esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Level Joint Feature Learning for Person Re-Identification
## 4 Results
### Effectiveness of PMN  
We evaluate the MFF model compared to three classic datasets: Market1501, CUHK03 and DukeMTMC-reID. PMN is proposed to extract local features from the low-to-high level layers. In order to further explore the influence of the PMN model, we conduct two experiments on each dataset. Firstly, we remove the structure of the PMN model. We fuse local features and global features extracted from entire backbone network. GLB is the structure without the PMN model, as in Figure 8. Experiments on GLB can clearly test the performance of our model without adding the PMN structure. Then we train the MFF model on three datasets and report their performance in Figure 8. Difference between MFF and GLB is that MFF fuses low-to-high level local features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**DukeMTMC-reID**} \\ \cline{2-3}  & **Rank-1** & **mAP** \\ \hline BoW + KISSME [33] & 25.1 & 12.2 \\ LOMO + KISSME [6] & 30.8 & 17.0 \\ Verif + Identif [46] & 68.9 & 49.3 \\ ACRN [47] & 72.6 & 52.0 \\ PAN [41] & 71.6 & 51.5 \\ SVDNet [34] & 76.7 & 56.8 \\ DPFL [43] & 79.2 & 60.6 \\ HA-CNN [25] & 80.5 & 63.8 \\ Deep-Person [48] & 80.9 & 64.8 \\ PCB+RPP [11] & 83.3 & 69.2 \\ \hline MFF (Ours) & 86.0 & 76.1 \\ \hline \hline \end{tabular} Rank-1 accuracy (%) and mAP (%) are compared above.  
\end{table}
Table 6: Comparison with existing methods on DukeMTMC-reID.  
Figure 7: One example query image in DukeMTMC-reID dataset and ranking list results from Rank-1 to Rank-10 using MFF model. The blue boundary means true positive and red means false positive.  
Figure 8: _Cont._  
We exhaustively train MFF and GLB on three datasets separately and use Rank-1 accuracy to Rank-10 accuracy as the evaluation standard. In Figure 8, a comparison of experimental results of two models not only shows the effect of model enhancement after fusing low-to-high level local features, but also shows that the improvement effect of PMN on each dataset is different. PMN structure has the most significant effect on CUHK03 especially on CUHK03-labeled data. But the effect on Market-1501 is less significant. Figure 8 shows that rank accuracy of MFF is higher than GLB on three datasets, which proves that low-to-high local features extracted by PMN structure have a positive impact on person re-identification.
## 3 Results and Discussion
### Acoustic-Assisted UCS Experiments
Besides studying the elastic properties of the different rock types present at the Kizildere geothermal site, acoustic measurements were conducted, over the course of each UCS experiment, in order to study the effect of \(\sigma_{1}\) on the seismic velocities whilst \(\sigma_{2}\) equalled atmospheric pressure. Figure 5 presents the compressional wave (P-wave) velocity as a function of \(\sigma_{1}\), i.e., depth below surface. The shear wave (S-wave) velocity as a function of \(\sigma_{1}\) is shown in the Supplementary Materials (Figure S2). It is evident from Figure 5 that the soft siltstones reveal significantly lower seismic velocities compared to the other rock types investigated. Since the siltstones underwent a relatively high degree of axial deformation, as previously discussed, compaction within the siltstones occurred, which led directly to an increased seismic velocity as a function of increasing \(\sigma_{1}\). For all types of porous media studied, the largest increase in velocity took place at the lowest absolute stress values. This is most likely the result of closure of potential microcracks within the core samples, yielding increased grain-to-grain (or mineral-to-mineral) contact areas, eventually resulting in an increased velocity. Besides the earlier arrival times of the source signal, i.e., higher velocities, the increased contact areas also yield higher amplitude magnitudes (Figure S3 in the Supplementary Materials). Lmestone sample TS2-SZL2 (continuous black line in bottom graph of Figure 5) shows a somewhat different trend compared to the other samples studied: the P-wave velocity remained fairly constant as a function of increasing \(\sigma_{1}\). The reason for this is the stiff behaviour of this particular sample (Young Modulus of 52.9 GPa, Table 8): the sample faced relatively little axial deformation with increasing \(\sigma_{1}\), yielding negligible amounts of compaction, hence a roughly constant velocity profile.
# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 3 Artificial Neural Networks  
Artificial neurons that function similarly to our brain's neurons make up the basis of deep learning. A perceptron or artificial neuron simulates a neuron with a set of inputs that each has a particular weight. The neuron computes some function and produces an output based on these weighted inputs. The neuron receives n inputs (one for each feature). Following that, it sums the inputs, applies a transformation (activation), and generates the output (see Figure 1) [1].  
Figure 1: A representation of an artificial neuron [1].  
A particular input's weight indicates how effective it is. Inputs with higher weights will have greater impacts on the neural network. The bias parameter for perceptrons is used to adjust the output of the neural network according to their input weights. It allows the model to fit the data in the best possible way. An activation function converts inputs into outputs. An output is produced by using a threshold. The following functions may be used as activation functions: linear or identity; unit or binary step; sigmoid or logistic; tanh, ReLU, or softmax. Several neurons are used to solve problems, since multiple inputs cannot be processed by a single neuron. A neural network is composed of perceptrons, interconnected in various ways and operating on different activation functions, as shown in Figure 2[1].  
A deep learning model is a neural network with more than two layers. The layers between input and output layers are called hidden layers. These layers can improve the accuracy. Neural networks can mimic the human brain, although they are still not close to the brain's processing capabilities. Also note that neural networks learn from massive datasets.  
Deep learning is among the fastest-growing areas in computational science, employing complex multilayered networks to model high level patterns in data. Most often, it is used in machine learning and artificial intelligence, and many major companies, such as Google and Microsoft, rely on it to manage critical problems, such as speech recognition, image recognition, 3D object recognition, and natural language processing. Some well-known deep learning algorithms are discussed in this section, such as backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 3 Design of Controllers
### PI Controller  
The model of the PI speed controller in the frequency domain is given by  
\[\mathrm{G(s)}=\mathrm{K_{p}}+\frac{\mathrm{K_{i}}}{\mathrm{s}} \tag{5}\]  
where the transfer function of the controller is given by G(s), the proportional gain constant is denoted by \(\mathrm{K_{p}}\), and the integral gain constant is denoted by \(\mathrm{K_{i}}\). The Ziegler-Nichols method [32] is used for the tuning of these parameters to ensure stability. The transient behavior of the speed response gives the time domain specifications, such as peak time (\(\mathrm{T_{p}}\)); percentage overshoots (\(\mathrm{M_{p}}\)), settling time (\(\mathrm{T_{s}}\)), and rise time (\(\mathrm{T_{r}}\)). The closed-loop second-order transfer function for the proposed five-phase PMBLDC motor with the PI controller is given as,  
\[\mathrm{(s)}=\frac{\frac{\mathrm{K_{p}s+K_{i}}}{\mathrm{s}^{2}+\mathrm{J} \mathrm{s}+\mathrm{K_{p}}}}{\mathrm{s}^{2}+\mathrm{J}\mathrm{s}+\mathrm{J} }} \tag{6}\]  
where T(s) is the closed-loop transfer function of the entire system, J denotes moment of inertia, and B denotes coefficient of friction of the PMBLDC motor. Comparing the characteristic Equation (6) with that of a standard second-order system characteristic, we obtain Equation (7) providing the gain constants as,  
\[\mathrm{Kp}=2\xi\omega\mathrm{n}\mathrm{J}\mathrm{-B}\mathrm{;}\mathrm{ Ki}=\mathrm{J}\omega\mathrm{n}2 \tag{7}\]
## 3 Fundamentals
### The Influence of the Degree of Dispersing of the AAC in the Materials
#### 3.3.2 Factor #3: Molecular Weight of a Surfactant  
The subject of research was a building mortar based on a hybrid AAC with \(\mathrm{M_{b}}=1.2\) (Mix 4) (reference composition, see Table 1). The criteria for evaluating the effectiveness of a surfactant was the consistency (flow) of fresh mortar and the compressive strength of mortar.  
