esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 High Traffic Load Issues
### Orchestra Limitations  
The current TSCH scheduling does not support dynamic allocation of cells based on traffic and node requirements. Despite its autonomous characteristics, Orchestra [14] determines the TSCH schedule for each node regardless of its traffic load, which can significantly affect communication delay. In the Orchestra schedule, each node reserves slots per slotframe, independently from the amount of data traffic it has to deliver to the root. This is a problem because the demand of each node is not the same and varies depending on the location of the node in the network. Nodes close to the root are constantly in demand and have heavy traffic because all nodes transmit packets to this destination. These nodes close to the root, in addition to sending packets themselves, also forward packets from the leaf nodes to the root node. This results in an accumulation of packets in the queues of nodes close to the root due to their high load, which can lead to a loss of packets induced by an overflow of the buffer memory [23]. On the other hand, the nodes located at the end of the network do not transit that much traffic, and their scheduling can be relaxed. This is why a better allocation of cells is necessary. This limitation makes Orchestra impractical for many delay-sensitive applications. The limitation is that when traffic is not distributed evenly across the network, Orchestra does not adapt its cell allocation mechanism. This limitation is due to the fact that Orchestra allocates the same number of timeslot to all nodes, which leads to significant delays, particularly near the root node where the traffic is dense due to the queuing of packets in the buffers of congested nodes [17]. Consequently, each of these nodes close to the root requires, compared to the leaf nodes, a greater number of available slots to be able to clear this congestion of traffic. We must therefore try to minimize the latency at this level and give these nodes more resources to dump traffic in order to improve the convergecast performance. Indeed, several critical applications such as monitoring and alerting applications may suffer from these delays caused by high traffic load.
# Spectrogram Classification Using Dissimilarity Space
## 2 Proposed Approach
### Projection in the Dissimilarity Space  
Existent classification methods learn to classify patterns using their feature space. In this work, patterns are represented in a dissimilarity space in which every pattern \(x\) is represented by its similarity to a selected set of prototypes \(P=p_{1},...p_{k}\) by a dissimilarity vector:  
\[F(x)=[d(x,p_{i}),d(x,p_{i+1}),...,d(x,p_{k})], \tag{1}\]  
where the similarity among pattern \(d(x,y)\) is obtained using a trained SNN. In order to project each image in the Dissimilarity space \(\Re^{k}\), Algorithm 5 compares each input image (stored in \(X\) in step 3) with the \(k\) centroids (stored in \(P\)) using the trained SNN \(tSNN\) with the predictSiamese function (step 4). The resulting feature space \(F\) includes the projected features of all the input images.  
```
1:Images (\(imgs\)), Centroids (\(P\)), number of centroids (\(k\)), and trained SNN (\(tSNN\)).
2:Feature vectors (\(F\)).
3:functiongetDissSpaceProjection
4:for\(j\leftarrow\) from 1 to size(\(imgs\)) do
5:\(X\gets imgs[j]\)
6:\(F[j]\leftarrow\textsc{predictSiamese}(tSNN,X,P)\)
7:endfor
8:return\(F\)
9:endfunction
```  
**Algorithm 5** Projection in the Dissimilarity space pseudocode
## 1 Introduction
In [10], [PERSON] et al. propose an incremental gradient descent method on Grassmannian manifold for solving the robust PCA problem, named GRASTA. In each iteration, GRASTA uses the gradient of the updated augmented Lagrangian function after revealing a new sample to perform the gradient descent. [PERSON] et al. [13; 14] show that subspace tracking is accomplishable on Grassmannian subspace only when limited entries of data are exposed. In [11], [PERSON] et al. proposed the Online Robust PCA (OR-PCA) which is based on stochastic optimization of an equivalent reformulation of the batch RPCA. OR-PCA processes only one sample per time instance and thus is able to efficiently handle big data and dynamic sample sets, as well as in online systems. Compared to its batch-counterpart where the adopted nuclear norm tightly couples the samples and thus the samples have to be processed simultaneously, OR-PCA pursues the low-rank component in a different manner: using an equivalent form of the nuclear norm, OR-PCA explicitly decomposes the sample matrix into the multiplication of the subspace basis and coefficients plus a sparse noise component. Through such decomposition, the samples are decoupled in the optimization and can be processed separately. However, [PERSON] et al. acknowledged that the computational time of OR-PCA is linear in the sample size and nearly linear in the ambient dimension. The most relatively related to our approach in techniques is [12], where a compressive sensing based recursive robust PCA algorithm is proposed. Their proposed method essentially solves compressive sensing optimization over a small batch of data to update the principal components estimation. However, their algorithm guaranteed a good performance only on data with structured noise which is too restrictive in real life scenarios. In this work, we demonstrate how the Robust PCA can optimally separate the two subspaces modeling the clutter and the target signals. To tackle the above mentioned RPCA limitations, this work develops a simple processing mechanism based on a relatively small batch of frames and processes the new frames in a fashion that allows to keep the target's position in real-time. The target detection and localization are highly influenced by the capacity to recover the two subspaces successfully. This paper considers 2 radar modules, each with a mono-static UWB radar configuration where waveform pulses are transmitted from a single transmitting antenna and the scattered waveforms are received by a collocated receiving antenna. According to our experiments, within a radar range of 10 m, the averaged frame rate is 40 frames per second. Therefore, in order to perform real-time target-detection using the algorithms processing one frame at a time, each frame should be processed in 0.025 s, which is very ideal considering the time required for performing all the involved steps, namely noise filtering, clutter rejection, detecting and localizing the target. For instance, for the stochastic OR-PCA algorithm, the average computational time for one sample of length 240 is 0.0748 s, and 0.0724 s for GRASTA [15].
