we described, we compared DEMs of the test areas with the results of the automated filter methods described by [PERSON] and [PERSON] [14].  
To evaluate the accuracy to the reference DEMs, we calculated the mean error and the root-mean-square-error (RMSE). According to the ASPRS (American Society for Photogrammetry and Remote Sensing) Guidelines [23] and Gianinetto and Fassi [24], the RMSE is often used to assess the accuracy of elevation data and is defined as (10):  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}{(\Delta{Z_{i}})}^{2}}{n}} \tag{10}\]  
where \(\Delta{Z}i\) are the elevation residuals (i.e., the differences of the elevation measures with respect to reference data); \(n\) is the number of measures.  
[PERSON] et al. [25; 26] further recommend using the median, the normalized median absolute (NMAD), the standard deviation, the 68.3% quantile and the 95% quantile of absolute residuals for accuracy assessment of DEMs.
## 3 Drone Routing Algorithms
### Trajectory Planning
#### 3.1.5 Collaborative Routing Problem-Truck and Drone (CRP-TD)  
The work in [15] investigates a collaborative truck-drone routing problem for contactless parcel delivery (CRP-TD) during the COVID-19 pandemic. It develops an improved variable neighborhood descent (IVND), which combines the metropolis acceptance criterion of simulated annealing and the tabu list of the tabu search algorithm to solve the formulated problem, which minimizes the delivery time and considers energy consumption of drones. It also integrates the K-means clustering and the nearest neighbor strategy to generate an initial solution. The performance of IVND was evaluated by comparing it with other algorithms on instances with different scales as well as testing several critical factors to verify its robustness. The experimental results demonstrate that IVND has superior performance compared to other benchmarks in terms of both speed and accuracy.
## 3 Homotopy DCD with Non-Convex Regularization Algorithms
### Extension to Multiple Measurement Vectors (MMV) Case  
Although we have derived HR-DCD, Hlog-DCD and H\(l_{p}\)-DCD algorithms from a single measurement vector (SMV), the results can be generalized to MMV case. In some kinds of applications, such as wideband source localization [42], multiple frequency bins are explored. As we all know that single frequency results can be easily disturbed by noise and interference, and in this issue, we can use a frequency diversity to achieve better performance. A traditional method of modifying the above approach to multi-frequency signals is averaging the results of all the frequencies. But due to the presence of noise, environmental mismatch and interference, different frequencies may give different results, thus this combining method may not work well.  
By the similar approach used in [28], our proposed methods require that for all frequencies to have the same support, which utilize the joint sparsity pattern, and choose an element to add to the support according to the corresponding propositions to achieve greater frequency diversity and avoid the possible wrong results. And the final result can be given by averaging all the results of all the frequencies through this way.
## 3 FE Modeling of the Runyang Cable-Stayed Bridge
### Validation of FE Model  
First, the cable forces under gravity load are calculated and are compared with the test results (half bridge), as shown in Figure 7, where good agreement is observed, showing the effectiveness of the FE model. It is worth noting that the forces of cables near piers, pylons and mid-span are relatively larger. Note that the cables in Figure 4 are labeled from \"Zhenjiang\" to the south tower as A13, A12,..., and A1, from the south tower to mid-span as J1, J2,..., and J13, from mid-span to the north tower as J1\({}^{\prime}\), \(\mathcal{J}^{\prime}\),..., and J13\({}^{\prime}\), from the north tower to \"Yangzhou\" as A1\({}^{\prime}\), A2\({}^{\prime}\),..., and A13\({}^{\prime}\), respectively.  
In addition, one static load case (_i.e._, load case 1) in the completion test of the bridge, as shown in Table 1, is randomly selected to further validate the force increments under vehicle loads. As shown in Figure 8, the calculated force increments, especially for the long cables near piers and mid-span are in good agreement with the measured ones.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Load Test** & **Truck Positions** & **Illustration** & **Truck Configuration** \\ \hline \multirow{4}{*}{Case 1} & Sixteen trucks, symmetrically loaded at the mid-span in 4 lines and 4 rows & \\  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of load case 1.  
