esa-sceva/satcom-chunk-collection huggingface dataset:
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 2 Related Work
### Pretrained Language Models  
[PERSON], et al. (2018) [6] presented a language representation model, BERT (Bidirectional Encoder Representations from Transformers), that is designed to pretrain deep bidirectional representations from unlabeled text. This model can be fine-tuned with just one additional output layer.  
[PERSON], et al. (2020) [7] introduced BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), a domain-specific language representation model that is pretrained on large-scale biomedical corpora. In the same way [PERSON], et al. (2019) [8] presented ClinicalBERT, [PERSON], et al. (2021) [9] the PubMed-BERT, and [PERSON], et al. (2020) [10] the RoBERTa-PM. These models are pretrained on domain-specific, crawled and processed for improvement of the downstream performance. These PLMs improved performance on tasks like text mining, named entity recognition, relation extraction, and question answering.  
[PERSON], et al. (2020) [11] proposed an extension of BERT for ICD coding, BERT-XML. It was pretrained on a large corpora of EHRs with specific vocabulary. This model handles long input text by splitting into chunks and perform predictions for each chunk with a label attention mechanism.  
[PERSON], et al. (2022) [12] proposed a PLM to predict ICD codes (PLM-ICD) that tackles the challenges of previous pretrained models, namely the use of domain-specific pretraining, segment pooling for the long input sequence problem, and level attention for the large label set problem. Experiments were conducted over MIMIC-II and MIMIC-III datasets. PLM-ICD is the first transformer-based PLM that has a competitive performance on these MIMIC datasets.  
[PERSON], et al. (2023) [13] reproduced, compared, and analysed state-of-the-art machine learning models for automatic medical encoding, including PLM-ICD. The authors show that many models underperform due to poor configurations, poor train-test splits, and insufficient evaluation. The analysis shows that all models have difficulties with rare codes, while long documents have a negligible impact. They presented the first results on the newly released MIMIC-IV dataset.
## 2 Related Work
[28] focused on improving training speed and accuracy by adding attention layers to ConvNets. In 2014, Oxford University and Deepmind Company proposed a deep convolutional network with a depth of 16-19 layers. ResNet [29] includes a residual block to ensure the efficiency of increasing the depth of deep networks. The network depth reached an unprecedented level, and ResNet provided excellent classification performance. These deep networks with many parameters have powerful data representation capabilities. The parameter layer of the neural network can extract shallow features, such as texture, as well as the functional semantic features of objects. Although deep structures have powerful data representation capabilities, they have high computational complexity. Thus, it is difficult to perform real-time analysis on mobile terminals without efficient GPU resources. Some recent work has been aimed at improving training or reasoning speed rather than parametric efficiency. For example, RegNet [30], ResNeSt [31], and EfficientNet-X [4] focus on GPU and TPU inference speeds. In 2016, lightweight models achieved a significant breakthrough. They use depthwise separable convolution and have fewer parameters with only a negligible decrease in accuracy. Examples include SqueezeNet [32], MobileNet [33], and XceptionNet [34]. Significant progress has been made in reducing the size of the network. MobileNet, ShuffleNet, and XceptionNet are excellent applications of depthwise separable convolution. The combination of complementary search techniques and the NetAdapt method has resulted in further improvements in the inference speed [35]. In addition, transfer learning has been applied to image classification with small sample sizes. [PERSON] et al. [36] proposed a scene classification method for HSRRS images with transfer learning and the DeCNN (TL DeCNN) model, with obvious classification advantages which will not overfit. [PERSON] et al. [37] also fine-tuned the classification of satellite and scene images based on transfer learning and ResNet50, which classifies images more effectively. Transfer learning is also suitable for solving the problem of insufficient indoor scene recognition data.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 2 Overview of the Three Path Planning Algorithms  
Our previous work investigated three different VRP approaches for this problem using these simulated fish farms [32]. These approaches were the Google Linear Optimization Package (GLOP) [33], Graph Attention Model (GAM) [34], and our heuristic solution, the HAUCS Path Planning algorithm (HPP). These methods were selected because they represent high performing methods with different approaches.
