esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the realm of retail supply chain management, accurate forecasting is paramount for informed decision making, as it directly impacts business operations and profitability. This study delves into the application of tree-based ensemble forecasting, specifically using extra tree Regressors (ETRs) and long short-term memory (LSTM) networks. Utilizing over six years of historical demand data from a prominent retail entity, the dataset encompasses daily demand metrics for more than 330 products, totaling 5.2 million records. Additionally, external variables, such as meteorological and COVID-19-related data, are integrated into the analysis. Our evaluation, spanning three perishable product categories, reveals that the ETR model outperforms LSTM in metrics including MAPE, MAE, RMSE, and R\({}^{2}\). This disparity in performance is particularly pronounced for fresh meat products, whereas it is marginal for fruit products. These ETR results were evaluated alongside three other tree-based ensemble methods, namely XGBoost, Random Forest Regression (RFR), and Gradient Boosting Regression (GBR). The comparable performance across these four tree-based ensemble techniques serves to reinforce their comparative analysis with LSTM-based deep learning models. Our findings pave the way for future studies to assess the comparative efficacy of tree-based ensembles and deep learning techniques across varying forecasting horizons, such as short-, medium-, and long-term predictions.  
demand prediction; forecasting; ensemble models; deep learning; extra tree regressor; LSTM; supply chain management
## 2 Related Research
### Indoor Localization System  
This subsection presents several existing indoor localization methods. Although there are many studies for indoor localization, BLE is the most commonly used wireless communication technology because of its low-power properties and high accessibility (most smartphones support BLE communication). A model-based localization method using BLE beacons was proposed in [21], which tracked the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A positioning algorithm using a particle filter was proposed in [22], which used accelerometer and geomagnetic sensors with a floor map-based particle filter that used the proximity of Bluetooth beacons.  
The problem with these approaches is in non-line-of-sight (NLoS) situations--where a wall or an obstacle exists between communication nodes--because the permeability through an obstacle is relatively low in ultra-high-frequency bands. Thus, BLE received signal strength indicator (RSSI) values can be inaccurate and unpredictable. To solve this problem, several studies used an excessive number of devices in a small room to improve positioning accuracy [21; 22; 23]; such a solution is inadequate because when the number of communication nodes increases, the wireless communication environment can be crowded and cause mutual interference in wireless communications.  
A hybrid situation-aware indoor localization (SAIL) system [24], described in our previous study, uses a combination of low frequency (LF) and BLE to detect the indoor location of multiple mobile nodes in real-time. The system can detect whether a mobile node is inside or outside, based on the door where an anchor node is installed, and identify whether the mobile node enters or exits the space. The anchor node includes two LF antennae inside and outside the door and periodically transmits both LF signals (125 kHz) containing position information. The mobile node near the door receives the two LF signals and compares each signal's strength to determine the user's indoor location. Anchornodes interact with adjacent anchor nodes to form a WSN that extends throughout the building, which enables indoor location awareness of the mobile node. To exploit the LF's obstacle permeability and BLE's low-power characteristics, we adopted this method for indoor location recognition and collected the user's indoor location data by implementing the user's wearable device as a mobile node.
## 3 Security Issues And Challenges
### Challenges on CC  
The CC faces many challenges, which can be summed up as but are not limited to technological and technical challenges and security challenges. Addressing those challenges requires the alignment of CC with business goals to ensure business continuity, user satisfaction, and effectiveness.
# Research on Demand-Based Scheduling Scheme of Urban Low-Altitude Logistics UAVs
## 3 Algorithm  
This paper uses an improved simulated annealing algorithm to solve the model. Specifically, dynamic allocation is added on the basis of the standard simulated annealing algorithm. For the dynamic allocation algorithm, Ref. [17] is referred to. Based on the literature, the process has been properly improved. The dynamic allocation algorithm can predict the best delivery scheme and drone demand dispatches. The simulated annealing algorithm is used to iteratively update the scheme to obtain the optimal one. The combination of the two can solve the problem of the variability of model parameters, so that the parameters can be dynamically adjusted during the calculations to find the best solution. The overall algorithm flow is shown in Figure 1.  
