esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Factors Affecting Frost-Heave Crack Propagation
#### 3.3.2 Influence of External Temperature  
The average value of the strain curve after peak stabilization should be regarded as the maximum generalized strain at the crack tip, as shown in Figure 11b. The relationship between the maximum generalized strain and the exterior temperature is illustrated in Figure 16. Linear model fitting was employed in this study, as it could better fit the experimental data. The expression between the generalized strain and the external temperature was obtained as follows:  
\[\varepsilon=-0.032T-0.19\ (-20\ \leq\ T\leq\ -5) \tag{21}\]  
By introducing \(\varepsilon^{ini}\) into Equation (21), the outdoor temperature range of the crack with which frost-heave propagation is prone to occur can be obtained, i.e., \(T<-6.6\ ^{\circ}\)C. There are a large number of cold regions around the world. For example, cold regions make up 53.5% of the total area of China. Consequently, maintenance measures should be taken to prevent crack damage in areas where the temperature drops below \(-6.6\ ^{\circ}\)C. This provides a suggestion for the prevention and maintenance of frost-heave cracking damage to slab-tracks in different areas.  
Figure 15: The relationship between the maximum generalized strain at the crack tip and crack width (\(d\)).
## 5 Simulation Results
### Case 6: Scheduling of the _EV_ Charging
#### 5.7.2 Evse-3  
This charging strategy implements a variable charger with discrete charging powers. An example is the Siemens VersiCharge systems [54]. This work considers the discrete power values of [3.3, 3, 2.7, 2.4, 2.1] kW, and the charger can turn on and off according to the signals from the IHEMS. Figure 11 shows that EV charging has expanded to 5 h as compared to EVSE-1 and EVSE-2. This charging strategy enables the EV to get charged more from the RES, and consequently, the utility loading decreases as shown in Figure 12. The peak utility load decreases to 1.14 kW at 01:00, which is a 62% decrease as compared to EVSE-1.
# The Evolutionary Traits of Carbon Emissions from the Planting Industry in Beijing, China
## 3 Results
### Carbon Emissions from the Planting Industry
#### 3.1.1 Carbon Emissions from Agricultural Inputs
During the period of 2011-2023, the average annual carbon emissions of agricultural inputs in Beijing's planting industry were 197,200 tons, a decrease of 37.61% compared to the average value during the period of 2000-2010. Among them, the carbon emissions of chemical fertilizers, pesticides, agricultural films, diesel, and irrigation were 82,600, 14,300, 49,100, 15,100, and 36,100 tons, respectively, accounting for 41.87%, 7.28%, 24.89%, 7.65%, and 18.32% of the total carbon emissions respectively. Compared with the period from 2000 to 2010, the contributions of chemical fertilizers, pesticides, and irrigation to the carbon emissions from the planting industry have not changed significantly. The carbon emissions created by agricultural film increased by nearly 5 percentage points, and the carbon emissions from diesel decreased by about 4 percentage points.
## 3 Results and Discussion
### Structure, Morphology, Surface and Spectroscopic Studies
The S 2p core level spectra in Figure 4c indicate that there are two chemical species in CdS nanoparticles [35], the binding energy has \(-0.3\) eV shift for S 2p\({}^{3/2}\) in P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite, indicating that the proportion increase of S-S bond due to the formation of TiOS on the surface [36]. The high resolution XPS spectrum in Figure 4d shows the existence of two peaks, which correspond to Ti2p in P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite, respectively [37; 38]. The O 1s spectra in Figure 4e show that two chemical states of oxygen coexist in P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite, and the peak located at 529.9 eV corresponds to the O\({}^{-2}\) in O-Ti-O. The existence of surface adsorbed hydroxide (\({}^{\bullet}\)OH) can be ascribed to 531.8 eV. While we can see only one peak at 531.79 eV in pure CdS due to the adsorbed hydroxide [39; 40]. The high resolution XPS spectrum in Figure 4f shows the existence of two peaks, which correspond to Zn2p in P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite [41; 42]. Figure 3: UV-vis absorbance spectra of pure CdS nanoparticles, Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanoflowers, and P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite with P25:Cd of 1:10, 1:5 and 1:3.
