esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate control of the shield attitude can ensure precise tunnel excavation and minimize impact on the surrounding areas. However, neglecting the total thrust force may cause excessive disturbance to the strata, leading to collapse. This study proposes a Bayesian optimization-based temporal attention long short-term memory model (BOTA-LSTM) for multi-objective prediction and control of shield tunneling, including shield attitude and total thrust. The model can achieve multi-ring predictions of shield attitude and total thrust by allocating larger weights to significant moments through a temporal attention mechanism. The hyperparameters of the proposed model are automatically selected through Bayesian hyperparameter optimization, which can effectively address the issue of complex parameter selection and optimization difficulties in multi-ring, multi-objective tasks. Based on the predictive results of the optimal model, an intelligent control method that considers both shield attitude and total thrust is proposed. Compared to a method that solely predicts and corrects for the next ring, the proposed multi-ring correction method provides the opportunity for further adjustments, if the initial correction falls short of expectations. A shield tunneling project in Hangzhou is used to demonstrate the effectiveness of the proposed model. The results show that the BOTA-LSTM model outperforms the models without the integration of a temporal attention mechanism and Bayesian hyperparameter optimization. The proposed multi-ring intelligent correction method can adjust the shield attitude and total thrust to a reasonable range, providing references for practical engineering applications.  
shield tunneling; temporal attention mechanism; Bayesian optimization; multi-objective control; deep learning
## 4 Experimental Results
### Results
#### 4.3.4 Class Activation Map (CAM)  
Class activation maps ([[https://github.com/chaeyoung-lee/pytorch-CAM](https://github.com/chaeyoung-lee/pytorch-CAM)]([https://github.com/chaeyoung-lee/pytorch-CAM](https://github.com/chaeyoung-lee/pytorch-CAM)) [58; 59; 60], accessed on 10 September 2022) highlight different object regions of interest. They are plotted from the final layer of the convolutional neural network [59]. They help us to know where the model focuses more to ascertain whether the model is actually learning the discriminating features or not. We compare this with relevant data augmentations, including CutOut, CutMix, and MixUp, to check whether RSMDA is really learning the discriminating features for two objects from their respective incomplete views. For this purpose, first, we take two images, i.e., the cat and dog shown in the first row of Figure 7. In the second row of Figure 7, we prepare the augmented output as an input for the model. Then, we use ResNet50 ([[https://pytorch.org/hub/nvidia_deeplearningexamples_resnet50/](https://pytorch.org/hub/nvidia_deeplearningexamples_resnet50/)]([https://pytorch.org/hub/nvidia_deeplearningexamples_resnet50/](https://pytorch.org/hub/nvidia_deeplearningexamples_resnet50/)), accessed on 10 September 2022), a pre-trained model, to obtain the CAM for each augmented input. In the third and fourth rows of Figure 7, only the CAM for the dog and the CAM with the dog are shown, respectively, to clearly show where the model focuses more.  
Figure 6: Comparison of DAs against different adversarial attacks for fashionMNIST dataset using different models.  
The same is repeated for the cat class in the fifth and sixth rows. RSMDA suggests that it learns features that are clear to the model, i.e., the tail of the dog where the model focuses more on dog classification. We believe such tiny features are quite helpful for models to recognize objects. Among these four augmentations, CutMix has a strong capability to capture features, as shown in the third column of Figure 7. From the experiments, it seems that RSMDA has the capability to learn tiny or small features that are quite helpful for models in recognizing objects.  
Figure 7: Data augmentation visualizations comparison.
## 2 State-of-the-Art
### Virtualisation of PLCs  
Current R&D work deals with the virtualization of complete PLC controllers and their outsourcing into the cloud. A scalable control platform for cyber-physical systems in industrial productions is researched and realized in [6]. In [7], a cloud-based controller is presented, which also uses a virtual control system in an Infrastructure as a Service (IaaS) cloud. The work of [8] also uses virtualized PLC controls in the cloud and connects these to OPC UA-based automation devices using web technologies.  