The higher degree of dispersing of the AAC suspensions (higher content of water) in the transition from heavyweight concretes to building mortars was accompanied by the lesser role played by molecular weight in ensuring the surface activity of a surfactant. For example, a plasticizing action of a surfactant (Figure 10) in the mortar mixes for floor application based on AAC with \(\mathrm{M_{b}}=1.2\) (Mix 10) increased with the decrease in molecular weight. As a result, there was no need to use NaLST in conjunction with high- or low-molecular-weight surfactants.  
The subject of research was a lightened grout based on a hybrid AAC with \(\mathrm{M_{b}}=1.1\) (Mix 11) (reference composition). The criteria for evaluating the effectiveness of a surfactant were the plastic viscosity, as well as the dynamic and static shear stresses.  
Low molecular weight aliphatic surfactants are the most effective for lightened grouts which are rich-in-water AAC (\(\mathrm{M_{b}}=1.1\)) suspensions with a maximal degree of dispersing.  
Figure 10: The consistency (flow) of mortar mixes based on the AAC with \(\mathrm{M_{b}}=1.2\) (Mix 4) during the time after mixing (0 min—light blue, 20 min—brown, 30 min—pink, 50 min—violet), and the compressive strength of mortars after hardening for 1 (blue), 7 (red), and 28 days (green) vs. the type of surfactant.  
The addition of polyol with a maximum number of hydroxyl groups in its molecule (sorbitol) had the greatest effect on plastic viscosity (Figure 11a), dynamic shear stress (Figure 11b), and static shear stress (Figure 12). The higher action effect of sorbitol on rheological characteristics, compared to those of glycerol and ethylene glycol, is attributed to its action, which is not dependent upon temperature.  
The action effect of low-molecular-weight aliphatic surfactants to control rheological properties of the AAC suspensions with high degrees of dispersing, for example, in the case of lightened grouts, within a wide temperature range of 22-75 \({}^{\circ}\)C, is shown. These surfactants act like stabilizing, plasticizing, and structure retarding admixtures. The action effect of polyols becomes greater with the increase in the quantity of hydroxyl groups in a molecule, indicating better hydrophilic properties as a factor of surface activity.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 4 Discussion
### Uncertainty  
The study utilized NDVI and SPEI at different timescales to determine and examine the expansion or contraction trend of the VWD and vegetation surplus in the Loess Plateau. Additionally, it analyzed the response characteristics and sensitivity changes of vegetation to SPEI at different timescales. The Thornthwaite method was used to calculate PET (potential evapotranspiration). The calculation accuracy of PET will affect the calculation results of SPEI; thus, it is an uncertain point. Regarding the analysis, the period of the study was restricted to only 21 years due to the limitations of time and the resolution of the data, so the sliding window of 5 years was employed for calculation and analysis. this period is insufficient for verifying the robustness of the sliding window analysis results over larger sliding window periods, such as 10 years and 15 years, nor is it long enough to analyze the periodic changes of VWD. In future studies, the research timescale can be extended, and the data resolution can be improved using multi-source remote sensing data. Moreover, multiple analysis windows of different sizes can be studied to validate one another and to generate more robust findings.
## 4 Artificial Intelligence (AI)-Enabled 6G Networks
### Traffic Anomaly Detection  
Network traffic anomalies refer to unusual changes in the traffic such as a transient change in users' requests, port scans, and flash crowds. The detection of such anomalies is important for the security of the network and reliable services. DL approaches have recently gained popularity for traffic anomaly detection in complex, dynamic, and heterogeneous wireless networks (Figure 12). [PERSON] and [PERSON] [69] proposed a C-LSTM neural network to model the spatial-temporal traffic data information and to detect an anomaly. The CNN layer in the model reduces the variation in the information, the LSTM layer models the temporal information, and the DNN layer is used to map the data onto a separable space. The tanh activation function is employed in all the layers except the LSTM output layer, which uses softmax activation. [PERSON] et al. [70] evaluated the performance of ML and DL models for anomaly detection. The authors implemented extreme learning machine, nearest neighbor, decision tree, random forest, SVM, Naive Bayes, quadratic discriminant analysis, Multilayer Perceptron (MLP), LSTM, RNN, AE, and CNN models. The results showed that DCNN and LSTM detect anomalies with the highest accuracy.
