esa-sceva/satcom-chunk-collection huggingface dataset:
# Upper Body Posture Recognition Using Inertial Sensors and Recurrent Neural Networks
## 2 Materials and Methods
### Machine Learning Algorithm  
For the machine learning algorithm, a recurrent neural network (RNN) was used as the architecture for the machine learning model. RNNs contain cyclic connections that make them more powerful in modeling sequence data when compared to feedforward neural networks. RNNs have been widely used in language modeling, objective detection, and speech recognition and have received increased attention in biomechanics. RNNs are often used to construct sequence-to-sequence models and have been applied to HAR tasks because of RNNs' competence in temporal domain processing [24; 33; 34; 35; 36]. RNNs can transition between hidden states of previous time steps to subsequent time steps, processing and passing down time-dependent sequences in chronological order. Thus, sequences obtained from time-dependent signals are suitable for RNN inputs. However, as a result of long-term dependency [37], RNN performance decreases as input sequencesincrease. In order to solve this problem, a long short-term memory (LSTM) cell was designed. An LSTM contains four units in its structure, including an input gate, an output gate, a memory cell, and a forget gate, and can decide whether input data can be stored in memory cells, effectively solving long-term dependencies that occur in RNNs [38; 39].  
A long short-term memory (LSTM)-based RNN was used to improve information extraction in the temporal domain (Figure 3). Two LSTM-based layers were present in the model structure [25; 32]. Each contained 128 LSTM units, with a rectified linear unit (ReLu) applied as an activation function. The experimental setup for the training procedure is listed in Table 1. Data were trained on GPU for 600 epochs by using an Adam optimizer and 10-fold cross validation to prevent overfitting. One epoch occurs when an entire dataset is passed forward and backward through the neural network only once. An Adam optimizer is an algorithm for first-order gradient-based optimization of stochastic objective functions. Cross validation is a technique for evaluating predictive models by partitioning original samples into a training set to train models and a test set to evaluate them. Raw data from IMU sensors were used as model inputs. The model output was one of seven classes representing different sitting postures.
## 4 Implementation and Operations
### Packet Format on a Sensing Field  
In TinyONet, several data packets are defined in order to communicate with physical sensors as shown in Figure 6. Each packet has ID, COMMAND and data fields. The ID field is two bytes-length field holding the unique identifier of an individual physical sensor. The COMMAND field has a single byte that represents packet types. There are five packet types; REGISTER, GET, SET, URGENT and BYPASS. The contents and length of a packet vary with the type of the COMMAND.  
The REGISTER and GET packets are transmitted from a sensing field. When a sensor is newly launched onto a sensing field, it will send a REGISTER packet to _SensorMapper_. When this packet is delivered to _SensorPool_ from _SensorMapper_, it will create an instance of _VirtualSensor_. A GET packet is sent when a sensor catches a new sensing data to report. This reported data in the GET packet will be stored into the corresponding _VirtualSensor_'s cache with a timestamp. The SET and URGENT packets are used to send commands from TinyONet to the sensing field. The SET data packet usually contains some commands for physical sensors. The basic communication model of TinyONet is based  
Figure 5: Message delivery through _InfoBus_.  
Figure 6: Structure of the data packet.  
on asynchronous reporting, but the synchronous access is also required to support the query-driven reporting model. For this purpose, the URGENT packet is used and this is presented in section 4.8. The BYPASS packet is a special bidirectional packet that is not processed within TinyONet. When some sensor applications need to use the node-centric communication, they can use the BYPASS packet to deliver their proprietary data to the sensors and vice versa. A detailed procedure for BYPASS and URGENT packets is described in section 4.8.
# Micro Vibration Measurement with Microscopic Speckle Interferometry Based on Orthogonal Phase
## 4 Experiment  
The schematic diagram of the microscopic speckle interference vibration measurement system is shown in Figure 4. The light source is a single longitudinal mode laser with a wavelength of 532 nm, and the output power is 100 mW. The CCD we use is 640 \(\times\) 480 spatial resolution at low frame rate and 160 \(\times\) 120 spatial resolution at the highest frame rate of 400 frames/s. The exposure time of the CCD is 300 microseconds. The pixel size of the CCD is 4.8 um \(\times\) 4.8 um. The objective lens numerical aperture is 0.25 and the magnification is 10. The vibration signal of the measured object is introduced by the displacement platform produced by German PI company, and the accuracy of the vibration signal is higher than 0.1 nm.  