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 2 Generation Algorithm
### C2 Subnetwork  
As we can see from the structure in Figure 1, the combat network is divided into three levels, the structure shows that C2 subnetwork is similar to the head of whole combat SoS. Table A1 in Appendix A shows the details of all C2 nodes, and Figure 2 shows the initial structure of the C2 subnetwork. Node in the C2 subnetwork is shown in Table A1.  
The level-0 node plays the most important role in the reconfiguration. On one hand, the level-1 node act as a real-time backup of the level-0 node, which ensure conflict will happen, and the operation command could be continued when the level-0 node is destroyed; on the other hand, the level-1 nodes are empowered to take over the duty of level-2 nodes and any third-level command center when they are destroyed. Therefore, the robustness [24] of the C2 subnetwork is significant. All C2 nodes in the fourth-level have no constant entities, their work is done by subordinate nodes, which have the maximum degree.  
C2 subnetwork generation algorithm:  
Specify the C2 network level number L and specify the C2 level node number array \([N_{1},N_{2},\ldots N_{L}]\), where \(N_{L}\) is the central C2 node number and \(N_{L}\) is the outer C2 node number.  
1. Generate the first level of C2 nodes, the number of C2 nodes of the first level is \(N_{1}\), and generate the central node (root node \(D_{0}\)), which is also the highest level C2 node, the number is 1; set the level number flag \(h=1\);
2. The generation of second-level \(D_{i},i<N_{2},N_{2}\) nodes is the number of nodes, the second-level command nodes are connected to the central command node, and the same-level command nodes are interconnected according to probability \(P_{d}\) to form a command coordination relationship.
3. The outer command nodes are generated in sequence, and the level number flag \(h=h+1\) is set. The command nodes of the same layer are interconnected according to probability \(P_{d}\) to form a command coordination relationship, and according to probability \(P_{\text{C2}}\), they are connected with non-directly connected upper-level nodes to form a cross-layer command relationship.
4. Repeat step (4) until the C2 network is generated.
## 2 Calculation Methods
### Materials  
As the basic unit cells, we selected from cubic perovskites including CsSnCl\({}_{3}\), CsSnI\({}_{3}\), SrTiO\({}_{3}\), and SrRhO\({}_{3}\). To perform computational studies, we first optimized each of the halide or oxide perovskites individually. To investigate the structural and electronic properties of mixed perovskite compounds, we then constructed 2 \(\times\) 2 \(\times\) 2 supercells from various configurations of the four cubic perovskites. By studying the effects of various perovskite mixtures on the overall stability and bandgap, we sought to achieve values that were optimal for photovoltaic cell applications.  
We performed computations based on PAW pseudopotentials based on the electronic configurations of the elements as displayed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compounds** & **a (Bohr)** & **Experimental Data (Bohr)** & **Error** \\ \hline CsSnCl\({}_{3}\) & 10.639 & 10.609 [42] & 0.282\% \\ CsSnI\({}_{3}\) & 11.856 & 11.826 [42] & 0.253\% \\ SrRhO\({}_{3}\) & 7.522 & 7.427 [43] & 1.263\% \\ SrTiO\({}_{3}\) & 7.441 & 7.446 [44] & 0.067\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculated lattice parameter (a) and previous calculations as well as experimental data.
## 3 Experimental Flight and System Identification
### Linear Model Refinement in System Identification
Secondly, considering that the open-loop dynamic model could easily be retrieved from the closed-loop dynamic model, system identification using the gray box model approach with the indirect method was conducted. In the indirect method, the system matrix of the closed-loop CFD dynamic model in Equations (9) and (10) was used as the initial dynamic model. Through system identification with several different methods and various restriction settings, several identified dynamic models were obtained. The criteria to select the identified dynamic model were: (1) the fit percentage should be high enough; (2) time-domain responses of the closed-loop dynamic model should have similar characteristics to the experimental flight data; and (3) the open-loop dynamic model should have similar stable and unstable poles and zeros to those of the computationally calculated dynamic model, whereas the closed-loop dynamic model with the PD feedback controller should be stable.