The key point of the improved simulated annealing algorithm in this paper is to add a dynamic allocation mechanism to the standard simulated annealing process to improve the efficiency of the algorithm. The specific steps are as follows:  
Step **1**: Initialize the parameters. Make the initial temperature \(T\) sufficiently high and generate feasible routes for the drones to obtain the initial solution \(S\). The number of iterations for each \(T\) value is \(L\).  
Step **2**: Repeat steps 3 to 10 for \(k=1,\cdots,L\).  
Step **3**: Start the dynamic allocation of the drones.  
Step **4**: Obtain the package quantity \(N_{s}^{e}\) at the demand point. If it is greater than zero, find out the optimal delivery scheme of the drone and go to Step 2; otherwise, go to Step 3.  
Step **5**: Judge whether the power of the drone is sufficient for the delivery. If the power is not enough, replace the primary battery with the backup battery and charge the primary battery. If the backup battery is also insufficient, the drone will be charged. Go to Step 7.  
Step **6**: Judge whether all the demand points have been processed. If they have all been processed, the algorithm ends. A feasible solution will be generated, that is, the new solution \(S^{\prime}\) will be obtained. Otherwise, the next demand point will be processed, and Step 4 will be entered.  
Step 7: Judge whether the working time of the drone is sufficient. If it is not enough, assign a new drone and go to Step 4. If it is sufficient, update the package volume at the demand point to \(N_{s}^{e}=N_{s}^{e}-n\) and go to Step 4.  
Step **8**: Calculate the increment \(\Delta T=C(S^{\prime})-C(S)\), where \(C(S)\) is the evaluation function.  
Step **9**: If \(\Delta T<0\), accept \(S^{\prime}\) as the new current solution; otherwise, accept \(S^{\prime}\) as the new current solution with probability \(\exp(-\Delta T/T)\).  
Step **10**: If the termination conditions are met, the current solution is output as the optimal solution, and the program ends.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 3 System Model
### Energy Model  
In the SDMWN architecture, in addition to location information, mobile devices also set the following parameters: transmission power, receiving power, channel state, etc. [25]. The processing rate for each mobile device according to the Shannon-Hartley formula is presented:  
\[S_{i,j}^{m}=B^{m}*log(1+\frac{p^{m}h^{m}}{\sigma^{2}d_{i,j}})\quad i,j=1,2,3, \ldots,N \tag{1}\]  
where \(d_{i,j}\) denotes the distance between node \(V_{i}\) and node \(V_{j}\). \(B^{m}\) and \(\sigma^{2}\) denote the system bandwidth and noise power at the mobile devices, respectively. \(p^{m}\) denotes the transmission power, and \(h^{m}\) is the channel power gain of mobile device \(m\). When \(m=i\), this formula represents the sending data rate; or when \(m=j\), \(S_{i,j}^{m}\) is the receiving data rate. Consequently, the transmission time on this link can be given by:  
\[T_{ij}^{m}=\frac{I_{i,j}}{S_{i,j}^{m}}\quad i,j=1,2,3,\ldots,N \tag{2}\]  
Figure 1: System architecture.  
and also the energy consumption of the mobile device can be given by:  
\[E_{i,j}^{m}=p^{m}\frac{I_{i,j}}{S_{i,j}^{m}}\quad i,j=1,2,3,\ldots,N \tag{3}\]  
In this study, both sending and receiving energy consumption adopt the above model. When it denotes the sending energy consumption, the bandwidth and the power are the parameters of the sending device; when it indicates the receiving energy consumption, we use the channel state information of the receiving node to calculate. According to the actual application, the energy consumption weight between any two points in the network topology is the sum of the sending and the receiving energy consumption.
