esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For the design requirement of an SDWL with a small wavelength separation, the threshold pump power can be controlled by changing the intra-cavity losses of different wavelengths through the insertion of either an etalon or a birefringent filter within the resonant cavity [21; 22]. Etalons are generally used for dual-wavelength lasers with frequency intervals smaller than 10 nm, while birefringent filters are more commonly used for dual-wavelength generation with frequency intervals of several tens of nanometers. However, since the method involves the insertion of elements to control losses, it may have some impact on the efficiency of the laser output. Under fixed resonant cavity parameters, by altering the stimulated emission cross-sections, it is possible to control the threshold pump power at different wavelengths and thus achieve dual-wavelength laser emission. One approach to modifying the stimulated emission cross-sections is to change the angle between the laser propagation direction and the crystal axis, or to adjust the cooling temperature of the crystal [23; 24; 25]. The technique of achieving dual-wavelength lasers through cryogenic temperature tuning exploits the Stark splitting of the laser's upper and lower levels, which is induced by the unique lattice structure of the crystal. Further investigation of this technique shows potential for generating dual-wavelength lasers that are difficult to produce with conventional crystals, thus meeting the demands of various application scenarios. In this work, we report for the first time a temperature-controlled cryogenic Tm,Ho:YAP laser with balanced output at 2000 nm and 2119 nm and conducted a detailed study of the dual-wavelength balancing process for the SDWL. This temperature-controlled gain balancing method for two resonant waves originating from transitions of different energy levels enables the use of a single, mature and reliable laser gain medium to achieve more universally applicable dual-wavelength laser outputs. At an incident pump power of 11.8 W, a balanced output power of \(\sim\)1.51 W was generated, corresponding to an optical-to-optical conversion efficiency of 12.7% and a slope efficiency of 15.8%. Moreover, the beam-quality factors of the two wavelengths were M\({}_{2000}^{2}=1.39\) and M\({}_{2119}^{2}=1.30\), respectively. We experimentally obtained the optimal temperature for balancing the output powers of the two wavelengths. Considering the local heating effect from the pump absorption, the optimum temperature for the simultaneous dual-wavelength emission was experimentally found to vary with pump intensity.
## 3 Results
### Result Analysis of Drift Potential Reduction Percentage of Teejet 80-03 Nozzle  
The drift potential reduction percentage under different spray conditions is shown in Figure 6. It can be seen from the figure that the \(DPRP_{V}\) and \(DPRP_{H}\) of most spray conditions are greater than 0, indicating that these spray parameters effectively reduce the probability of droplets drift in the vertical and horizontal directions compared with the reference spray parameters.  
In the case of the same spraying medium, both \(DPRP_{V}\) and \(DPRP_{H}\) decrease with the increase of pressure, because it has been found that the increase in pressure leads to an increase in the proportion of small droplets and an increase in the probability of drift.  
Under the same spray pressure, the \(DPRP_{V}\) and \(DPRP_{H}\) of different concentrations of PEG-20,000 solution are greater than that of clear water. When the spraying medium is PEG-20,000 solution, \(DPRP_{V}\) and \(DPRP_{H}\) have no obvious change trend with the increase of concentration. The \(DPRP_{V}\) at a concentration of 20 g/L is greater than that of 10 g/L, but the \(DPRP_{H}\) is less than that of 10 g/L, while the \(DPRP_{V}\) and \(DPRP_{H}\) at a concentration of 30 g/L are both less than those of 10 and 20 g/L polyethylene glycol respectively. This shows that, although the droplet size increases with the increase of concentrations of the PEG-20,000 solution, the deposition decreases at a horizontal distance of 2-3 m from the nozzle and decreases at a vertical distance of 0.1-0.2 m from the ground of the wind tunnel, but the deposition at a horizontal distance of 4-7 m is still sufficient to affect the final drift potential reduction percentage.
## 5 Experimental Results and Analysis  
The experimental environment uses the Python3.8 language environment (Anaconda3). The operating system is Windows 10. The configuration of the experimental machine is a Core i5 CPU with a main frequency of 3.6 Ghz.  
Figure 3: DQNAgent model.  
