esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conceptualization of Last Mile Logistics  
The term last mile was first coined in the telecommunications industry [14] and was originally used to indicate the final leg of a delivery system for traditional deliveries from brick-and-mortar retailers [32]. Early applications of LML in the literature narrowly extended supply chains directly to the end consumer, which simply represented a home delivery service for consumers [33; 34] and the last part of a delivery process [13]. In this  
Figure 4: The 50 most frequent terms identified from the target research articles. regard, traditional LML was conceived of as deliveries for the last mile supply chain, last mile, final mile, home delivery, B2C distribution, or grocery delivery. However, the traditional definitions have not been sufficient to describe newly emerging LML features driven by recent e-commerce changes that inherently involve new uncertainties [35; 36], exclusion of in-store order fulfilment processes [23], and non-specific final locations [33; 37]. Despite a steady increase in the number of contributions to LML research, LML has been conceptualized inconsistently across studies without consensus around a single definition of LML [11]. As various LML approaches have been available, a consistent and robust definition of LML is necessary to be a basis for LML design frameworks. Table 1 shows the various LML-related definitions used in our target research articles. An important insight from the target research articles is the functional scope of three different logistics domains: city logistics, urban logistics, and LML. The three logistics domains can be clarified with different perspectives [14]. City logistics is a critical field in urban areas and mainly focuses on stakeholders' interrelationships from the perspective of macro-level logistics. However, urban logistics refers to how parcels can be effectively transported in urban areas at a meso level. On the other hand, LML is related to delivery processes at a micro level. Considering these three different functional scopes, in this paper, a working definition for LML is proposed to synthesize all the above LML-related definitions as follows:  
_LML is the final branch of parcel delivery services that involves a point of delivery to a final consignee's preferred, predefined collection point or destination location after order placement._  
Based on the above LML definition, the scope of interest in this paper covers a research stream from leaving warehouses of the supplier or logistics provider to arriving at the collection point designated by a final consignee. The underlying characteristics of LML concepts under the LML definition have been presented in various types of LML-related research. The operational and technological aspects of LML identified in Section 2 show
# Practical Inner Product Encryption with Constant Private Key +
###### Abstract  
Inner product encryption, first introduced by [PERSON] et al., is a type of predicate encryption in which a ciphertext and a private key correspond to an attribute vector and a predicate vector, respectively. Only if the attribute and predicate vectors satisfy the inner product predicate will the decryption in this scheme be correct. In addition, the ability to use inner product encryption as an underlying building block to construct other useful cryptographic primitives has been demonstrated in the context of anonymous identity-based encryption and hidden vector encryption. However, the computing cost and communication cost of performing inner product encryption are very high at present. To resolve this problem, we introduce an efficient inner product encryption approach in this work. Specifically, the size of the private key is only one \(\mathbb{G}\) element and one \(\mathbb{Z}_{p}\) element, and decryption requires only one pairing computation. The formal security proof and implementation result are also demonstrated. Compared with other state-of-the-art schemes, our scheme is the most efficient in terms of the number of pairing computations for decryption and the private key length.  
Keywords: predicate encryption; inner product encryption; constant-size private key; efficient decryption; constant pairing computations +
Footnote †: _Applied Sciences_
## 3 Results
### Calculated Rol Data  
Injection rate calculations were performed using Equation (1), using the flow obtained from the test bench and translated to the total mass of injection \(\dot{m}_{f}\) data collected during on-site testing (Figure 6). This process ensured the veracity of the measurement for all variables and avoided measurement errors. A relationship of units is required to ensure the accuracy of the results, allowing for a reliable comparison and analysis. The 0D model for calculating the RoI suggests the use of physical variables such as injection pressure, nozzle geometry, and properties of fuel such as density, and these parameters are applied mainly in Equation (1). Therefore, the injection rate for Viscor fuel (Fuel 1) was calculated with the equation, based on the flow and mass data presented in Table 9, the fuel density values detailed in Table 1, and the characterization of the nozzles shown in Table 8. Table 9 shows the volumetric flow values divided into two groups for each nozzle: the results obtained from the previously measured fuel flow and mass.  