Figure 6: FE model of the Runyang Cable-stayed Bridge.  
Figure 7: Comparison between calculated and measured cable forces (under gravity load).
## 3 Solutions for Different Points of Optimization in Urban Cold Transportation
### Points of Optimization  
Most research on environmental optimization in the urban cold chain fundamentally focuses on GHG emissions. The main emissions from urban cold transportation can be divided into fuel/energy emissions, food and medicine waste emissions, and equipment lifetime emissions. Although equipment and fuel consumption are closely associated, it is easier from an optimization perspective to consider fuel and energy emissions as equipment-specific optimizations and emission reductions caused by, for example, switching the delivery vehicle from an ICEV to an EV, as affecting equipment lifetime emissions.  
There are different kinds of algorithmic optimization methods for each of these emission categories. For the fuel or energy consumption of operations, the most significant algorithmic optimizations have to do with routing. Emissions in the form of degraded and discarded goods need to be tackled from multiple angles, as product degradation can arise both when hardware is working as intended and when breakdown occurs. In the former case, algorithms are needed for modeling the behavior of perishable goods in different conditions to improve current cooling technology. As for the breakdown or malfunction of devices, predictive algorithms can be utilized to both predict when the cooling device will need maintenance and to minimize the damage when equipment failure does occur by detecting it early. Life cycle assessment typically does not involve algorithms but, when in possession of these lifetime emission estimates, algorithms can be used to analyze the data to find the optimal measures to enhance the environmental performance of a given fleet in a given economic frame.  
Lastly, the degree of resource utilization of a cold transport operation can also significantly affect its final emissions. It is a common problem for trucks to be driven half empty due to the temperature constraints of products, combined with a limited demand for one type of product; this means that transport potential is left unused, leading to greater emissions. Although the equipment in use can also significantly affect the degree of utilization, packing problems are an algorithmic way to optimize the contents of each truck to achieve a higher degree of resource utilization.  
Additionally, it is important to note that urban logistics and cold transportation exist within the wider urban transportation network. Congestion is a ubiquitous problem in these urban traffic environments, and logistics and cold transportation--which often have tight delivery schedules--are not immune to these challenging traffic conditions, with unpredictable changes caused by traffic accidents and construction. Traffic prediction usingreal-time data can be added to routing algorithms to make them more accurately reflect travel times in the dynamic traffic system. Congestion can be alleviated with improvements to general traffic infrastructure, which can algorithmically be achieved by, for example, introducing smart traffic fees for the use of specific roads or parking, or smart traffic lights.
###### Abstract
For example, the works [12,13] used past air quality records as a reference to calculate the spatial quality relationship between \"areas with monitoring stations\" and \"areas without monitoring stations\" and applied this relationship to predictions. Most studies have attempted to achieve the two goals stated above separately or in one model [14, 15, 16]. In this study, we focus on forecasting fine-grained air quality for every region, including the one without a monitoring station. The proposed model has three major components: feature extractor, inversed distance attention layer, and hybrid predictor. We use the attention layer to learn the relationship between the embedding results of the light feature extractor and the general feature extractor. Then, we apply the inversed distance as an amended coefficient to strengthen the influence of high correlation monitoring stations. Finally, the hybrid predictor generates long-term air quality predictions. To summarize, the main contributions of this study are as follows:  
1. To the best of our knowledge, we are the first to combine the air quality forecasting of monitoring stations, and predict the air quality of sites without monitoring stations, forecast the long-term fine-grained air quality without monitoring stations and realize the goal of long-term prediction for locations without monitoring stations. 2. This study proposes a hybrid predictor with two complementary data flows that can perform well in long-term prediction for the locations without monitoring stations. 3. The experimental results show that the proposed model outperforms the baseline methods. In addition, the ablation study confirms the effectiveness of the proposed structure. The rest of this paper is organized as follows. In Section 2, we introduce the related work. We formulate the problem and introduce the data in Section 3. In Section 4, we introduce our proposed model in detail. In Section 5, we experimentally evaluate the effectiveness of the proposed solution by comparing different methods. Finally, we conclude our work with a discussion about future work in Section 6.