During measurement, the laser beam is split into measuring beam and reference beam by beam splitter. The measuring beam is focused on the measured object through the microscope objective and then reflected back to the beam splitter. Similarly, the reference beam is reflected by the reference object. The two beams will interfere after passing through the beam splitter, and then are detected by the CCD. In order to ensure the continuity of the light intensity curve, we need to ensure that there are at least 20 collection points for each vibration period. Therefore, the upper limit of the vibration frequency can be measured is 20 Hz corresponding to 400 frames/s. The measured frequency range depends on the camera acquisition frequency. If a camera with a high-speed acquisition frame rate is used,  
Figure 4: The schematic illustration of the experimental setup.  
the measurement frequency range can be further expanded. The optical path structure diagram is shown in Figure 5.
## 4 A Typical Application: Wuhan Huoshenshan Hospital
### Historical Wind Data for Wuhan City  
The weather data website ([[https://gis.ncdc.noaa.gov/maps/ncei/cdo/hourly](https://gis.ncdc.noaa.gov/maps/ncei/cdo/hourly)]([https://gis.ncdc.noaa.gov/maps/ncei/cdo/hourly](https://gis.ncdc.noaa.gov/maps/ncei/cdo/hourly))) administrated by the National Oceanic and Atmospheric Administration (NOAA) provides users with free global wind data recorded at hourly intervals. In this study, the hourly wind speed data for Wuhan from 1 January 2019, to 31 March 2019, was used as the representative wind condition for Wuhan during the service period of the Huoshenshan Hospital. The distribution of wind speed data is shown in Figure 14. The median value is 1.90 m/s, and the proportion of wind speed not exceeding 5 m/s reaches 97%. Therefore, in the subsequent analysis of cases with different wind speeds, the wind speed at the height of 10 m was taken as 0.5, 1.9, 3.5, and 5.0 m/s, respectively, to cover the wind speed range to the maximum extent. For ease of expression, the cases with different wind speeds and directions are correspondingly named as Case-S-D hereinafter, where S represents the wind speed (i.e., 0.5, 1.9, 3.5, and 5.0 m/s) while D represents the wind direction (i.e., N, NE, E, SE, S, SW, W, and NW). According to Tables 10 and 11 in Appendix B, the aerodynamic roughness length, \(z_{0}\), and the Obukhov length, \(L\), were taken as 0.1 m and 350 m, respectively, to define the mean wind speed profile in the FDS.  
Figure 14: Distribution of the hourly wind speed data of Wuhan from 1 January 2019, to 31 March 2019.
# Mapping of the Upper Limb Work-Space: Benchmarking Four Wrist Smoothness Metrics
###### Abstract  
Smoothness is a commonly used measure of motion control. Physiological motion is characterized by high smoothness in the upper limb workspace. Moreover, there is evidence that smoothness-based models describe effectively skilled motion planning. Typical smoothness measures are based on wrist kinematics. Despite smoothness being often used as a measure of motor control and to evaluate clinical pathologies, so far, a smoothness map is not available for the whole workspace of the upper limb. In this work, we provide a map of the upper limb workspace comparing four smoothness metrics: the normalized jerk, the speed metric, the spectral arc length, and the number of speed peaks. Fifteen subjects were enrolled, performing several reaching movements in the upper limb workspace in multiple directions in five planes (frontal, left, right, horizontal and up). Smoothness of the wrist of each movement was computed and a 3D workspace map was reconstructed. The four smoothness metrics were in general accordance. Lower smoothness was found in the less dexterous sectors (up and left sectors), with respect to the frontal, horizontal, and right sectors. The number of speed peaks, frequently used for evaluating motion in neurological diseases, was instead not suitable for assessing movements of healthy subjects. Lastly, strong correlation was found especially between the normalized jerk and speed metric. These results can be used as a benchmark for motor control studies in various fields as well as clinical studies.  
smoothness; dexterity; upper limb; workspace; wrist +
Footnote †: journal: Applied Sciences  
1
Footnote 1: Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA),  
Italian Council of National Research (CNR), 20133 Milan, Italy1  
Footnote 2: Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO Valais),  
3960 Sierre, Switzerland2
## 4 Results
### Cross-Case Analysis  
As mentioned, the data collected from the interviews regarding the relationship between warehouse features quality and performance indicators were coded according to the terminology of the Kano model (R, I, M, O, A). Results are displayed in Table 5.  