The RoI calculations for diesel fuel (Fuel 2) were carried out in the same way as those for Viscor fuel (Fuel 1). Table 9 shows the injection rate values for the flow rate and mass injected in each work cycle. On the other hand, Figure 7a is a 3D graph, where the \(x\)-axis is the injection pressure, the \(y\)-axis is the calculated \(\text{RoI}_{\text{m}}\), and the \(z\)-axis is the fuel flow rate. The same happens with Figure 7b, and the difference is that here the \(\text{RoI}_{\text{m}}\) calculated based on the mass is presented. The lines coloured have the same meaning than in the Figures 5 and 6.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 2 Related Works
A worth-reviewing paper overviewing of applications and advances of DL in agriculture in detail and information on key aspects (such as databases, and typical crop classification problems, among others) is presented in [5]. Another work of great interest is that reported in [44], which outlines CNNs and their applications in different fields. Regarding the use of the Campo Verde database for crop classification purposes, the following works are worth making note of. The Campo Verde database was introduced in [30], with the intention to outline the problems related to the farming field for educational purposes. It is documented and manually annotated. [30] also depicted an experiment with a random forest classifier. The study in [31] evaluates a CNN and a FCN obtaining greater efficiency when using the latter architecture. For experiments, two sequences consisting of an image stacking were evaluated: the first sequence corresponding to the period from October 2015 to February 2016, and the second sequence from March to July 2016. As a conclusion, the FCN is proven to be a better solution outperforming the baseline in terms of processing time in the inference phase. Another similar technique was proposed in [20], which uses CNN- and RNN-based architectures. This study reported higher results for the RNN since the CNN is fed with the co-occurrence matrix features rather than extracting their own features. A classification approach of tropical crops is presented in [32], which followed a method that compares auto-encoders, CNNs, and FCNs networks in terms of segmentation performance over Sentinel images of Campo Verde. As a result, it was observed that the accuracy was better when using DL techniques (specifically, CNN), rather than the other considered methods--indeed, the CNN-based approach showed a more stable behavior. The research presented in [33; 34] apply DL techniques to classify not only Campo Verde but also [PERSON] database [45] (another agriculture database from Brazil). Both works use approaches based on an FCN and similar parameters including a 32 \(\times\) 32 patch size and assess the methodology over individual images and sequences composed by several images. It is worth mentioning that [34] adds an LSTM structure to the methodology, which allows learning from multi-temporal data. The incorporation of such LSTM yielded a remarkable increase in the overall performance, highlighting then the importance of the temporal information for crop recognition. At this extent, it is worth noticing that most of the previously mentioned works were traditionally tested only on Sentinel images of Campo Verde (not the Landsat images) for crop classification tasks since Landsat images are covered by clouds. Furthermore, studies have accomplished a \(f_{1}\) score not over 75%. Likewise, in [46], it is highlighted that the classification of heterogeneous crops is still a challenging open issue as there exists a diverse and complex range of spectral profiles of crops, as it is the case of Campo Verde.
# Seismic Risk Analysis of Existing Link Slab Bridges Using Novel Fragility Functions
## 4 Failure Rate and Result Discussion
### Failure Rates Associated with Different Damage Mechanisms Combination  
In order to enrich the level of information concerning the specific vulnerabilities of the bridge, fragility analysis is performed using the novel approach defined by Equation (3), capable of quantifying the probability (conditional to seismic intensity levels) of occurrence of multiple failure modes (i.e., groups of mechanisms activated simultaneously).  
Fragility curves for different subsets of failure scenarios are presented in Figure 10 for both damage and collapse limit states. This type of fragility analysis is useful (even before moving to the next step of risk estimation) because useful insights can be retrieved concerning the group of damage mechanisms most representative of different intervals of seismic intensity. For instance, looking at the collapse limit state (Figure 10b), it can be observed how the range of IMs approximately from 0.2 g to 0.75 g is governed by link-only related failures, while at the higher intensities both bearings and links failures are predominant. An additional fragility type, labeled as _Global_, is reported in Figure 10, and it is given by the sum, _im-by-im_, of the probabilities related to the different damage scenarios. This fragility can be also seen as the envelope of the classic ones presented in the previous Section 4.1 (Figure 8).  
The annual rates of exceedance associated with each of the aforementioned failure mode combinations are collected in the chart of Figure 11. The combined damage scenarios are identified with capital letters denoting the initials of the names of the involved elements (e.g., PLBA = Piers-Links-Bearings-Abartments; LB = Links-Bearings, and so on). The risk estimates related to Global fragility are also shown in the chart using a colored asterisk as a marker and are labeled as _Global_. It is worth noting that the failure rates presented in this  
Figure 10: Fragility curves for different damage mechanism combinations evaluated for two limit states: (a) damage, (b) collapse.  
Figure 9: Annual failure rates for different elements and failure modes at damage and collapse LSs.  
chart are always lower or at maximum equal to those presented in the previous subsection, simply because they refer to mutual partitions of the failure.
# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
## 3 Efficiency Optimization Control Based on Current Angle Look-Up Table
### Energy Optimization Control Strategy Based on Current Angle Look-Up Table  
The effectiveness of current angle \(\beta\) for efficiency optimization control was verified and the optimal set of \(\beta/i_{s}\) in the whole working range was obtained. Based on that, the control strategy based on current angle \(\beta\) is proposed in this paper. The schematic diagram is illustrated in Figure 11.  