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 6 Conclusions  
In this paper, we propose a dynamic deployment scheme for maritime UAVs based on route prediction, aiming to improve the communication coverage of mobile users under the constraints of UAV battery capacity, coverage, and computational resources. With an improved LSTM algorithm, we accurately predict the route distribution of maritime users, thus optimizing the deployment strategy. Combined with the PPO algorithm, this study innovatively utilizes the route-prediction results to enable the agent to formulate UAV-deployment strategies in advance based on the user distribution, thus effectively reducing the latency of task processing. Simulation results show that compared with traditional methods, the RUDD algorithm exhibits high stability in marine scenarios, the average processing delay of the task is successfully reduced to about 1 s, and the communication coverage is improved to more than 95%. This study provides an effective strategy for the deployment of UAV-assisted wireless sensor networks with a wide range of practical applications. Future research will focus on studying more complex marine environments and exploring how to further reduce the amount of algorithmic computation in order to further reduce the total system cost.  
Conceptualization, Y.X. and Y.S.; methodology, Y.X. and Y.S.; software, Y.S.; validation, Y.S.; formal analysis, Y.X.; investigation, Y.S.; resources, Y.X.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, Y.X.; visualization, Y.S.; supervision, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (62271303)/the Innovation Program of Shanghai Municipal Education Commission of China (2021-01-07-00-10-E00121), and the Natural Science Foundation of Shanghai (202R1423200) (author: [PERSON]).  
The data presented in this study are available in Section 5.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 7: Comparison of average energy consumption for different numbers of users.
## 5 Key 6G Technologies
### New Spectrum
#### 5.1.1 Millimeter Wave  
The introduction of mmWave technology by 5G new radio (NR) is expected to continue as a critical element in forthcoming 6G networks. In contrast to traditional RF technologies operating below 6 GHz, mmWave substantially expands the bandwidth by offering new frequencies up to 300 GHz. According to [PERSON]'s theorem, this significant increase in bandwidth will enhance the capacity of radio channels, satisfying the growing demand for higher data rates. At the same time, the reduced wavelength results in more compact antenna sizes. This enhancement not only boosts the device's portability and degree of integration but also facilitates the expansion of antenna array dimensions, consequently focusing the beams more narrowly. This aspect proves advantageous for particular uses, for example, radar identification and enhancing security [45]. Moreover, the characteristics of molecular and atmospheric absorption vary significantly within the mmWave band, offering opportunities for a variety of applications. Attenuation peaks, such as 60 GHz, 120 GHz, and 180 GHz, suffer significant propagation loss. In contrast, other frequencies, like as 35 GHz, 94 GHz, 140 GHz, and 220 GHz, have little attenuation and may be directly communicated across large distances. This feature can be utilized for short-distance confidential networks that have strict security demands [45; 46]. Presently, standardization initiatives in the mmWave domain are primarily concentrated on the 60 GHz band for indoor applications, as seen in standards such as ECMA-387 and IEEE 802.11 ad [47; 48]. Along with the advantages offered by mmWave technology, it introduces several new challenges. Firstly, the wide bandwidth available in the mmWave spectrum and the high power required for transmission can result in significant non-linear signal distortions, demanding more advanced technical specifications for integrated circuits compared to traditional RF devices. Furthermore, molecular and atmospheric absorption significantly reduces the optimum transmission distance of mmWave, especially in the 60 GHz range, thus strongly depending on LOS pathways for mmWave communication. Due to the poor diffraction qualities at such short wavelengths, this dependency becomes a severe constraint and causes substantial signal blockage in surroundings with plenty of small-scale barriers like cars, people, and even the user's body [49]. The fading effects are substantially more noticeable than in RF bands because of the significant propagation loss and dependency on LOS associated with mmWave technology. This also significantly increases the channel state's sensitivity to mobility. Consequently, there is an unparalleled need for excellent mobility management. Moreover, in situations where numerous links exist closely together, particularly indoors, the interference between different access points becomes substantial. Hence, there is a clear need for effective interference management strategies [50].
# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 5 Experiments  
### Experiments Presentation  
In this section, we introduce the experiment part of our work. As previously mentioned, we aim to train machine learning models to be able to detect road states. To this end, we have built a new dataset containing two types of road status: smooth and potholed. For each labeled segment, multimodal sensorial data were recorded from the accelerometer and the gyroscope of a driver's smartphone. As shown in Table 2 and Figure 6, a total number of seven features were recorded, namely the 3D accelerometer data (\(\text{acc}_{x}\), \(\text{acc}_{y}\), and \(\text{acc}_{z}\)), the 3D angular rotations from the gyroscope (\(\text{gyro}_{\times}\), \(\text{gyro}_{y}\), and \(\text{gyro}_{z}\)), and the speed of the vehicle. Thus, we aim through machine learning models to map the data from the smartphone sensors to the correct label (smooth or potholed). Technically this problem can be solved by a binary classification approach.  
To train our classifiers, a dataset was built using our collected data from our developed application in addition to a richer and larger dataset from the Kaggle website ([[https://www.kaggle.com/datasets/dextergoes/pothole-sensor-data](https://www.kaggle.com/datasets/dextergoes/pothole-sensor-data)]([https://www.kaggle.com/datasets/dextergoes/pothole-sensor-data](https://www.kaggle.com/datasets/dextergoes/pothole-sensor-data)), accessed on 14 November 2022). Merging the two datasets was a better approach since it makes the trained model more robust and less sensitive to a single smartphone or a single vehicle. The merged dataset contained nearly 1540 samples (1080 for the smooth label, 460 for the potholed label). Moreover, on average, each road sample corresponds approximately to one second of recording, which represents the time granularity of our road segments. Based on this dataset, several classifiers were deeply investigated to obtain the best hyperparameters that give the optimal classification results. From all the tested classifiers, seven classifiers were selected due to their efficient performance in our detection problem. A brief description of these classifiers is given in the next paragraph.
## 4 Experiments
### Ablation Experiments  
In order to better evaluate the effectiveness of each part of the model, comprehensive ablation experiments were conducted on two datasets for the model in this section. Firstly, we evaluated the two parts of PreferSAGE-SCF--PreferSAGE and SCF. Due to the inability of SCF to function independently, this paper divides PreferSAGE-SCF into three parts: PreferSAGE, PreferSAGE-UserCF, and PreferSAGE-SCF. Among them, PreferSAGE does not contain collaborative filtering; PreferSAGE-UserCF uses standard collaborative filtering methods. The experiments on dataset DA are shown in Table 3, and the results on dataset ml-1m are shown in Table 4. From the results, it can be seen that the SCF part is helpful in improving the predictive performance of PreferSAGE. After comparing these results with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Hit** & **F0.5-Score** & **NDCG** \\ \hline UserCF & 0.600 & 0.172 & 0.170 \\ ItemCF & 0.160 & 0.037 & 0.029 \\ GraphSAGE & 0.980 & 0.554 & 0.621 \\ GAT & 0.980 & 0.561 & 0.623 \\ ImprovedSAGEGraph & 0.920 & 0.565 & 0.631 \\ PinSAGE & 0.960 & 0.564 & 0.622 \\ PreferSAGE-SCF & 1.000 & 0.648 & 0.707 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on dataset DA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Hit** & **F0.5-Score** & **NDCG** \\ \hline UserCF & 0.377 & 0.062 & 0.093 \\ ItemCF & 0.513 & 0.098 & 0.145 \\ GraphSAGE & 0.609 & 0.148 & 0.261 \\ GAT & 0.617 & 0.150 & 0.266 \\ ImprovedSAGEGraph & 0.608 & 0.151 & 0.267 \\ PinSAGE & 0.621 & 0.155 & 0.268 \\ PreferSAGE-SCF & 0.666 & 0.174 & 0.291 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results on dataset ml-1m.  
Tables 1 and 2, we can also see that retaining only the PreferSAGE part can still achieve good results for the model.  
The next step is to conduct ablation experiments on the various components of the PreferSAGE algorithm. This paper divides PreferSAGE into four parts of the experiment, including No-Skip-Connection, No-Reference, and PreferSAGE. Among them, No-Skip-Connection does not use skip connections in the process of information aggregation; No-Prefer does not contain preference weights, that is, only spatial importance is used in importance sampling. The experiments on dataset DA are shown in Table 5, and the results on dataset m1-1m are shown in Table 6.