# Steam Explosion-Based Method for the Extraction of Cellulose and Lignin from Rice Straw Waste
## 2 Materials and Methods
### Experimental Techniques
#### 2.3.2 Thermogravimetric Analysis (TGA)  
The thermal stability of cellulose and its solutions was evaluated using a TGA 2 STARe System thermal balance, equipped with STARe v16.4 software (provided by Mettler-Toledo, Columbus, OH, USA). Each sample, weighing between 5 and 7 mg, was placed in an alumina crucible. The analysis involved heating the sample incrementally from 30 to 600 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min under a nitrogen atmosphere, with a flow rate set at 30 mL/min [56]. To ensure accuracy, each sample underwent a triplicate analysis, and the displayed results represent the average of these three trials. The margin of error in these tests was maintained at less than \(\pm\)0.05%.  
The TGA data underwent a baseline correction to address instrumental drifts. Different weight loss steps were identified and quantitatively analysed to determine the percentage of each constituent (hemicellulose, cellulose, and lignin). This was based on the total weight loss at specific temperature intervals corresponding to the known decomposition or volatilisation temperatures of the constituents [57]. The residual mass at the end of the TGA run provided estimates of non-volatile components. This methodology enabled the precise quantification of sample constituents, essential for understanding their composition and thermal behaviour.
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 3 Methodology
### Model Structure  
#### 3.2.1 Overview of Resizer Swin Transformer Network  
[PERSON] et al. applied joint transfer learning approaches to classify AD by first using 3D gray matter volume images as input, extracting local information via ResNet to generate feature maps, and finally adding location encoding to the maps that were input to the ViT [25]. The Swin Transformer performs multi-scale learning through cross-window connection and patch merging, as well as decreases the computational complexity of the ViT from the square level to the linear level [28]. Therefore, this study proposes the ReSwin Transformer (RST), a novel network structure that combines a CNN and the Swin Transformer to realize AD classification using sMRI images.  
The RST model proposed in this study is shown in Figure 1. Axial-plane sMRI slices \(\mathbf{X}\) are the input to the RST. A CNN model is first utilized to map the gray-scale image from one channel to three channels to generate color images. The Resizer module then scales the input image proportionally and accentuates the information essential to classification according to the Swin Transformer during the training procedure. The images are separated into non-overlapping patches using the patch-splitting module. Each patch is regarded as a separate _token_. A linear embedding layer then maps the _token_ to a size C channel as the Swin Transformer input. Finally, a Softmax layer summarizes the AD and NC classification predictions from all the slices. The RST uses cross-entropy losses during network training, as follows:  
\[L_{loss}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\text{log}(p(y_{i}))+(1-y_{i})\text{ log}(1-y_{i}) \tag{1}\]
## 2 Materials and Methods
### Model Performance Evaluation and Feature Interpretation  
To evaluate the results of the multi-class classification task, we measured the model's predictive performance on both the training and test datasets. Recall and precision were calculated for each class to compare predictive performance. To quantitatively assess the prediction accuracy for each class, we performed AUROC analysis with the scikit-learn library (v1.3.0) [19; 20; 21]. For interpretability, we used the LimeTextExplainer function from the LIME (Local Interpretable Model-Agnostic Explanations) package (v0.2.0.1) to evaluate and visualize the contribution of individual words in a sentence to the model's class predictions [22]. The LimeTextExplainer function measures the change in classification probability when words are excluded one by one, enabling an indirect comparison of each feature's contribution to the model's predictions. Furthermore, to assess the potential biases within the model, ten repeated 5-fold cross-validations were performed.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 3 Establishing Group Keys
### Intersection Strategy with Onion Encryption  
Instead of building the groups from smaller ones, it is possible to take a top-down approach in which the target group is built as an intersection of larger multicast groups. Again, let \(\mathcal{N}=\{1,\ldots,n\}\) be the set of receivers and let \(S\) be the designated sender.  
During a setup phase, for each \(i\leq n\), a randomly drawn key \(L_{i}\) is preloaded on \(S\) and on all receivers in \(\mathcal{N}\) except for node \(i\).  