This paper has designed a total of four scale scenarios, including small-scale retail models, medium-scale town models, large-scale regional clusters, and extra-large-scale urban models. Each scenario consists of 10 variants, with each variant being run 10 times. The termination condition for the experiments is time-controlled, ensuring that each test is uniformly evaluated under similar temporal constraints. The termination condition during the experiment was set to reaching the predetermined running time. Verify the performance of the proposed MODRL-SIA framework algorithm in solving LRPTW. Customer points are randomly distributed on a 100\(\times\)100 km map, and the central warehouse coordinates are (50, 50). Optional auxiliary warehouses are distributed at (25, 25), (75, 75), (25, 75), and (75, 25) with a construction cost of 1000 yuan each. The size of customer points is divided into small-scale (30 customer points), medium-scale (50 customer points), large-scale (80 customer points), and ultra-large (100 customer points). The number of central warehouses is set to one, and the number of optional auxiliary warehouses is set to four. The time window length of the customer point is set to 100 min, and the start time of the time window of the customer point is set to a random number between 0 and 1340. The demand at the customer point is set to a random number between 1 and 100 kg, the unloading time at the customer point is set to 30 min, and the time window of the warehouse is set to 0-1440 min. Taking a 4.2 m refrigerated truck as an example, fuel consumption is 15 L per 100 km, the gasoline cost is 8 yuan per L, refrigeration emits 0.2 kg of C0\({}_{2}\) per hour, and gasoline combustion emits 2.254 kg of C0\({}_{2}\) per liter. The vehicle traveling speed is 0.83 km/min, the vehicle fuel cost is 1.2 yuan/km, and the vehicle fixed cost is 100 yuan. The maximum load capacity of the vehicle is 1000 kg, and the maximum driving distance of the vehicle is set to 1000 km. Transportation losses occur when a single vehicle trip is greater than 100 km, at 5% when less than 200 km, 10% when less than 300 km, 15% when less than 400 km, 20% when less than 500 km, and 30% when more than 500 km. According to the commonly used means of transport and their properties in reality, the details of test examples are shown in Table 2.
## 4 Challenges and Future Research Directions
### Network Management and Orchestration  
Different applications of smart grid systems require specific network slices in the case of 6G. Therefore, 6G needs to deal with heterogeneous network slices for different applications, as well as those that are crossed with multiple technical domains, such as radio access, cloud, core, and edge networks. Thus, intelligent network management and orchestration techniques are required for the use of a 6G wireless network in a smart grid system.  
Zero-touch service management and intelligent networks can be a feasible solution for the intelligent and autonomous network orchestration and management techniques for 6G [129]. Different applications are considered in this technique but smart grid applications should still be well studied. In addition, more ongoing research and studies are required for network management and orchestration techniques.
# Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia
## 2 Material and Methods
### Methodology  
#### 2.3.1 Calculation of Water Use Efficiency (WUE)  
In the methodology, the water use efficiency was calculated as the ratio between GPP and ET, according to Equation (1) [46; 47].  
\[\text{WUE}=\frac{\text{GPP}}{\text{ET}} \tag{1}\]  
where WUE is given in gC/mm\(\cdot\)m\({}^{2}\), GPP is given in grams of carbon per square meter (gC/m\({}^{2}\)), and ET is given in millimeters (mm).
# Automatic Symbol Resolution on Embedded Platforms by the Example of Smart TV Device
## 4 Background
### Firmware Security  
Software vendors try to secure their firmwares in order to make it more difficult or even impossible to use them during the attacks on Smart TVs. Firmware can be obtained in two ways: the first one is to dump it from the hacked device, and the second method is based on using publicly available tools that already have encryption keys extracted from hacked devices and can be used to unpack update files provided by the vendors on their websites. The four most popular methods for securing firmwares are presented and shortly discussed.  
* Firmware signing is one of the ways of protecting Smart TV devices before installing corrupted firmwares. This feature is implemented by the software vendor, who signs the firmware image with a private key, kept in secret. A device with such a feature enabled will first validate the firmware before accepting its installation. In case of detection compromised firmware integrity, the device will reject its upgrade or installation. This can be omitted by turning off the feature that checks firmware signing or uploading a rootkit when the firmware is already run by the system [59].
* Firmware encryption--different vendors use different types for firmware encryption. In the case of Samsung's devices, firmwares can be encrypted using multiple algorithms. There are already available tools for decryption of such firmwares ([[https://wiki.samygo.tv/index.php?title=Playing_with_Firmware_Images](https://wiki.samygo.tv/index.php?title=Playing_with_Firmware_Images)]([https://wiki.samygo.tv/index.php?title=Playing_with_Firmware_Images](https://wiki.samygo.tv/index.php?title=Playing_with_Firmware_Images)), accessed on 13 April 2021).
* Firmware data mangling is an example of applying the approach \"security by obscurity\". In this case, one or more base functions can be generated and blended into the existing code. Such an approach may mean that it will be more difficult or even impossible for the attackers to distinguish the base function's codes from the codes of the firmware [60].
* Removing symbols from firmware is another example of the \"security by obscurity\" approach. In this case, software vendors remove symbols (Figure 2) from the final version of the firmware [27]. In such case, if one wanted to hack the firmware, they would have to use hardcoded addresses instead of function symbols. This method is not a reliable one, because of possible differences with function's addresses between different firmwares [27] (Figure 3).