###### Abstract  
Friction stir processing (FSP) offers a unique opportunity to tailor the microstructure and improve the mechanical properties due to the combination of extensive strains, high temperatures, and high-strain rates inherent to the process. Reactive friction stir processing was carried out in order to produce in situ Al/(Al\({}_{3}\)Fe\({}_{4}\) + Al\({}_{2}\)O\({}_{3}\)) hybrid nanocomposites on wrought/as-annealed (673 K) AA1050 substrate. The active mixture of pre-ball milled Fe\({}_{2}\)O\({}_{3}\) + Al powder was introduced into the stir zone by pre-placing it on the substrate. Microstructural characterisation showed that the Al\({}_{13}\)Fe\({}_{4}\) and Al\({}_{2}\)O\({}_{3}\) formed as the reaction products in a matrix of the dynamically restored aluminium matrix. The aluminium matrix means grain size was found to decrease markedly to 3.4 and 2 \(\upmu\)m from \(\sim\)55 \(\upmu\)m and 40-50 \(\upmu\)m after FSP using wrought and as-annealed substrates employing electron backscattered diffraction detectors, respectively. In addition, tensile testing results were indicative that the fabricated surface nanocomposite on the as-annealed substrate offered a greater ultimate tensile strength (\(\sim\)160 MPa) and hardness (73 HV) than those (146 MPa, and 60 HV) of the nanocomposite formed on the wrought substrate.  
AA1050; reactive friction stir processing; in situ nanocomposites; grain refinement; EBSD; annealing Article
## 3 Wireless Sensor Networks
### Prototyping SPWS
#### 3.2.2 Software  
For developing and debugging software for the family of Microchip microcontrollers the MPLAB X IDE application is recommended. It was used to implement three firmware versions, one for each type of node: transmitter; receiver and repeater.  
To minimize the energy consumption, the transmitter device uses the following sequence of actions: sets the microcontroller initial configuration; acquires the sensors values; configures the SPI pins; turns on the MRF24J40 MA module and configures the radio (Personal Area Network (PAN), Channel, and Source); loads the MRF24J40 radio registry with data to transmit; transmits the data; waits for the TX interrupt pin; switches off the RF module; and puts the device in deep sleep mode.  
Figure 6: 3D design of the SPWS enclosure. (**A**) Top view showing the circuit and battery box with holes for air circulation and for the USB connector, as well as the box lid where the PV panel is mounted; (**B**,**C**) views of the bottom part of the lid where the LDR and PIR sensors are mounted; (**D**) detail of the top view of the box interior showing where the PT1000 is mounted in such a way that it will be in contact with the SPWS mounting surface. PV: Photovoltaic Panel; LDR: Light Dependent Resistor; PIR: Passive Infrared Sensor.  
Figure 7: **Left**: an open SPWS showing the PCB, battery and PV panel; **Right**: a SPWS mounted on a wall.  
On the receiver node, the sequence of operations is: sets the microcontroller initial configuration; sets USART communication; configures the microcontroller SPI pins; configures the MRF24J40 radio; executes the following cycle waiting for transmitter's data: activates the RX interrupt pin; reads the MRF24J40 data; sends data to the repeater collector; and turns ON the interrupts.  
Finally, the repeater node executes the following operation: sets the microcontroller initial configuration; configures SPI pins; turns on the MRF24J40 MA module; configures the MRF24J40 radio; executes the following cycle waiting for transmitter's data: activates the RX interrupt pin; reads the MRF24J40 data; loads the MRF24J40 radio registry with data to transmit; transmits the data; and waits on the TX interrupt pin.  
As already mentioned, all information is sent using the format provided by the digital sensors or the ADC, to decrease the transmission energy consumption. Conversion to physical variables units is performed on the repeater device.
# Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
## 6 Transferability of the Adversarial Noise at Smaller Image Regions
### Effects of Shuffling on Adversarial Images' Transferability
Since our goal is to only measure the former source, we compare the \(c_{t}\)-label values of shuffled adversarials with those of shuffled ancestors. When the percentage is larger than \(50\%\), the adversarial images have, on average, a stronger adversarial effect (for the untargeted scenario if one considers \(\Delta_{a}^{k,i}\) and for the target scenario if one considers \(\Delta_{t}^{k,i}\)) when shuffled than when they are not. The adversarial effect is then perceived more by other CNNs for regions of the corresponding same size than at full scale. For all values of \(s\), the first percentage is larger than the second one. This means that distorting the shape of the ancestor object (done by shuffling) helps the EA more than BIM in fooling other CNNs than the targeted \(\mathcal{C}_{k}\). This occurs although computation shows that shuffled BIM adversarials are typically classified with a larger \(c_{t}\)-label value than the shuffled EA adversarials. The percentages achieved with \(s=56\) not only are the largest compared with those with \(s=32\) or \(112\) but also exceed \(50\%\) by far. Said otherwise, a region size of \(56\times 56\) achieves some optimum here. An interpretation could be that a region of that size is small enough to distort the \(c_{a}\)-related information more while also being large enough to enable the adversarial pixel perturbations to join forces and to create adversarial features with a larger impact on different CNNs than the targeted one.