## 2 Algorithm Principle
### INS + LEO2 Alternate Switching + Altimeter Integrated Navigation Algorithm  
#### 2.4.1 Algorithm Description  
According to the minimum geometric dilution precision (GDOP) satellite selection algorithm [6; 29], two LEO satellites whose positions are visible at any time during the flight of the moving carrier are selected, and we temporarily name them LEO-1 and LEO-2. Similarly, we assume that the ECEF coordinates of the moving carrier measured by INS extrapolation are \((\hat{x}_{c},\hat{y}_{c},\hat{z}_{c})\). \(\hat{\rho}_{1}\) and \(\hat{\rho}_{2}\) represent the measurements between the satellite position calculated by satellite 1 and satellite 2 through the ephemeris to the moving carrier position measured by INS extrapolation in real time, respectively, since they are not actual measurements, so we call them virtual ranging values [20]. Subsequently, the two satellites alternately switch periodically with the real ranging value and the virtual ranging value, and the switching period is \(\Delta t\). The schematic diagram is shown in Figure 3.
# Isoprenoids Production from Lipid-Extracted Microalgal Biomass Residues Using Engineered E. coli
## 2 Results and Discussion
### Inhibitor Changes with Different Detoxification Methods  
The acidosis reaction could not only converts the lignocellulosic into fermentable monomeric sugar, but some by-products (weak acid, furfural and 5-HMF, etc.) were also formed simultaneously. These by-products could inhibit the microorganism from producing high value-added products [16]. Due to absence of lignin in the LMBRs, only three main inhibitors including acetic acid, furfural and 5-HMF, would be produced during the acidosis process. Acetic acid is formed primarily by hydrolysis of acetate groups of the acetate hemicellulose. While furfural and 5-HMF are derived from pentoses and hexoses, respectively [17].  
To date, many of investigations have shown that neutralization, overlining, activated charcoal, ion exchange resin and reducing agents all have the ability to remove the inhibitors from the acidosis hydrolysate [6; 7; 8; 9; 10]. In the present study, we adopted five different kinds of detoxification strategies to explore the optimal detoxification method and analyzed the changes in the concentration of three types of inhibitors (acetic acid, furfural and 5-HMF). As is shown in Figure 3, compared with the raw hydrolysate, the concentration of all three types of inhibitors was reduced more or less in five different detoxification hydrolyzates.  
Figure 2: Effects of acid hydrolysis conditions on the glucose concentration and yield. (**A**) Effect of ratio of solid to acid on acid hydrolysis efficiency; (**B**) Effect of concentration of sulfuric acid on acid hydrolysis efficiency; (**C**) Effect of temperature on acid hydrolysis efficiency; (**D**) Effect of reaction time on acid hydrolysis efficiency. The experiment was performed in triplicate.  
Though methods A and B could decrease the acetic acid volume by more than half, the concentration of the furfural and 5-HMF inhibitors still remains too high. The hydrolysates after detoxification by methods C and D have a lower 5-HMF concentration, however, the acetic acid of the two hydrolysates was barely removed. Interestingly, in the detoxification hydrolysate using method E method, the acetic acid, furfural and 5-HMF were reduced about 55.32%, 99.19% and 98.22%, respectively. It showed that E detoxification method was the most efficient in the simultaneous removal of the three abovementioned inhibitors simultaneously. Meanwhile, compared with other detoxification methods, E method also presented a series of advantages such as lower cost, and easier operation in addition to the better capacity to remove inhibitory compounds. As a result, when cost, removal efficiency and difficulty of the detoxification process are all taken into consideration, method E has the highest potential to be used in the future industrialization field.