## 3 Controller Design  
In practical industrial application, PMSM is affected by disturbance, which includes external disturbance and internal disturbance. Despite controller is robust, but these nonlinear disturbances still have an impact on system, moreover, feedback-based control method cannot react promptly to disturbances. For speed loop, a sliding mode speed regulation controller is designed, and a feed-forward compensator based on ESO is designed for not only speed loop but \(q\)-axis current loop.
## 3 Design of Voltage Detector
### Implementation of Voltage Detector  
According to Figure 2, the cell temperature of PV panel rises gradually in the sunlight, the output power decreases as the cell temperature rises, and the output voltage decreases as the cell temperature rises. Because of this characteristic, at the first MPPT, the voltage \(V_{m}\) can be regarded as the MPPvoltage. When the cell temperature changes, the PV curve of the PV panel shifts left, since the voltage from the PV panel is related to cell temperature. The following equation can be obtained:  
\[V=\frac{n_{s}bTA}{q}\ln(\frac{n_{p}I_{ph}+n_{p}I_{sat}-I}{n_{p}I_{sat}}) \tag{17}\]  
\[V_{m}=V+\sigma(T_{c}-25)\,, \tag{18}\]  
where \(T_{c}\) is the surface cell temperature of PV panel and \(\sigma\) is the cell temperature compensation coefficient. The control mode of voltage detector is shown in Figure 4.  
The operating method of the controller is shown in the above figure; when the dynamic errors \(\varphi_{1}\) and \(\varphi_{2}\) are smaller than the set \(\varepsilon_{1}\) and \(\varepsilon_{2}\) values, the error magnitude does not exceed the set value, meaning if the PV panel is working somewhere about the MPP, the MPPT is not executed. On the contrary, when the dynamic errors \(\varphi_{1}\) and \(\varphi_{2}\) are greater than the set \(\varepsilon_{1}\) and \(\varepsilon_{2}\) values, meaning the PV panel is not working around the MPP due to cell temperature change or other factors, the MPPT is restarted to track the MPP.  
The flowchart of the fractional order incremental conductance method used in this paper is modified as Figure 5. This method uses voltage V and current I as inputs. The variation of voltage and current are approximated as \(d^{\alpha}I=I-\alpha I_{o}\) and \(dV^{\alpha}=(V-V_{o})^{\alpha}\).  
This controller does not implement MPPT when the dynamic error is not large, so as to reduce the power oscillation loss by MPPT in steady state. The simulation and experimental results are shown in the next section.  
Figure 4: Schematic diagram of chaos synchronization dynamic error detector control.
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 4 Power Control Techniques
### MUE-Assisted Power Control Technique  
In this technique, the femtocell tunes its transmission power based on the measured information sent by the macro user via the macrocell [25]. This is due to the non-existence of a direct interface between the femtocell and MUE [26], which brings an extra signaling overhead. To mitigate interference with the MUE, the optimal transmit power of the femtocell is given as:  
\[P_{MUE\.Assist.}=\max(P_{min},\ \min(P_{1},P_{max})) \tag{6}\]  
\[P_{1}=10\log\left(10^{S_{MUE}+\varepsilon_{1}}\right)/10-10^{I/10})+PL_{FBS \to MUE} \tag{7}\]  
In (7), interference to the MUE from different femto BSs is \(I\). The received power at the MUE from the macro BS is denoted as \(S_{MUE}\) and \(\varepsilon_{1}\) is the necessary QoS requirement for the MUE. The path loss from the femtocell to MUE \(PL_{FBS\to MUE}\) is given as:  
\[PL_{FBS\to MUE}=\max(15.3+37.6\log_{10}\left(R\right),38.4620\log_{10}\left(R \right))+L_{ow} \tag{8}\]  
where \(L_{ow}\) is the outdoor wall penetration loss, which is 10 dB.