###### Abstract  
Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.  
image detection; one-stage; small target; fine-grain; attention mechanism +
Footnote †: journal: Applied Sciences
## 5 Discussion and Conclusions
### Future Directions
Neighborhood-level business circles are small in scale and limited in number, consisting mainly of supplementary services like small retail stores, newsstands, vending machines, and similar setups. These services are accessible within a 100 m to 200 m radius, allowing users to reach them in one to two minutes. Such business circles should be strategically placed between accommodation and office space or established in areas with the highest accessibility within the 100 m to 200 m travel radius to meet the retail needs of nearby users. Strengthen sustainable building practices During the initial construction phase of the new communities, it is crucial to consider the flexibility of commercial spaces. Designating adaptable areas based on users' pathways and peak gathering times will enable future adjustments, aligning with users' changing needs and societal trends, ultimately fostering the sustainable development of the communities. The flexibility in the layout and architectural design of commercial spaces enables the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Walking Time** & **Type of Business** & **Feature** \\ \hline Communities-level business circles & 10–15 min & All business types & High frequency of use, longer usage time, medium reachability requirements \\ \hline Group-level business circles & 5–10 min & Catering, retail, life service & High frequency of use, shorter usage time, high reachability requirements \\ \hline Neighborhood-level business circles & \textless{5 min} & Mainly retail & Medium frequency of use, shorter usage time, high reachability requirements \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The spatial characteristics of the campus business district. implementation of energy-efficient building practices, including energy-efficient lighting, sustainable building materials, and intelligent technologies. [PERSON] shown that smart thermostats and HVAC control systems can be programmed to enhance heating and ventilation efficiency, lowering energy consumption while ensuring comfort [37]. Integrating commercial spaces with green landscapes promotes biodiversity, reducing the urban heat island effect and improving air quality in communities, as [PERSON] observed [38]. Moreover, repurposing existing heritage buildings or historic structures in communities can minimize the environmental impact of new construction while fostering a distinctive community business culture. Nevertheless, autonomous action within the campus's commercial service space remains improbable without dedicated institutions, incentives, or a compelling push from users and faculty for sustainable endeavors. As society increasingly emphasizes environmental preservation, energy efficiency, and sustainable progress, many communities are projected to prioritize the sustainable advancement of commercial service spaces in the foreseeable future. Governments and institutions might also implement policies or incentives to prompt community-based businesses to consider sustainable development.
# Key Bit-Dependent Side-Channel Attacks on Protected Binary Scalar Multiplication +
## 6 Countermeasures
### Countermeasure for Hardware Implementations  
For hardware implementations, we suggest random initialization that initializes the \(d_{i}\) variable with random bit before each key bit identification phase, as per Algorithm 1. We verified that the leakage based on Properties 1 and 3 could be efficiently eliminated. The result of the classification of \(p_{i}\) is shown in Figures 12(a),b, and 14 (top). The success rate of the attack was approximately 50%, and it was similar to randomly guessing the secret scalar bits with a probability of \(1/2\).  