# Adaptive Emergency Call Service for Disaster Management
## 4 Adaptive Multi-Hop Emergency Call Protocol: 5G-SOS  
The 5G-SOS protocol aims to maximize the probability that the emergency call reaches at least one gNB with a minimum delay and with a reasonable number of exchanged messages. The 5G-SOS procedures and adaptive parameters used by an emergency device and relay device are presented in Algorithms 1-3. The notations used in the Algorithms are presented in Table 2.  
As detailed in Algorithm 1, when an emergency UE, \(E\), generates an emergency call, \(DATA\), the application layer constructs a data message, including the emergency data (rescue video, voice, or text), GPS localization, observed RSSI and a couple of value \(DATA.srcID\) and \(DATA.callID\). \(DATA.srcID\) corresponds to the identifier of the source of the emergency call, \(E\). \(Data.callID\) is the internal identifier given by \(E\) to the emergency call using the function \(callIDGenerator\). \(callIDGenerator\) means that whatever the two different emergency calls initiated by \(E\) are, they have different \(callID\). If the emergency phone, \(E\), is under the coverage of a given gNB, then the emergency call is sent using the traditional 4G/5G uplink communication (PRACH and PUSCH channels). Otherwise, a D2D communication procedure is opted. First, a PSCCH message is sent announcing that emergency data will be sent during the next sidelink radio frame period. The parameters included in PSCCH inform the receivers about the subframes (TRP) and frequencies (RIV) used to transmit the emergency data over the PSSCH channel. The destination group ID of the PSSCH is set to \"ANY\" since it concerns all neighboring UEs. Next, the emergency phone \(E\) counts the number of times its own call is relayed by the neighbors (see lines 10-17 of Algorithm 1). To do so, each \(DATA\) is identified by the exclusive couple of values (\(DATA.srcID\),\(DATA.callID\)). If the received message by the \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline \(\eta\) & FIFO list of calls in the buffer to be relayed \\ \hline \(\chi\) & List of calls already received and processed in the past \\ \hline DATA & Emergency call \\ \hline srcID & Identifier of the source device \\ \hline callID & Identifier of the emergency call \\ \hline callIDGenerator & Function used by each UE to generate unique call IDs \\ \hline waitingCall & A call stored in \(\eta\) \\ \hline deadline & latest time by which a waitingCall is transmitted \\ \hline nbAttempts & Count that \(DATA\) was trasmitted or relayed \\ \hline relay & relay device information content in DATA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notations used in Algorithms 1–3. is its own message (the same couple (_srcID_, _callID_)), then the number of relay neighbors, \(N_{RS}\), is incremented. If, after a period of \(T_{0}\), \(N_{RS}\leq RS_{\text{threshold}}\), the _DATA_ is resent until the maximum number of re-transmissions, \(n_{0}\), is reached. Every attempt is considered a new emergency call by changing the _DATA.callID_ identifier of the message.
# Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning
###### Abstract  
Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.  
adaptive camouflage; convolutional neural network (CNN); k-means; object detection; image completion; machine learning; saliency detection +
Footnote †: journal: Applied Sciences
## 4 System Modelling Methodology  
In this section, a novel design and implementation for HRIS-DMA is studied, which will enable the HRIS to transmit and reflect the incident signal effectively with lower signal power loss. The mutual system requirements, channel model, power optimisation model, and decision-making algorithm are demonstrated by conducting tests and comparing three distinct transmission situations utilising varying sets of THz frequencies. The system design architecture considers the city's layout, which includes tall buildings, high trees, narrow alleys, and blocked users. The network profile depicted in Figure 2 is a model area architecture that closely resembles the implemented design.  