While Table 5 shows all of the sample data, it is possible to offer a set of specific views related to the different features of the 3 PLs included in the sample.  
Considering differentiating features of the sample companies, such as size, no difference seems to emerge in the given ratings in most cases. Here, even if 3 PLs hosted different clients of different industries that are different in terms of flow and peculiarities (e.g., regulation, security needs, fragile products, etc.), it is possible to see that the interviews report similar ratings of the features and indicators.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Warehouse Feature: Location**} \\ \hline
**Warehouse Performance Indicators** & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** & **Case 7** & **Case 8** \\ \hline Delivery lead time & A & O & R & O & O & R & A & O \\ On-time delivery & A & M & O & A & M & R & O & O \\ Flexibility & A & I & I & I & M & R & M & O \\ Responsiveness & A & O & M & I & I & R & O & A \\ Completeness of the logistics service & A & I & I & R & R & R & I & I \\ Labour productivity & O & O & M & A & O & A & O & I \\ Order processing cost & M & O & R & R & I & R & R & I \\ Warehouse utilisation & I & R & M & R & R & A & R & M \\ Logistics building adaptability & R & O & R & O & M & O & I & O \\ \multicolumn{6}{c}{Warehouse Feature: External Spaces} \\ \multicolumn{6}{c}{Warehouse Performance Indicators} & Case 1 & Case 2 & Case 3 & Case 4 & Case 5 & Case 6 & Case 7 & Case 8 \\ Delivery lead time & I & I & I & R & I & R & M & M \\ On-time delivery & I & M & R & A & R & R & M & M \\ Flexibility & M & M & I & M & R & R & M & O \\ Responsiveness & I & I & R & R & R & R & M & M \\ Completeness of the logistics service & M & I & R & I & R & R & M & M \\ Labour productivity & M & R & R & R & I & R & O & O \\ Order processing cost & M & R & M & R & I & O & I & O \\ Warehouse utilisation & I & R & M & R & O & A & I & O \\ Logistics building adaptability & O & O & I & I & M & O & M & A \\ \hline \hline \end{tabular}
\end{table}
Table 5: Result of the rating of the relationship between warehouse features’ quality and performance indicators, using Kano model (R = reverse; I = Indifferent; M = Must-be; O = One-dimensional; A = Attractive).  
On the other hand, interesting differences appear when the perspectives of 3 PLs that offer warehousing and transportation services directly versus 3 PLs offering only direct warehousing services and 3 PLs renting or owning the warehouse under analysis are considered. The differences are discussed in the following paragraphs.
# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
## 3 Literature Review
Despite the positive institutional and regulatory milestones, the RIA Botswana ICT report [19] found that Botswana's ICT readiness ranking deteriorated to the worst levels. The ICT report attributes the national broadband project's deterioration to the universal service obligations (USO), weak tariff regulations, poor quality of service (QoS) and other value-added network services, dimensions to high termination costs, and skewed market [19]. In addition, the ICT report foundthat only 9% of their study respondents used the internet and resided in urban areas, while the rest of the country experienced a lack of internet connectivity access [19]. Orange Botswana reported that the deployment of WiMAX IEEE 802.16e significantly improved the QoS and affordability of the internet connections in Francistown and Gaborone [20]. Although the WiMAX standard body initially developed technology for metro and high-density areas access, it has successfully demonstrated connectivity and access for rural areas with less infrastructure deployment [17].