# Optimization of Valorization of Chicken MDCM to Produce Soluble Protein and Collagen Peptides
## 3 Results and Discussion
### Profitability Assessment  
The assessment was performed for a hypothetical fractionation process plant, treating 1.0 \(\times\) 10\({}^{7}\) kg of chicken MDCM side-stream annually, and yielding food-grade chicken protein hydrolysate, chicken bone collagen peptides, chicken fat, and calcium- and phosphate-rich minerals. The theoretical annual amounts of these products from 1.0 \(\times\) 10\({}^{7}\) kg of chicken MDCM were calculated on the basis of the values given in Section 2.7. A total yield of fractionation processing estimated as 80% was used to calculate the amounts of products obtained.  
The annual production, prices, and revenues of the chicken MDCM side products are presented in Table 4. The applied prices were based on expert and industry knowledge. According to the assessment, the total sales revenues for the side products were about 5.5 \(\times\) 10\({}^{6}\) EUR/year. Of this, 62% were derived from collagen peptides, and 31% were derived from MDCM protein hydrolysate.  
The main process equipment consisted of a high-pressure extraction unit, filters, centrifuges, dryers, and a packaging unit. The investment cost depended on the existing utilities and production facilities. If the process was integrated with a chicken slaughterhouse, the investment costs for the MDCM side-stream process were estimated at 1.0 \(\times\) 10\({}^{7}\) EUR, according to expert knowledge.  
The operating costs included materials, e.g., food grade protease enzymes, operators' salary costs, energy consumption, maintenance and repairs, and other variable costs. Operating costs were estimated at 2.0 \(\times\) 10\({}^{6}\) EUR/year, and the depreciation of investment and rate of interest were estimated at 2.0 \(\times\) 10\({}^{6}\) EUR/year for 10 years. Therefore, the estimated total annual production costs were 4.0 \(\times\) 10\({}^{6}\) EUR. The chicken MDCM fractionation process was profitable according to this preliminary assessment; the annual profit was about 1.5 \(\times\) 10\({}^{6}\) EUR. This value is in the same range as the profit values calculated for annual processing of 1.0 \(\times\) 10\({}^{7}\) kg of salmon filletting side-streams to various high-value products [37].  
Since prices are the main driver in the protein business case, the price stability is essential. The price of chicken collagen peptides has decreased in recent years, bringing uncertainties concerning the process's profitability. The investment is, therefore, classified as high-risk. The economic feasibility of the process also depends on how well the additional processes fit the existing process, and how well the resulting products can be marketed. The feasibility of a process is always a case-by-case issue, because the skills of the process designer, marketer, and buyer are essential.
# SSDLiteX: Enhancing SSDLite for Small Object Detection
## 1 Introduction  
Recently, convolutional neural networks (CNNs) show high performance in computer vision tasks including image classification [1; 2; 3; 4; 5; 6], object detection [7; 8; 9; 10; 11; 12], and image segmentation [13]. However, the enormous requirement of the storage and computation prohibits CNNs from being adopted in embedded systems that are power- or resource-limited. CNNs in such systems should have high performance with the small number of operations and coefficients.  
Many computer vision applications including autonomous vehicles and surveillance systems are related to object detection. A CNN for object detection is usually constructed by adding some auxiliary layers to a base CNN for image classification. There are various kinds of object detection CNNs, and widely used for resource-limited systems is the combination of MobileNet V2 and SSDLite [14; 12; 15; 16]. The network shows an appropriate detection capability with low complexity, but its performance is sometimes not so satisfactory, especially in detecting small objects.  
In this paper, some optimizations will be applied to SSDLite. It has been known that SSDLite maintains the performance but reduces the complexity compared to the previous version, SSD (Single Shot Multibox Detector) [11]. However, the detailed changes have not been analyzed. This paper will compare SSDLite and SSD, discussing the pros and cons of the difference. The analysis shows that SSDLite does not utilize high resolution feature maps sufficiently. By utilizing such feature maps, SSDLite can be optimized to have better performance, especially in detecting small objects, with slightly increased complexity.  
This paper is structured as follows. After discussing object detection neural networks in Section 2, the structures of SSD and SSDLite are compared in Section 3. Experimental results are presented in Section 4, and conclusions are described in Section 5.