Firstly, the actual speed of the motor was obtained by the encoder, and then the difference between the target speed and the actual speed was converted into the required torque according by the speed loop PI controller. Secondly, the \(T_{e}/n_{m}\)-\(\beta/i_{s}\) look-up table was searched online according to the required torque and the actual speed, and the target \(\beta\) and corresponding \(i_{s}\) were obtained. Finally, in the voltage command module, two voltage PID controllers were used to get the required control voltage \(U_{\alpha}\) and \(U_{\beta}\). The SVPWM algorithm was used to get the voltage signal and then control the inverter and the motor, so that the motor can realize the specified work. It should be noted that the voltage feedback calculation module can increase the accuracy of the voltage signal.
## 4 Communication Technologies
### GPRS Communication  
A communication option that does not use trains as base stations is the use of communication technologies based on cellular networks, such as GPRS or more concretely the GSM-R (Global System for Mobile Communications-Railway) [24] protocol, specially tailored for railway communications. GPRS is a protocol for packet-oriented mobile communication and operates on the known telephone networks and 2G and 3G data. In this architecture, each Arduino would work independently and would have a GPRS device that allows sending information directly to the Internet without having to communicate with each other or rely on trains (see Figure 11).  
The main characteristics that must be addressed are the amount of transmitted data for battery consumption and the cell coverage. This option is not viable where there is no cell coverage. These two limitations do not make GPRS the most attractive option. However, due to its robustness and availability (by not relying on trains to communicate) it is an option that may be appropriate to take  
Figure 10: RFID: communication architecture.  
into account in specific sections of slab tracks or critical sections where continuous monitoring, quick response and high reliability are required.
## 1 Introduction
Sparse representation provides superior estimation even in an ill-conditioned system [33], and has been found to be very useful in medical imaging applications. However, one challenge of designing this system is the presence of a multiplicative speckle signal because dictionary learning methods are not effective on multiplicative and correlated noise. We overcome this by using two different methods. Firstly, the speckle noise is transformed into additive noise using an enhanced homomorphic filter that can also capture high and low frequency signal of the image. Secondly, we introduced TV regularization of the image and sparse prior over learned dictionaries. Total variation regularization is efficient for noisy image, while the patch-based dictionaries are well adapted to texture features [34], and reduces the artifacts in smooth pixel regions [35]. The advantage of the sparse prior is that it utilizes fewer dictionary columns to reconstruct a noiseless ultrasound image without losing many important features of the signal. Therefore, in our proposed model we combined the two approaches, the patch-based SR over learned dictionaries and the pixel-based TV regularization method, for efficient speckle reduction. The K-singular value decomposition (KSVD) algorithm [36] is used to learn two modified dictionaries from reference ultrasound image datasets and the corrupted images; these are referred to as dictionaries 1 and 2, respectively. The results are evaluated on both dictionaries and compared with conventional algorithms to show that the speckle noise is suppressed effectively in the ultrasound image using SR. The rest of the paper is organized as follows. Noise model and related works are described in Section 2. The proposed SR framework for speckle reduction in ultrasound imaging is presented in Section 3. In Section 4, the experiments and results obtained are discussed. The paper is concluded in Section 5.
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 3 The Proposed SC Flipping Algorithm
### Training Process of the DLSTM network  
To obtain the training data of the DLSTM network, we carried out the simulations presented in Figure 6a. First, we obtained the polar codes according to the method in Section 2. Then, we performed the binary phase shift keying (BPSK) demodulation and incorporated additive white Gaussian noise (AWGN) channel noise. After that, \(2\times 10^{6}\) polar codes were generated and decoded by the SC decoder. If the decoding result passed the CRC detector, the decoding was successful. Otherwise, it became the training sample for the DLSTM network. The sample consists of the absolute value of the LLR sequence and one-hot encoding of the first error bit, which is denoted as \(\mathcal{D}=\{(\mathbf{x_{1}},\mathbf{y_{1}}),(\mathbf{x_{2}},\mathbf{y_{2}}),\ldots,(\mathbf{x_{j}},\mathbf{y_{j}})\}\), where \(j\) is the size of the sample, including the training set and validation set. \(\mathbf{x_{c}}\), \(1\leq c\leq j\) is the input of the network. \(\mathbf{y_{c}}\), \(1\leq c\leq j\) is the label and represents the expected output of the DLSTM network. The process of extracting information bits is shown in Table 1, where the binary sequence in the second row is the information pattern, in which \(0^{\prime}\) denotes the frozen bit and \(1^{\prime}\) denotes the information bit. The proposed algorithm clips all frozen bits in the output layer because flipping a frozen bit is useless and causes corresponding error propagation. The classification is reduced by \((N-K)/N\) compared to the method of [PERSON] [17].  