## 5 Simulation Results
### Impact of Components  
In this section, to prove that our proposed construction (\(E_{j}=I_{a,j}Q_{j,b}\left(\tau_{m}\right)\)) is valid, we evaluate the performance of the proposed LQFC protocol via simulations. We compare it with two schemes, namely, the LQRS protocol and the FCRS protocol. In the LQRS protocol, RNs are selected based on the LQoS, and in the FCRS protocol, we select RNs based on the NFC.  
Figure 2a-d depict the network performance versus the number of vehicles. Specifically, it is observed that the LQRS protocol outperforms other protocols (the LQRS protocol and the FCRS protocol) in terms of the delivery ratio, the delivery latency, the overhead, and the hop count. This is because our designed LQRS protocol by jointly considering the LQoS and the NFC to make relay selection. If we only consider the influence of the NFC on relay selection, we can find that it is close to LQRS protocol in terms of the delivery ratio, the delivery latency, and the hop count. However, the overhead value of FCRS protocol increases significantly because the selected node may have poor LQoS and cannot accurately transmit the message. For the LQRS protocol, and the converse is also true. Therefore, based on the above analysis, we clearly know that our proposed construction is valid.
## 4 Model Results
### Evaluation of the RF Model  
The constructed MPR model underwent a comparison with the two other models through 5-fold cross-validation and was based on the evaluation. For this, a method for estimating the bearing capacity \(q_{rs}\) was established using the training dataset. The formula is illustrated as follows:  
\[\begin{split} q_{rs}=17.6798+&\quad 5.0749\times q_{u}-6.6 152\times\left(\frac{l}{d_{sc}}\right)-0.130\times\left(q_{u}\right)^{2}\\ &+0.0018\times\left(q_{u}\right)^{3}+0.0247\times\left(q_{u} \right)^{2}\times\left(\frac{d}{D}\right)\\ &-0.4547\times q_{u}\times\left(\frac{d}{D}\right)^{2}+0.2410\times q _{u}\times\left(\frac{h}{d_{sc}}\right)\times\left(\frac{d}{D}\right)\\ &-247.4930\times\left(\frac{d}{D}\right)\times\left(\frac{h}{D} \right)^{2}+29.6706\times\left(\frac{t}{D}\right)\times\left(\frac{d}{D}\right) ^{2}\\ &+3.1539\times\left(\frac{l}{D}\right)\times\left(\frac{h}{d_{sc} }\right)\times\left(\frac{l}{d_{sc}}\right)\end{split} \tag{6}\]  
The MPR model was used to determine the load-bearing capacity of new data, and the results were evaluated using pair plots. Figure 5a shows a comparison of the predicted values for the load-bearing capacity to the actual values for the test data. The predictions from the MPR model align closely with the 45-degree line, which indicates that the model can provide accurate predictions for samples that were not included in the training set. The RMSE and \(R^{2}\) values for the test data were 15.655 and 0.983, respectively. Figure 5b displays the distribution of the predicted bearing capacity values from the MPR model, the actual values, and the errors. The mean, maximum, and minimum errors were 0.92, 38.93, and 0.56 kPa, respectively. It is worth noting that the means and standard deviations of the actual and predicted bearing capacities (\(q_{rs}\)) in the test data were 195.44 kPa and 120.11 kPa and 194.53 kPa and 118.29 kPa, respectively. Table 3 shows the calculated values for each experiment in the test set, as well as the corresponding actual and predicted values.  
Figure 4: Normal distribution of the residual errors of the 5-fold cross-validation training dataset: (**a**) MPR model, (**b**) LR model, and (**c**) RF model.
# An Overview of Quantum Machine Learning Research in China
## 5 Challenges and Prospects
### Challenges
#### 5.1.2 Hardware Limitation  
In the current NISQ era, quantum devices only allow the employment of a small number of qubits to represent data and perform simple quantum calculations. Hence, it is challenging to encode large-scale data into quantum state data and execute QML algorithms in quantum computers [34; 38; 60]. Therefore, it is necessary to implement feature extraction or other kinds of dimension reduction operations on high-dimensional datasets before executing the algorithms [65]. For instance, [PERSON] et al. proposed a scalable QNN system based on the collaborative utilization of multiple small quantum devices. The multiple quantum devices play the roles of quantum feature extractors and are independent of each other. Hence, researchers can flexibly combine quantum devices of different sizes to extract local features in a more efficiently way [43].
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
## 3 Results
### Maintenance Management  
The perception of maintenance management on healthcare facilities was analysed by [PERSON] et al. (2015). They provide an overview of the types of maintenance strategies applied to maintain facilities and a relationship between the types of maintenance strategies implemented and end user satisfaction was determined [41].  