In this model, the BS serves as the central node that transmits data to the users. It must have high transmission power and be capable of handling multiple user connections simultaneously. The BS sends the downlink signals to the HRIS, which then redirects the signals toward the UE. The HRIS is deployed to enhance signal propagation and coverage by intelligently reflecting the signals received from the BS. The HRIS is equipped with a dynamic DMA that can adjust its reflective properties based on real-time user location data. The HRIS modifies the phase and amplitude of the incoming signals to focus them toward the intended UEs, thereby improving signal strength and coverage. The control unit processes location and signal quality data from the UEs and instructs the HRIS on how to adjust its reflective elements to optimise the communication link using the novel DMA.  
The choice of HRIS elements was made by considering both computing efficiency and comprehensive coverage of critical functions by balancing the system's complexity and performance. The number of elements in the field of HRIS can vary based on the individual requirements and complexity of the system. In comparable studies and real-world applications, 10-20 components are commonly employed to provide optimal performance without excessive intricacy. Research articles such as [56; 57; 58] and practices in the business frequently have utilised a comparable set of components to guarantee thorough functionality without unnecessarily complicating the model. The algorithm's design guarantees its suitability for real-world networks by prioritising certain features such as scalability, adaptability, and interaction with existing infrastructure. This enables implementation in practical situations, such as smart cities and improved wireless networks.  
In this study, the system under consideration was single-user multiple-input multiple  
Figure 2: Illustration of an HRIS-DMA-aided downlink communication system, where obstacles block the direct BS–user links. LOS –\(>\) line of sight, RLOS –\(>\) redirected line of sight, and NLOS –\(>\) no line of sight.  
output (SU-MIMO) to meet the specific application scenario, performance advantages, and experimental setup. It is worth mentioning that the system is applicable to be expanded to a multi-user MIMO system with a much-complicated beamforming design to minimise inter-user interference.
# Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
## 3 Experimental Results and Analysis
### Results and Analysis
#### 3.2.1 Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Anchor\_Size** & **mAP (\%)** & **F1-Score (\%)** & **IoU(\%)** \\ \hline Mask R-CNN & [32,64,128,256,512] & 68.58 & 71.31 & 61.42 \\ RPN-panicle1 & [8,16,32,64,128] & 64.57 & 80.49 & 60.36 \\ RPN-panicle2 & [16,32,64,128,256] & 80.25 & 74.78 & 75.73 \\ RPN-panicle3 & [64,128,256,512,1024] & 60.41 & 69.06 & 58.75 \\ RPN-panicle4 & [128,256,512,1024,2048] & 55.42 & 67.31 & 52.73 \\ Panicle-Mask & [16,32,64,128,256] & 89.10 & 81.95 & 84.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection results of the models for different anchor box sizes.
# APIASO: A Novel API Call Obfuscation Technique Based on Address Space Obscurity
## 5 Experimental Evaluation
### Model Protection Strength Evaluation
#### 5.1.1 The Obscurity Degree of API Address Space
The horizontal coordinate represents the obscurity degree of the API address space at different level setting criteria, and the vertical coordinate represents the ratio of program execution time before and after program obfuscation. The results show that the best address space obfuscation is achieved at a low runtime overhead with a threshold of 2. Therefore, all subsequent experiments will be conducted with a threshold of 2. According to the previous section, the API call obfuscation methods include IAT tampering, basic block-level position obfuscation, function-level position obfuscation, DLL-level position obfuscation, and API call site tampering. We applied the above obfuscation means and APIASO to six magnitudes of programs under Windows. The DLL space functions called by the program are recorded and the \(APIASODegree(P)\), which is shown in Figure 6. The horizontal coordinate represents the obfuscated program, and the vertical coordinate represents the \(APIASODegree(P)\) after obfuscation. The results show that the APIASO is significantly higher than other obfuscation methods. To visually describe the distribution of functions in memory during API calls, we track the execution of all functions specifically associated with _CreatefileA_ calls. We count these functions and record their offset address and virtual memory address. As shown in Figure 7, the Windows standard call procedure is compared with the calling procedure under the obfuscation of the other four types of API calls, with the vertical coordinate indicating the address offset of the function and the horizontal coordinate indicating the virtual memory address of the function. During a standard API call, only one call instruction exists in the user code space, so there is only one function associated with the API call. However, during API calls with call site tamper and IAT redirection, functions related to the obfuscation also exist in the user space in addition to the function  
Figure 5: Address space obscurity at different levels. containing the call instruction. No changes occur within the API code space. When it comes to API calls under position obfuscation, the code related to the entry function of _CreatefileA_ exists in the user code space, while the entry function of _CreatefileA_ in the API code space is hidden. Lastly, for API calls under APIASO, low-level, bogus, and special functions exist in the user code space along with the other obfuscation-related functions mentioned above. Only high-level functions remain in the API code space, while other key functions are hidden within the user code space. Figure 6: Degree of address space obscurity. Figure 7: Function execution trajectory during API calls. Therefore, APIASO significantly obscures the boundary between user code space and API code space compared to other obfuscation techniques, which significantly increases the difficulty of API call analysis.