```
1:\(P\) is a point on an elliptic curve, a \(\lambda\)-bit scalar \(d=(d_{\lambda-1},d_{\lambda-2},\cdots,d_{0})_{2}\)
2:\(Q=dP\)
3:\(regD[\lambda-1:0]\leftarrow\{d_{\lambda-1},d_{\lambda-2},\cdots,d_{0}\}\)
4:\(R_{0}\leftarrow\infty\), \(R_{1}\gets P\)
5:\(d_{i}\gets random\ bit\)
6:for\(i=n-1\) down to \(0\)do
7:\(d_{i}\gets regD[i]\)
8:\(R_{1-d_{i}}\gets R_{d_{i}}+R_{1-d_{i}}\)
9:\(R_{d_{i}}\gets 2R_{d_{i}}\)
10:\(d_{i}\gets random\ bit\)
11:endfor
12:Return\(R_{0}\)
```  
**Algorithm 1** ECC Scalar Multiplication (initialized by random bit)
### Reasons to Design Multiaxial Ankle-Foot Prostheses  
According to the literature, patent and market research carried out by the authors, most of the studies on foot prostheses have focused on movement in the sagittal plane. Only some of these studies were developed considering the rotations of the foot in the other two planes, i.e., frontal plane and transverse plane [19; 30; 31; 38; 47]. However, they are still under development or in a conceptual phase of the design. The importance of developing a foot prosthesis that also considers the abduction-adduction (rotation in the transverse plane) and the inversion-eversion (rotation in the frontal plane) is justified by the fact that even the healthy foot, during level-ground walking, not only rotates in the sagittal plane, but version-inversion and adduction-abduction also occur [48; 49]. Rotations in the frontal and transverse planes are even more accentuated in other types of movements such as the turning step or side-stepping, where the external foot with respect to the directions of these two movements passes from being in version to being in inversion [47]. These two movements are not to be underestimated, as the turning step movement alone represents about 25% of the daily steps [50]. In addition, it should be considered that people can find themselves walking on laterally inclined surfaces during their daily life. In fact, walking on laterally inclined surfaces is something that people face in everyday, especially because in urban streets, sidewalks can be laterally inclined up to 6\({}^{\circ}\) for drainage reasons [51]. Moreover, in general, the surfaces on which individuals walk are not always perfectly flat and regular, therefore, adaptations are always necessary.  
The rotations of the foot prosthesis to adapt to the ground conditions depend on the loads acting on it (module and direction) and on the stiffness/flexibility of the foot in the three directions of rotation. A foot prosthesis designed to move only in the sagittal plane has stiffness tending to infinity for rotations in abduction-adduction and inversion-eversion. With infinite rotational stiffness in the transverse plane and frontal plane, any loads on the foot in those two planes generate high torque at the ankle. This high torque is then transferred to the stump of the amputee generating a perception of discomfort or even pain. Designing a prosthesis that also allows rotations at the ankle in the frontal plane and transverse plane does not mean designing a prosthesis with free abduction-adduction and inversion-eversion, but with a stiffness not tending to infinity in the above-mentioned planes.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
## 3 Single SDN Switch Model  
We use the above model to examine the behaviour of a single SDN switch. Assume the length of the packet buffer \(N=100\), \(T=8\cdot 10^{-7}\) s, \(K=950\), and \(p=0\)--i.e., all connections have their entries in the Flow Match Unit; in consequence, the service time is uniformly distributed with the mean \(1/\mu=0.038\) msec and the squared coefficient of variation is \(C_{B}^{2}=0.33\).  
Figure 5 presents an example of the distribution of interarrival times between IP packets taken from CAIDA measurements; on this basis, we determined \(C_{A}^{2}=1.02\) for the input flow. Additionally, we considered the greater variability of interarrival times expressed by \(C_{A}^{2}=4.08\) and \(C_{A}^{2}=8.16\) to investigate its influence on the performance of the switch.  
The changes in the input rate \(\lambda\) during the considered interval of 1 s are plotted in Figures 6 and 7. Figure 8 displays an example of the probability density function in Equation (14) for a chosen moment \(t=0.750\) s. when the empty queue is empty at the beginning of the interval under consideration, i.e., \(x_{0}=0\) at \(t=0\); it shows the impact of the interarrival time variability on the density function. It is worth to note the logarithmic scale of the plot and the possibility to determine by the model very small numerical values of the function.  
Figure 6 shows the changes in \(p_{N}(t)\)--i.e., probability of packet loss due to the buffer congestion--following the input traffic pattern, and also as a function of the variability of interarrival times, defined by the squared coefficient of variation \(C_{A}^{2}\). Again, even very small numerical values are given by the model, and the computations are stable. Figure 7 presents the time-dependent mean queue. When traffic intensity \(\lambda\) is small, transient periods are short, and the changes in the mean queue follow the changes of \(\lambda\) closely. However, for greater values of \(\lambda\), i.e., for higher utilization \(\varrho\), the transient states are visibly longer than the time between the traffic changes, and the queue is permanently in a transient state. The increase in \(C_{A}^{2}\) also increases the duration of transient states and the size of the queue.  
Figure 5: Packet interarrival times of IP traffic and CAIDAmeasurements versus Poisson distribution.
## 4 Experimental Results and Comparative Analyses
### Datasets and Experimental Setup  
Our experimental evaluation begins with describing the datasets used and the specific parameters and metrics employed to assess model performance.