## 1 Introduction
The continuous tuning range is generally limited to a few nanometers. However, the capability of continuously shifting the wavelength is most important for sensing applications, since it allows for precise scanning of molecule absorption lines. Figure 5 shows two examples of wavelength ranges accessible with such multi-segment DFB BSG lasers. The left-hand picture displays a more than 80 nm wide discrete tuning range in the 1.4 \(\upmu\)m region, thus effectively enabling the substitution of five conventional DFB LD with one DFB BSG device. The right-hand picture shows superimposed emission spectra of a BSG LD covering a 13 nm range with 25 different wavelength channels. To improve the selectivity of each mode even further, four segment lasers can be realized (see Figure 6). Two additional current controlled segments without gratings are used to enable phase control and allow for a stable tuning of the wavelength channels by ensuring spectral coinciding of BSG modes and FP modes in the resonator. The overall potential of the concept becomes clear when considering that in general there is no restriction for the number of channels incorporated in one BSG  
Figure 4: Mode combs of two coupled BSG segments. Lasing wavelength channels can be selected by current tuning of the mode combs and changing the position of maximum overlap. Figure 5: Superimposed emission spectra of BSG LD showing a discrete tuning range of more than 80 nm (left-hand) and a 0.5 nm grid covering 13 nm (right-hand). grating, nor for their positions and their spacing. Widely tunable monomode sensing devices covering wavelength ranges of 80 nm (at 1.3 \(\upmu\)m) allowing reproducible DFB-like tuning on application-specific wavelength channels with excellent SMSRs are thus attainable. This tuning range exceeds the possibilities of ordinary DFB lasers, which typically show tuning ranges of less than 5 nm, by far. Thus widely tunable DFB BSG devices are suited excellently for multi component gas analysis since they cover a wide wavelength range which usually contains several absorption lines of the gases of interest. Therefore only one tunable device may replace several conventional DFB lasers, resulting in significantly reduced costs and compact assembly components. Due to their improved properties in respect to conventional tunable DBR lasers [38-41], these devices represent a convenient as well as highly accurate possibility for multi-component gas analysis. The presented concept of coupled segments involving lateral metal BSG gratings can be applied to virtually every semiconductor material [37], so far enabling devices with emission wavelengths between 760 nm and 3.0 \(\upmu\)m.
# Error-Correction Coding Using Polynomial Residue Number System
## 2 Modular Codes  
Modular codes belong to the class of arithmetical codes. The name of these codes is associated with the rules of their construction: arithmetical operations are performed modulo the bases of the code. As mentioned above, modular codes are of two types, and we will consider both of them here.
## 4 Discussions
### Limitations of the Present Approach  
Although the conversion methodology and the analytical solutions based on Timoshenko beam theory presented in this study could give very accurate results for elastic mechanical properties of open-cell unit cells and lattice structure, they are still based on linear elastic deformation of structures. Therefore, the analytical relationships are valid only for small deformations (i.e. in the elastic range). The analytical relationships presented in this study could be beneficial for several applications where the deformations remain in the elastic regime, such as in biomedical implants. In some applications of lattice structures and porous materials such as energy absorption and actuation, the matrix material undergoes plastic or hyperelastic deformation, and hence the lattice structures behave non-linearly. Therefore, the range of strains and whether or not the lattice structure's behaviour is linear is one of the most important factors that should be considered before utilization of the analytical relationships presented in this study.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 3 Results
### Experimental Parameters  
Our algorithm comes with some parameters that can affect the classification performance, such as the number of hidden layers and nodes in the SSAE, and the number of superpixels and training pixels. Specifically, the number of hidden layers and nodes in each layer can influence the performance of the SSAE. Moreover, superpixels divided from the Pauli RGB image can affect the classification results. A smaller number of superpixels implies that a superpixel contains more pixels and therefore more contextual information of the neighborhood can be considered. However, the classification of boundaries becomes poor when the number of superpixels is too small. Furthermore, the number of training pixels also influences the estimation accuracy. In order to analyze the impact of these parameters, experiments are carried out with the Qinghe dataset.  
To determine the optimal number of layers, SSAE topologies with increasing depth were trained with the same number of superpixels and training pixels. In this experiment, the SIR-C image was divided into 2200 superpixels, and 10% of labeled pixels were randomly selected as the training set to train the network. The overall accuracy (OA), which is defined in Equation (24), is calculated to compare different architectures. From the plot shown in Figure 5, it can be seen that a three-layer SSAE is the most suitable for the dataset. Furthermore, the network has 60, 80,and 100 nodes in these hidden layers. From the above mentioned results, a three-layer SSAE with 60, 80, and 100 nodes was applied in the following experiments. The OA is:  
\[OA=\frac{\sum\limits_{i=1}^{r}x_{ii}}{N}, \tag{24}\]  
where \(N\) is the number of total samples, \(r\) denotes the number of classes, and \(x_{ii}\) is the diagonal elements in the confusion matrix.  