Problems with the virtualization of PLCs result especially from the fact that already available manufacturer-specific PLCs are virtualized. These controllers, however, are closed systems, which were originally not developed considering the aspects of web technologies. Adjustments, modifications or extensions of these controllers by third parties are hardly possible. Functionality cannot be resolved as services. The flexibility of virtualization is very limited.  
Reference [9] proposed a methodology for converting an automation plant managed by PLCs onto an ESFM control module (EFSM--extended finite state machine) that is driven by single board computers or SoC (system-on-a-chip). The ESFM Control Module can use IoT devices, but in that solution, the functionality (control program) cannot be resolved as a service from the cloud.  
In summary, it can be estimated that there are different solutions and efforts to equip PLC controllers with additional functions in order to be able to use the controllers in an Industry 4.0-type IP network. To this end, the known work already uses web technologies in part, in a manufacturer specific and/or limited way, and increasingly also tries to use the service principle and cloud structures as a new paradigm for the realization of control functions. However, there are still the following deficits that result in corresponding needs for research:  
* Although web technologies are used, a flexible distribution of the structure and function of the control functionality is not used. The information encapsulation of industrial control programs in local or virtualized devices (PLCs) is not called into question.
* For smart control services using cloud technologies as an essential feature of a future networked industry, systematic investigations, architectures, interfaces and demonstration solutions are lacking.
* Available standard technologies from the world of IP networks for increasing flexibility and efficiency are not or insufficiently used in the control level.
## 1 Introduction  
Donkey milk is a minor dairy production which is gaining growing interest and international acceptance mostly for human consumption, but also for the production of beauty products [1]. The current trends in the donkey industry in Europe show an increase of donkey breeds reared in Italy [2]. Moreover, the revaluation of equine milk for human consumption could contribute to the rural eco-sustainable development for the micro-economics of those areas threatened by marginalization [3]. Today, donkey milk is mainly marketed as raw, pasteurized, or freeze-dried [2,4]. Moreover, in the last few years, donkey milk has been proposed as an ingredient for the production of functional fermented beverages [5], and cheeseses [6].  
Due to its physico-chemical composition and nutritional quality similar to human milk, donkey milk is mainly used for feeding infants who suffer from a cows' milk protein allergy, or immunocompromisedelderly people [7; 8]. However, due to the small production and target market of the consumers, potential safety hazards, including the bacterial quality of raw donkey milk, should be subjected to stricter regulation [9]. Indeed, due to its high nutritional content, milk can support a rich microbiota, including beneficial, spoilage, and pathogenic microorganisms [10].  
Over the last few years, next-generation high-throughput sequencing (HTS) has been extensively used for the determination of microbial communities in milk and dairy products, in order to identify microorganisms that are difficult to culture or present at low concentration [11; 12]. However, few studies aimed to elucidate the composition of the microbiota of non-cow milk and the corresponding products [13]. To date, knowledge on the microbiota of donkey milk is limited to only a few studies, mainly addressed by culture-dependent tools [14; 15] and, only recently, by a metagenomic approach [16].  
It is well known that cold storage could encourage the growth of spoilage psychrotrophic bacteria with detrimental effects on the quality of raw milk and its processing [17; 18]. In the last few years, some studies evaluated the impact of refrigeration and storage on raw cow milk bacterial communities by using molecular tools [19; 20]. However, no information is available on the progress of the microbial population in unpasteurized donkey milk stored at refrigerated conditions, even if recent studies suggest the possible exploitation of unpasteurized donkey milk to produce cheeses [6].  
Therefore, in order to increase the knowledge of the microbial diversity associated with donkey milk, in this short communication we reported on the bacterial communities in fresh raw donkey milk, and of the same matrix after three days of cold storage.