# Evaluation of an Optical Sorter Effectiveness in Separating Maize Seeds Intended for Sowing
## 2 Materials and Methods  
### The Considered Technological Flow and the Optical Sorting Equipment  
The study was carried out at a farm producing cereals for planting material, located in Romania, Ilfov County. Tests were conducted using a known and controlled contamination level using several types of contaminants (other types of seeds such as sunflower seeds, soybean seeds, and damaged seeds). The process evaluation aimed at obtaining highly qualitative corn seeds intended for sowing and exploring the working environment of a medium-level breeding farm in Romania. The analyst's participation was necessary since the farm's technical flow had failed to produce the desired outcomes.  
The technological flow adjustments were made by the farm technicians, with the exception of the optical sorter, whose adjustment was made by the representatives of the producing company.  
The technological flow at the farm level is presented in Figure 1.  
Commercial optical sorter equipment was used for the testing, which is highly specialized for grain separation. It uses advanced optical technology to sort the seeds and separate them according to their size, shape, color, and other physical attributes. According to the producers, the proposed technology was highly accurate and could detect even the smallest differences in the seed shape and size. The mass of seeds was guided by gravity in several troughs provided with flow channels, through which the total mass of material aligns, moves, and falls in front of the optical sorting system in the shape of a \"canvas\" or \"curtain\". The optical sorting system used optical cells to identify impurities and seeds that did not adhere to a pre-established color and formed standards set in the computer memory. The optical sorting system lighted the front and back of the material \"canvas\" and the material itself. The components in the seed mass that deviated from the \"reference image\" recorded in the computer's memory were detected by the optical system, and a jet of compressed air changed their fall trajectory.  
The material removed from the flow is called \"reject\" in this paper, while the rest of the good seeds falling on a normal trajectory is called \"accept\". This process was controlled by specialized software that controlled the recording of falling material images and compared them with the preset standard images. The decision to blow the rejected seeds was very fast, and the working operations carried out by the machine could not be noticed by the human eye. In order to assess the effectiveness of the optical sorter integration in the flux, the working approach used was based on the knowledge of the raw material's quality.
## 4 Results and Discussion
### Performance of Deep Classifiers Trained Using Transfer Learning  
This section shows the results of the deep neural networks tested. GoogleNet, MobileNetV2, and InceptionV3 perform better than AlexNet and SqueezeNet. We achieved the highest accuracies 97.86%, 96.04%, 98.40%, 98.61% and 99.47% with AlexNet, SqueezeNet, MobileNetV2 GoogleNet and InceptionV3, respectively. The training of these neural networks is performed with three different training optimizers, namely adam, sgdm, and rmsprop. Table 1 shows the details of the experiments performed by varying training functions and learning rates to fine-tune the deep models. The loss for InceptionV3 is lower compared to other tested deep models. The highest accuracy (99.47%) is achieved by the InceptionV3 model with Adam optimizer and a learning rate of 0.0001, while GoogleNet with the training function rmsprop and a learning rate 0.0001 achieves the highest performance of 98.40%. On the other hand, MobileNetV2 perform slightly better compared to GoogleNet and achieved an accuracy of 98.61% with a learning rate of 0.001, and the sgdm optimizer. The possible reason for the highest accuracy achieved with InceptionV3 could be the number of layers in the model. InceptionV3 contains more layers than GoogleNet and AlexNet. AlexNet achieves the highest accuracy of 97.86% with the training function adam and a learning rate 0.00001. Another important observation is that for both AlexNet and InceptionV3, the highest accuracy is achieved with Adam, while GoogleNet and SqueezeNet achieve the highest accuracy with rmsprop. Only MobileNetV2 that achieved the second-highest accuracy with the sgdm optimizer. The learning rate is an important hyperparameter: In particular, AlexNet and SqueezeNet achieve the lowest accuracy 16.67% at a learning rate of 0.01, which is due to an unstable training process. If we decrease the value assigned to the learning rate, we achieve better performance as shown in Table 1. Overall, InceptionV3 performs the best, and SqueezeNet performs the worst among all five tested deep classifiers.