In order to train a more efficient DLSTM network, the data were divided into the training set and validation set according to the ratio of 8:2. During the training, the regularization method [21] was used to prevent training overfitting. The L2-norm was applied to the weights, taking L2 = 0.008. The Adam optimizer [22] was adopted to design adaptive learning rates for different parameters because it can handle sparse gradients very well. The goal of the DLSTM network is to be a well-trained classifier where each information bit is a category. We employed the cross-entropy function to measure the loss between the expect value and the predicted value.  
\[E(x)=-y\log\hat{y}+(1-y)\log(1-\hat{y})\enspace, \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**Index** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline Location & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Index of information bit & & & & & & & 0 & & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ \hline \end{tabular}
\end{table}
Table 1: Extract information bits for \(P(16,8)\).  
Figure 7: A DLSTM unit.  
where \(y\) represents the labeled data and \(\hat{y}\) represents the predicted value of the network. The smaller is the loss \(E\), the better is the network trained. As the entire DLSTM is trained offline, it does not increase the SC flipping decoder complexity.
# Effect of Bentonite Content and Hydration Time on Mechanical Properties of Sand-Bentonite Mixture
## 3 Results of Calcium Bentonite-Sand Mixture
### Effect of Calcium Bentonite Content on the Stiffness  
The tangent modulus was obtained by calculating the tangent slope at the different strains on the stress-strain curve, and the variation in tangent modulus with various bentonite contents and hydration times is shown in Figure 5. The presence of bentonite weakens the intergranular bite of the sand, and a bentonite suspension is attached to the surface of the sand, thus reducing the tangent modulus of the specimen. At the initial stage of axial deformation, the mixture specimen does not shrink the water absorption section, and the volume variable is almost unchanged. When the axial strain is around 2%, the pure sand specimen reaches the critical axial strain and changes from shear shrinkage to dilatancy. Meanwhile, the sand-bentonite mixture also reaches the critical axial strain and begins to generate dilatancy and absorb water. With the development of axial strain, the dilatancy of the sand-bentonite specimen is smaller than that of pure sand. The results show that the presence of bentonite weakens the shear shrinkage and dilatancy of sand in the static triaxial test of drainage. The change may be caused by the cementitious effect of bentonite in the interspaces.  
Figure 6 shows the nonlinear behavior of the tangent modulus of the sand-bentonite mixture at various bentonite contents and confining pressures. The results indicate that the hydration time has less effect on the tangent modulus at large strain levels. The tangent modulus decreased with the increase in hydration time. The initial tangent modulus of the test curve with a short hydration time (24 h) is relatively higher than that with longer hydration times.
## 1 Introduction
### Characteristics of Technical Lignins  
Technical lignins are those produced in industrial delignification processes. They are mainly generated in pulp and paper mills, although the lignin generation during biomass pretreatment in lignocellulosic biorefineries has been increasing in recent years [29; 30]. Despite their high availability and applicability in industry, lignins from pulp and paper mills have been used on a large scale as fuel for heat and electricity, with less than 2% being used in the production of specialty chemicals [2; 3; 6]. Regarding residual lignins from biorefineries, it is still necessary to create better strategies for their commercialization [31]. It is worth mentioning that the inappropriate disposal of residual black liquors can cause serious environmental problems [32].  
In the literature, lignins are divided into sulfur-containing lignins and sulfur-free lignins. The Soda and Organosolv processes generate sulfur-free lignins, while the Sulfite and Kraft processes generate sulfur-containing lignins [8; 9]. Lignosulfonates are generated in the sulfite process and have a high molecular weight (1000-120,000 g/mol) and water solubility, which contributes to their commercial application [33]. In general, the sulfur content contributes to the higher solubility and anionic charge density of sulfonated lignins, as the molecular weight is directly related to the adsorption characteristics of these compounds [34; 35].  
Kraft lignins have lower sulfur content and lower molecular weight (1500-25,000 g/mol) compared to lignocellulosic [33]. The Kraft process is widely used in pulp and paper mills. The high availability and underutilization of residual Kraft lignins are some of the reasons that justify the interest of researchers in their valorization [5; 34]. The Soda process is typically applied in the delignification of agricultural crops [2]. Soda lignins have higher purity and lower molecular weight than lignocellulosiconates (1000-15,000 g/mol) [8; 33]. Finally, Organosolv lignins are those obtained from the homonymous delignification process, which involves the use of organic solvents at high temperatures. These lignins have high purity, low molecular weight (500-5000 g/mol), and low water solubility, although their characteristics depend on the solvents used [2; 33]. It stands out that procedures that apply a green chemistry approach using eco-friendly solvents are currently preferable [36]. More in-depth information about production, characteristics, and applications of technical lignins can be found in existing works [3; 5; 26].