# Physiological Study of Visual and Non-Visual Effects of Light Exposure
## 2 Measurements of Physiological Responses  
### Brain Activity  
Detecting the electrical and temporal changes associated with the electrical activity of neurons in the brain and the changes in blood in the cerebral blood vessels associated with the same activity can be used to evaluate brain activity. The former method includes electroencephalograms (EEGs) and magnetoencephalogram (MEGs). The latter method includes functional magnetic resonance imaging (fMRI) and near-infrared spectroscopy (NIRS) [22]. Compared to other brain activity devices, NIRS has three advantages. First, it has fewer physical restraints. Second, its relatively high temporal and spatial resolutions allow measurements focused on the prefrontal cortex activity that reflects arousal. Third, electromagnetic fields do not need to be managed, and the surrounding environment does not need to be limited. NIRS, which measures changes in oxygenated hemoglobin (\(\Delta\)oxyHb) concentration, has been used in many studies regarding light and color [18; 19; 23; 24].  
This study employed NIRS to measure brain activity. The NIRS device was an OEG-SpO2 (Spectratech, Tokyo, Japan) (Figure 1a). The sampling frequency of the device was 6.1 Hz. The device has six light emitters and six photodetectors. It can measure a total of 16 channels (Chs) corresponding to the prefrontal cortex (Figure 1b,c). The measured \(\Delta\)oxyHb signals were analyzed using four methods: (i) a digital filter, \(\leq\)0.1 Hz passband [25]; (ii) baseline correction [26]; (iii) the hemodynamic isolation method [27]; and (iv) standardization (calculating z-scores) [28].  
Figure 1: Specifications of the OEG-SpO2. (**a**) OEG-SpO2; (**b**) light source, detector, and measurement point; and (**c**) measurement points based on the international 10–20 system.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Direct LSSVM Localisation Method  
#### 2.6.1 Training and Estimation  
Ray-tracing produces multipath components (MPCs) for each BS-MS link or pair. For each MS position, all the BSs that can be seen by that MS, will produce the MPCs. The BS positions are known. The inputs of the regressor form a (\(N\) x 5) matrix whose five columns are: the BS x-coordinate, BS y-coordinate, the signal/ray's AOA at BS, logarithm of its time delay, and logarithm of its received power. The output sequences used for training, form a column vector with the x-coordinates or the y-coordinates of the MS depending on the coordinates being estimated at that point. Data-points (\(N\)) in this case therefore, refers to the total number of MPCs that are used, each having the above parameters.  
Training data points are created by randomly placing MSs within the coverage area of interest. For each BS-MS link, the first arriving paths are chosen. From these, those that are determined to be LOS are grouped separately to those determined to be NLOS. We determined in [2] that a training data size of at least 3000 data points was sufficient to produce high performing tuning parameters, but in this study, we use data points \(N\) = 10,000. These datapoints consist of half LOS and half NLOS MPCs. Training data was generated per each considered area and it is that training dataset, that is used for the LSSVM location estimation within that area.  
Training yields the regressor tuning parameters and constants, which are then used to estimate the coordinates of the MS for any new given data set. Training is done separately for the x and y coordinates using the appropriate output sequences. This approach means estimation of the MS position is O(2) as compared to the traditional regression for NLOS mitigation. It is however possible to just estimate, say, the y-coordinate and use it together with LOS information where available (via NLOS identification or otherwise), to calculate the x-coordinates for those positions that are determined to be in LOS as shown in Figure 4 below.  
Once the estimate of the y-coordinate is obtained, the x-coordinate can then be calculated as follows:  
\[x_{i}=\ y_{i}\cdot tan(\theta_{i}), \tag{3}\]  
where \(x_{i}\) is the x-coordinate corresponding to the y-coordinate \(y_{i}\), and \(\theta_{i}\) is the AOA. This approach is only suitable for LOS positions. After obtaining the estimates for both the x and y coordinates of the MS, the location error is calculated as in Equation (5). The methodology can be extended to 3D by incorporating the estimation of the elevation coordinate, \(z\), in a similar way. As illustrated in Section 2.1, the ray-tracer output data includes the elevation, or height of the MS. This data can be used to training and estimation. Training will have to be done separately for the \(z\) coordinate and the consequence will be increased computation.  