Now, let us describe what happens if a multicast group \(\mathcal{R}\) has to be established. If \(\mathcal{R}\) is not of the form \(\mathcal{N}\setminus\{i\}\), then for any \(i\) a group key has to be determined and deployed by \(S\) and the member nodes of \(\mathcal{R}\). Let \(\mathcal{N}\setminus\mathcal{R}=\{i_{1},\ldots,i_{k}\}\). Then, \(S\):  
1. Chooses a group master key \(K\) at random;
2. Broadcasts the _onion ciphertext_ \[\Phi=\mathsf{Enc}_{L_{i_{1}}}(\mathsf{Enc}_{L_{i_{2}}}(\ldots\mathsf{Enc}_{L _{i_{k}}}(K)\ldots))\] (1) together with an identifier of the set \(\mathcal{R}\).  
Preferably, \(\mathsf{Enc}\) should be format-preserving, that is, the ciphertext should be the same length as the plaintext; note that the group key is now transmitted to the target set of receivers in a single broadcast. Each receiver from the set \(\mathcal{R}\) can decrypt \(\Phi\), as it knows all keys \(L_{i}\) for \(i\
otin\mathcal{R}\), and only those keys are used to create \(\Phi\). If \(j\
otin\mathcal{R}\), then \(j\) cannot remove exactly one \"layer\" of the encryption, namely one created with the key \(L_{j}\). Thus, if \(\mathsf{Enc}\) is semantically secure, node \(j\) learns nothing about the group key.  
We refer to this strategy as the _filtering strategy_, as each layer of the onion from expression (1) may be viewed as 'filtering out' one node of the network. In this scheme, each receiver stores \(n-1\) keys and the sender stores \(n\). The main advantage of this method is reducing the number of broadcast messages transporting a group key to just one message and a reasonable number of preloaded keys! A drawback, however, is weaker resilience to node subversion. Any coalition of two compromised nodes may decrypt \(\Phi\), as each node is missing exactly one key and no two nodes are missing the same key.  
There are also a number of differences when two networks join. For the summing strategy, if we agree to double the number of messages to create a multicast group, nothing must be changed on the side of the receivers; only the new sender has to collect the keys from the old senders. Thus, the join operation is relatively easy. In the case of the filtering strategy, all receivers must receive new keys.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 4 Handover Decision Techniques  
In this survey, we categorized the HO decision approaches based on:  
* UE Velocity
* RSRP
* FLC approach
* Metaheuristic Approach
* ML/AI* Spatial Information
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 2 State of the Art
### Space Debris

Table 1: Top 10 breakups, January 2016.  
of a Debris Belt\" [9], introducing a concept, called \"Kessler Syndrome\", that indicates that a collision could cause a progressive avalanche in a chain that would increase the volume of space debris in orbit. The paper explained that the probability of collisions between satellites in orbit increases if the number of artificial satellites in Earth's orbit also increases. Like a chain, satellite collisions would produce orbiting fragments, each of which would increase the probability of further collisions, leading to the growth of a belt of debris around the earth. Orbiting space debris that is below 500 km typically falls back to earth in less than 25 years. At altitudes of 800 km, the orbital decay time is 100 to 150 years and above 1200 km or more it continues to orbit the earth for 2000 years or even more. This kind of generated debris cloud increases the likelihood of further collision, which could eventually lead to a long-feared Kessler syndrome phenomenon that could make space un-usable for the future. Nowadays [10], there is no doubt that the result of the so-called \"Kessler Syndrome\" is a significant source of future debris. The development of new operational procedures over the last 30 years has slowed the growth in orbital debris but these procedures have not been sufficient to prevent growth in the debris population from random collisions. In order to prevent this growth, the space community has identified three different approaches to confront the challenge, i.e., mitigation (to limit the creation of more debris), remediation (to remove debris from orbit), and space situational awareness (to prevent operational satellite collision).
# Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes
## 3 Method
### Improved Yolov5s Model
#### 3.2.1 MobileNetV3 Modules  
MobileNet model is a lightweight CNN introduced by Google in 2017 [39]. It revolutionizes the computation of convolutional layers by introducing Depthwise Separable Convolution. The technique significantly reduces the model's number parameters without compromising accuracy.  
Building upon the successes of MobileNetV1 and MobileNetV2, MobileNetV3 incorporates depth-separable convolutions and a residual structure with linear bottlenecks. Additionally, it introduces the SE channel attention mechanism, which assigns higher weights to important feature channels. To optimize network layers, MobileNetV3 combines the NAS (Network Architecture Search) algorithm [40] and the NetAdapt algorithm [41]. NAS optimizes individual network layers to explore the global structure, while NetAdapt locally optimizes network layers to determine the optimal number of convolutions.  
In terms of network structure, MobileNetV3 brings significant improvements to the tail structure. It removes layers before average pooling and employs \(1\times 1\) convolutions to compute feature maps. This modification reduces computational effort and latency while preserving high-level features. Furthermore, MobileNetV3 replaces the original swish activation function with a new hard-swish function, which enhances the quantization process and speeds up model inference:  
\[Hard-swish[x]=x\frac{ReLU6(X+3)}{6} \tag{1}\]  
The SE attention module in MobileNetV3 performs channel-wise pooling of the output feature matrix. To generate output vectors, the pooled values are passed through two fully connected layers. The former has a number of vectors that is one quarter of the quantity of channels, and it applies the ReLU activation function. As for another, it employs the h-swish activation function and produces an output with the same amount of channels. The structure of MobileNetV3, as illustrated in Figure 2, incorporates the aforementioned SE attention module.
## 5 Two-Stage Spatial Filtering for a PRS
The use of 3D reference windows instead of 2D or 1D improves interference statistics estimation, reducing the CFAR losses [33]. The outputs of this stage are the detected targets and the estimation of their ranges and Doppler values. * Second stage: DoA estimation. A new set of steering angles is defined, \(\mathbf{\Phi}_{DoA}=\{\phi_{DoA,1},\phi_{DoA,2},...,\phi_{DoA,N_{\phi_{DoA}}}\}\), with \(N_{\phi_{DoA}}>N_{\phi_{ba}}\) to increase azimuth estimation accuracy. For each DoA steering direction and Doppler shift, \(p\), the weight vector is calculated to maximize the directivity using Equation (11). DoA estimation is based on the beamformer output spectrum expressed in Equatino (16), for \((m,p)\) pairs where a target was declared. \[S_{m,p}(\phi)=\mathbf{w}_{MD}(p,\phi_{DoA})^{T}\cdot\widehat{\mathbf{R}}_{ \mathbf{s}_{\mathbf{s}}\mathbf{s}_{\mathbf{s}}}(m,p)\cdot\mathbf{w}_{MD}(p, \phi_{DoA})\] \[\widehat{\mathbf{R}}_{\mathbf{s}_{\mathbf{s}}\mathbf{s}_{\mathbf{ s}}}(m,p)=\mathbf{s}_{\mathbf{s}}\cdot\mathbf{s}_{\mathbf{s}}^{H}\] (16) \(\widehat{\mathbf{R}}_{\mathbf{s}_{\mathbf{s}}\mathbf{s}_{\mathbf{s}}}(m,p)\) is the instantaneous spatial covariance matrix estimation for the snapshot \(\mathbf{s}_{s}(m,p)\). For each \((m,p)\) pair, the value of \(\phi\) where the beamformer spectrum, \(S_{m,p}(\phi)\), is maximum determines the estimated azimuth for the target detected at \((m,p)\) delay and Doppler bins. As an alternative, a parametric estimator that exploits the information of \(\widehat{\mathbf{R}}_{\mathbf{s}_{\mathbf{s}}\mathbf{s}_{\mathbf{s}}}(m,p)\) eigenvalues and eigenvectors, such as MUSIC (Multiple Signal Classification), could have been used. However, MUSIC resolution decreases when it is applied to NULAs [15].