## 5 The Cryptographic Suite
### Integrity  
Encryption without authentication is insecure [24]. For example, an adversary may flip bits in unauthenticated ciphertext and cause predictable changes in the plaintext that receivers are not able to detect. To address this vulnerability, the system always authenticates messages. Security of hash functions is directly related to the length of the digest. However, as a digest is appended to the message, it becomes another source of message expansion and consequent communication overhead. For instance, the size of a digest produced by SHA-256 is 256 bits, which causes an additional overhead that amounts to around 35% of an average UAN message.  
UAN features a trade-off between security and performance by using 4 bytes digests resulting from truncating the real hash function value. Using, such a short hash size, the overhead becomes around 4.4% of the UAN average message, which is almost negligible. Furthermore, using such a short hash function value is not detrimental to security [28]. An adversary has 1 in \(2^{32}\) chances to blindly forge a digest. If an adversary repeatedly tries to forge it, he/she needs at maximum \(2^{31}\) trials. However, the adversary cannot perform trials off-line. This means that the adversary has to validate a given forgery only by sending it to an authorized receiver. This implies that the adversary has to send \(2^{31}\) messages in order to successfully forge a single malicious message. In a conventional network, this number of trials is not large enough. However, in an underwater acoustic network this may provide an adequate level of security. An adversary can try to flood the network with forgeries, but on a 500 bps channel with 184-bitmessages, he/she can only send about 2.71 attempts for second. Thus, sending \(2^{31}\) messages requires around 306 months, _i.e._, about 25 years. Battery-operated vehicles have not enough energy to receive that many messages. Furthermore, the integrity attack would translate into a denial of service attack since the adversary needs to occupy the acoustic channel for a long time. Fortunately, it is feasible to detect when such an attack is underway. UAN uses a simple heuristic: vehicles could signal the base station when the rate of digest/MAC failures exceeds some predetermined threshold.
# A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks
## 1 Introduction
Another antenna with an MIMO configuration was proposed in [21]; an electromagnetic band gap (EBG) configuration was adopted, increasing the gain by only 1.9 dB. Thus, an overall peak gain of 6 dB was achieved, with a bandwidth as high as 1 GHz. The size of the proposed EBG-based design is 27.5 mm \(\times\) 27.5 mm. A dielectric resonator (DRA)-based MIMO antenna was proposed in [22], with an operating band range from 26.71 to 28.91 GHz. A peak gain of 7 dB was achieved after incorporating a metasurface with a DRA-based MIMO configuration such that the total size was 20 mm \(\times\) 40 mm. A metamaterial surface was utilized as a reflector in [23] for an MIMO antenna system (two elements), resulting in a peak gain of 11.5 dB. The overall structure had a larger size of 31.7 mm \(\times\) 53 mm. In [24], an MIMO antenna system (two elements) with a 8.6 dB peak gain was proposed with an operational bandwidth ranging from 29.7 to 31.5 GHz. Furthermore, the proposed structure had a large size of 48 mm \(\times\) 21 mm. In [25], a MIMO antenna with a size of 95 mm \(\times\) 80 mm was presented. The proposed design achieved a wide band but with many rapid jumps in the reflection coefficient, indicating that the reflection coefficient response was not smooth with a stable noise level variation at the transmission frequency band. Moreover, the large size represents a further constraint. Overall, the use of metamaterial-based surfaces to improve the radiation characteristics of antennas is challenging in terms of controlling the capacitance and inductance in order to achieve a satisfactory output. As an alternative to metamaterial-based reflectors, surfaces are required for which capacitance and inductance can be easily controlled to enhance the radiation characteristics of the antennas, which is desirable for mm-wave communication. Moreover, wide bandwidth with a high gain requirement must be achieved in order to offer a high data rate, which is a promising feature of 5G technology. Due to the limitations observed, in this paper, we present a wideband, high-gain, and multiport antenna system (two antennas with two exciting sources). A compact antenna system is also proposed with a dielectric layer to improve the gain. Furthermore, to efficiently benefit from the MIMO system, high decorrelation in the radiation patterns is a desirable feature. Thus, a wideband ranging from 17.27 to 25.53 GHz with a total efficiency of more than 80% is achieved, with a peak gain of 8.47 dB, an isolation level of more than \(-\)30 dB, and the benefit of high decorrelation in radiation patterns.