Figure 4: Obtaining the second coordinate for LOS scenarios [3].
In this paper, the communication between V2V and V2I is included in the model construction and the information transmission energy loss is quantified. With the deepening of research, research on RSU layout [12; 13; 14; 15; 16] under the influence of V2V and V2I has also come into being. [PERSON] et al. [11] proposed a connection-oriented RSU deployment scheme with maximum coverage to achieve a maximum performance of V2I communication in urban areas. The number of RSUs should be reduced to ensure communication performance while covering most vehicles on the road. [PERSON] et al. [17] proposed a multi-hop forwarding scheme between vehicles so that there is always a multi-hop forwarding path to the target vehicle through V2V communication in the uncovered area. The above studies assume that the proportion of intelligent vehicles is 100%; the layout mode is optimized by optimizing algorithms, establishing models, etc., without considering the layout adjustment during the infiltration process of intelligent vehicles from 0% to 100%, which is a lack of flexibility. Based on the above purpose, this paper proposes a revenue model of intelligent connected vehicles under different penetration rates, determines the deployment spacing under different traffic density and penetration rates, and fills the gap in this field. The innovation points of this paper are as follows. (1) By introducing permeability to reconstruct the traffic flow information flow coupling model and node energy loss model, the positive and negative balance model of layout income is constructed based on the assumed conditions and the basis for roadside RSU deployment under different modes is provided for intelligent high-speed sensor network builders. (2) MATLAB programming software is used to realize the visual output of the income model results under the changes in highway traffic density, the intelligent vehicle penetration rate, and roadside RSU deployment spacing; a set of matching income verification models is established. In addition, the model has a reliability of more than 93% which can provide a deployment basis for intelligent high-speed sensor network builders to deal with the layout adjustment after the infiltration of intelligent vehicles in the future and provide the optimal deployment spacing under different income models, which not only saves deployment costs but also ensures high connectivity between nodes so that the deployment cost ratio is optimized.
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 4 Results and Discussion
### Discussion
#### 4.2.4 Impact of PCA Dimensionality Reduction on Identifying UFZs  
We utilize the PCA algorithm to reduce the dimensionality of the 128-dimensional image features extracted by ST-CNN and combine them with POI semantic features. To determine the optimal dimension after PCA dimension reduction, we conducted a comparative analysis by reducing the image features to 1-127 dimensions and compared them with the original 128-dimensional features, as shown in Figure 12. From Figure 12, the cumulative variance contribution rate of the top five image features has exceeded 80%. As the image dimension increases, the OA and kappa coefficient of UFZs identification initially show a rapid increase followed by a slower decrease. Notably, when reducing the image features to 15 dimensions, UFZs recognition achieves the highest OA of 88.17%, and the kappa coefficient is 83.91%. When the image features are reduced to eight dimensions, the kappa coefficient of UFZs recognition is the highest, reaching 83.96%, and the OA is 88.07%. Considering that the difference in OA is greater than the difference in kappa coefficient, we choose 15 dimensions as the best dimension for dimensionality reduction.