Then, the optimal number of superpixels obtained from the SIR-C image can be determined by following experiments. In this step, the SIR-C image is divided into 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000, and 3200 superpixels, and then classification performance is analyzed to find the optimal number of superpixels. As shown in Figure 6, 1800 superpixels is the favorable parameter.  
Finally, we validate the performance of our network architecture with different numbers of training pixels. We respectively choose 0.1%, 0.2%, 0.5%, 1%, 2%, 3%, 5%, 10%, 15%, and 20% of labeled pixels as the training set to train the network. From Figure 7, we find that, when the rate of training samples increases from 0.1% to 5%, the OA of classification increases sharply at first, and then  
Figure 4: Ground truth map.  
a stable and high OA is obtained. 5% of the labeled pixels is enough to train the network, and it can prevent overfitting. Thus, as shown in Table 2, 5% of the labeled pixels are randomly selected as a training dataset, and the remainder are regarded as the testing dataset.  
Figure 5: Changing of the overall accuracy (OA) with the number of layers.  
Figure 6: Changing of the OA with the number of superpixels.
# Heat-Resistant Behavior of PLA/PMMA Transparent Blends Induced by Nucleating Agents
## 3 Results and Discussion
### Crystal Structure  
XRD was employed to analyze the blends after isothermal crystallization to further investigate the impact of the PMMA and nucleating agent on the PLA crystal structure and grain size. The PLA crystals primarily exhibited four crystal forms: \(\alpha\), \(\alpha^{\prime}\), \(\beta\), and \(\gamma\). The \(\alpha\) crystal form was the most prevalent and stable, with corresponding WAXD 2\(\theta\) peaks at 14.8\({}^{\circ}\), 16.9\({}^{\circ}\), 19.1\({}^{\circ}\), and 22.5\({}^{\circ}\)[25]. As shown in Figure 5, prominent characteristic PLA crystal diffraction peaks were observed around 2\(\theta\) = 16.6\({}^{\circ}\) and 18.9\({}^{\circ}\), corresponding to the (200)/(110) and (203) crystal planes of the \(\alpha\) crystal. The PLA crystals obtained via crystallization at 90 \({}^{\circ}\)C and 100 \({}^{\circ}\)C exhibited two relatively weaker characteristic diffraction peaks around 2\(\theta\) = 14.9\({}^{\circ}\) and 22.2\({}^{\circ}\), corresponding to the (103), (010), and (015) crystal planes of the \(\alpha\) crystal. Notably, all the blends exhibited \(\alpha\)-crystalline structures, indicating that adding the nucleating agent and the PLA/PMMA simultaneously did not alter the PLA crystal form. Additionally, when the blends were crystallized at different temperatures, the diffraction peak intensity of the PLA/PMMA blends was lower than the samples without PMMA.  
The calculation results are shown in Table 4. Adding a nucleating agent to the PLA affected the grain size differently, with a decreasing trend in most cases since the nucleating agent acted heterogeneously, particularly with TMB-5. The grain size decreased significantly while the crystallinity of the system was retained. Furthermore, PMMA addition distinctly decreased the grain size, with the most pronounced effect observed at 80 \({}^{\circ}\)C. The mobility of PLA molecular chains was improved in the PLA blends following isothermal crystallization treatment, making PLA more susceptible to crystallization and resulting in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{2}{c}{\(n\)} & \multicolumn{2}{c}{**k**} & \multicolumn{2}{c}{**t\({}_{\text{1/2}}\) (min)**} \\ \cline{2-7}  & **90 \({}^{\circ}\)C** & **100 \({}^{\circ}\)C** & **90 \({}^{\circ}\)C** & **100 \({}^{\circ}\)C** & **90 \({}^{\circ}\)C** & **100 \({}^{\circ}\)C** \\ \hline PLA & 2.2 & 2.2 & 1.64 \(\times\) 10\({}^{-2}\) & 1.18 \(\times\) 10\({}^{-1}\) & 5.48 & 2.24 \\ PLAincreased crystallinity with smaller grains. Notably, at 80 \({}^{\circ}\)C, the grain sizes of the PMMA blend system were smaller than those at 90 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, which could be attributed to the slower PLA crystallization time and incomplete growth at these temperatures.