[PERSON] et al. (2003) analysed the maintenance incidence in a hospital through a survey [42]. The communication and low voltage system, condition exterior envelope, interior finishing, water and waste-water, and HVAC were in deteriorating condition.  
Maintenance costs throughout the useful life of a facility are very important when choosing the appropriate HVAC system to be installed in healthcare buildings. The central equipment show lower maintenance costs than partial systems [43]. The annual energy saving through monitoring and control procedures [44] are estimated between 5% and 15% according to the International Performance Measurement and Verification Protocol (IPMVP). The maintenance costs represent between 2% and 10% of the original investment on equipment, depending on the complexity of the system [45].  
Economic and environmental impact of energy saving was determined through maintenance engineer interviews.  
Figure 6 shows the potential average energy saving per built surface area (m\({}^{2}\)) through the optimization of the maintenance management in healthcare buildings with less than 5000 m\({}^{2}\) (no beds) and hospitals larger than 5000 m\({}^{2}\) (with beds). An annual average energy saving of 0.45 kWh and 0.50 kWh per built surface area (m\({}^{2}\)), with a payback less than one year was achieved.  
A series of energy-saving measures were used, including the development of a tailor-made maintenance plan for each hospital, implementation of predictive maintenance techniques, processes for monitoring and control of facilities, and the usage of qualified labour.
## 4 Methodology  
### The Overall Framework of Our Approach  
The idea of shapelet transformation is to separate the shapelet extraction process from the classification method. Following dimensionality reduction through shapelet transformation, sequence data can be classified using a general classifier. This also establishes the overall framework of the sequence data classification method, based on shapelet transformation. The proposed feature extraction method for imbalanced sequences follows this framework but also includes the processing of imbalanced sequence data. Therefore, the detection method for LTE-R cells with degraded communication performance proposed in this paper is divided into two steps: (1) Using the modified CNN to extract features from key performance indicator data within the drive test data; (2) Using a general classifier (in this case, SVM) to classify data after feature extraction and achieve the detection of degraded cells. The overall framework of the proposed approach is shown in Figure 3.  
From Figure 3, it can be seen that for the one-dimensional sequence dataset composed of the measured values of each key performance indicator from the drive test data, the input one-dimensional sequence data are first passed through a one-dimensional convolutional layer to obtain every subsequence of the input sequence data. Afterward, these subsequences are input into a squared distance layer, in which the weights of the neural network layer represent the shapelets of the input sequence data. Therefore, the role of this layer is equivalent to calculating the distance between each shapelet and each subsequence of the input sequence. After passing through the distance calculation layer, the distance between each shapelet and each subsequence passes through a min pooling layer and the output is the distance between each shapelet and the input sequence data, which is the result of shapelet transformation. As mentioned in Section 3.1, we selected three key performance indicators from the drive test data to use as input data: RSRP, RSRQ, and SINR. In order to perform the shapelet transformation of these three indicator sequences simultaneously, the shapelet transformation results of each indicator sequence are merged in the output layer to form a unified model. In order to obtain shapelets that can maximally distinguish different class sequences, we propose a two-stage training method to train the model. In the first stage, the contrastive loss [34] is used as the loss function to train the feature extraction network. In the second stage, the differential evolution algorithm is introduced to optimize the weights of the squared distance layer while setting an optimization objective based on the Fisher criterion. The entire model training process can also be regarded as the process of finding the optimal shapelets for each sequence. At this point, feature extraction for the imbalanced multidimensional sequence dataset is complete. Finally, following feature extraction, the dataset is input into an SVM for classification, thereby achieving the detection of LTE-R cells with degraded communication performance.
## 4 An Efficient Convolutional Denoising Autoencoder Method for BDS NLOS Detection
### Overall Network Structure  
We focus on developing a generic DL network for NLOS detection. Figure 5 shows the overall network framework of CDAENet. Intuitively, the proposed CDAENet is jointly trained in an end-to-end manner and consists of two main sub-modules containing the DAE and MLP classifier components. The original data are corrupted by incorporating some noise components to make the model more robust, the encoder achieves feature extraction along with data dimensional compression, and the reconstructed input data are generated by the decoder. The decoder is similar to the encoder part in that the sigmoid is chosen as the activation function for the hidden layer of the model. Meanwhile, we employ  
Figure 4: Two types of BDS signal: LOS only includes direct signals and NLOS caused by only diffracted/reflected signals from “invisible” satellites. (Green is the LOS satellite, and pink is the NLOS satellite).  
a convolutional operation to extract local features of the input signal, thus helping the algorithm to understand the data better. Finally, the MLP model is used to further improve the detection performance.