## 3 Results and Discussion
### Analysis of Stress Characteristics
#### 3.2.2 Analysis of Stress in the Wingspan Direction
Figure 17 shows the dynamic changes in maximum stress during yaw dynamics:  
* At a tip speed ratio of 4, the stress of downwind deflection was greater than that of upwind deflection within one deflection period, except at \(-25^{\circ}\) and \(-5^{\circ}\) yaw angles. There was a significant difference in the stresses of downwind and upwind deflections  
Figure 13: Dimensionless chord length position. for a particular yaw angle, and the stress of upwind deflection was always greater than that of downwind deflection at other yaw angles. * At a tip speed ratio of 5, the downwind yaw stress was less than the headwind yaw stress at yaw angles of \(-20^{\circ}\) and \(15^{\circ}\), and the differences in stress at a given yaw angle within a yaw period were inconspicuous. * At a tip speed ratio of 6, the stress of downwind deflection was less than that of headwind deflection at yaw angles of \(-10^{\circ}\), \(10^{\circ}\), \(20^{\circ}\), and \(25^{\circ}\). At one yaw angle, downwind deflection stresses were greater than headwind deflection stresses, and the stress differential between downwind and headwind deflection stresses rose steadily. Figure 14: Time\(-\)domain diagram of 3D stresses at key positions of the blade. Figure 16: Stress fluctuations in the aerodynamic center lines of the blades. Figure 15: Stress distribution along the spanwise direction. The mean blade stress was higher at a positive yaw angle than at a negative yaw angle for the same yaw angle in the three working circumstances.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 6 Advantages and Disadvantages of Ohmic Heating
In contrast, some scientists reported disadvantages for ohmic such as a narrow band of frequencies [51]. The initial cost of establishing ohmic heating systems is higher than conventional heating methods. Food that contains fat granules does not effectively heat with ohmic heating because it is not conductive due to a lack of water and salts. In addition, pathogenic bacteria present inside the fat granules are exposed to heat treatment less than the bacteria outside the fat particles [36]. In addition, the electrical conductivity increases with increasing temperature inside the system (food and solution) as a result of an increase motion of electrons [36]. Another problem with applying ohmic heating is the corrosion of the electrodes due to electrochemical reactions. The electrochemical corrosion increases even more when the ohmic heating systems are in operation, thus increasing the cost [66].
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 4 5G Enhancement Techniques--Key Technologies, Advantages, Limitations, and Future Challenges
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**SC Techniques** & **Key Technologies** & **Benefits** & **Limitations** & **Challenges** & **References** \\ \hline Small cells & Femto Cells, Pico Cells, and Micro Cells & Enhanced capacity, enhanced throughput, enhanced coverage, and easy deployment & Reduce to \(\sim\) small cells infrastructure, expenditures, and resources range & Coverage radius, mobility and handovers, deployment and testing & [49, 50, 52, 53, 54, 56, 57] \\ Carrier aggregation & Intra-band contiguous, intra-band contiguous, non-contiguous, intra-band contiguous & Increased capacity, high data rates, improved load balancing, extended coverage, low latency non-contiguous & Battery life, coverage priority, proper filtering & Optimization algorithms to maximise the battery life, even \(\sim\) Architectures for the dynamic frequency bands, power amplifiers, filter design & [49, 60, 62, 64, 63, 64, 65, 66] \\ D2D & Overlay mode, underlay mode selection, device discovery & Enhanced capacity, spectrum sharing, interference management, inter-operability & Security and privacy, limited range, nulldown users & Initial device discovery, synchronisation, mode selection overhead, interference mitigation & [8, 82, 83, 86, 87, 90, 90, 91, 103, 102, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181] \\ NOMA & MIMO, massive MIMO, multi user MIMO & Energy efficiency, throughput, spectral efficiency, power consumption & System complexity, number of antennas, interference, power consumption & Signal detection, channel estimation, pilot contamination, energy efficiency & [170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181] \\ MIMO & MIMO, massive MIMO, multi user MIMO & Energy efficiency, throughput, spectral efficiency, power consumption & System complexity, number of antennas, interference, power consumption & Rayleighing the gap between AI and SC technologies with the use of AI and MI, algorithms, & [182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192] \\ SC optimization & AI algorithms, ML and R. Based algorithms & Enhanced coverage, low latency, high data rates & QoS, complexity & By-designing the gap between AI and SC technologies with the use of AI and MI, algorithms, & [182, 183, 184, 185, 186, 187, 188, 189, 191, 192] \\ \hline \end{tabular}
\end{table} 
Table 18: Summary of 5G coverage enhancement techniques-advantages, limitations, and future challenges.