esa-sceva/satcom-chunk-collection huggingface dataset:
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Vector Space Model (VSM)  
It is a traditional method for measuring the distance between text documents after simplifying text data by BOW, where term weight vectors represent the original texts [27; 78]. VSM uses document-level word occurrences as its representational basis. Using different term-weighting methods, a document with basic terms can be mapped into the high-dimensional term feature space. The term-weighting algorithms are utilized to determine which terms are most significant. The performance of text analysis can be improved by using the appropriate term weighting. Term weighting aims to assess the significance of terms within a given document or corpus. Several different term-weighting methods are available. Short text has a limited length of the text, and the size vocabulary of words in the corpus is often quite large. Therefore, when calculating similarity/distance using cosine similarity or Euclidean distances [29], the VSM-based representation for short texts produces sparse and high-dimensional vectors, which are less discriminative.  
Topic-model-related methods are utilized to learn high-level semantic text representations to alleviate the disadvantages of VSM with a short text [1]. Based on the frequencies of the terms in the original text documents, the weights of the terms are calculated. The VSM transforms the term frequency into a numerical vector. Although simple to set up, VSM has drawbacks, including high dimensionality and sparsity. These weaknesses of VSM become even more apparent when dealing with short text data. In addition, the global term weighting is calculated by querying all documents. Generally, the common word weighting strategies can be divided into local and global term weighting schemes [27; 79].  
_I. Local term weight_  
It is the term frequency value within the document derived by several methods [78]. For example, the most important and commonly used local weighting schemes, as shown in Table 1, are term presence (_tp_), term frequency (_tf_), augmented term frequency (_aff_), the logarithm of term frequency (_ltf_), and BM25 term frequency (_btf_). The most notable and common representation is _tf_, which indicates the number of occurrences of the term in the document. Thus, it emphasizes the words that appear more frequently. _tp_ is the simple binary representation, which ignores the number of appearances of the term in the document.  
Figure 8: BOW with two text documents represented as binary vectors.  
This can be useful when the number of times a word appears is unimportant. _tp_ and _tf_ are combined in the _aff_ scheme. It tries to instil confidence in any term in the document and add confidence to frequently occurring terms. _ltf_ is used as a logarithmic function to set within-document frequency because a term that appears five times in a document is not always five times as important as a term that occurs once in that document.
## 4 Deep Reinforcement Learning Model for Layer Distribution
### Design for DRL-RLD Model  
The state space \(S\) consists of the following components to provide the agent with comprehensive state information that accurately reflects the environment: the position of the UAVs in the grid, the current memory usage of the UAV \(i\), and the computational capacity of the UAV \(i\). The CNN layers currently assigned to UAV \(i\), the operational status of UAV \(i\), the position of the ground device in the grid, and the positions of hot cells in the grid.  
The action \(a\) is defined as the allocation of CNN layers to the entire UAV swarm. The agent is capable of undertaking actions based on the current state of each UAV, including the computation capacity, memory usage, and operational status.  
The reward is designed to provide a higher reward when the end-to-end latency of distributed inference is low and impose penalties when the constraints of the system are  
Figure 2: The proposed DRL-RLD model for layer distribution.  
violated. This structure can enable the DRL-RLD model to learn how to distribute layers in a way that minimizes end-to-end latency. The penalties are imposed when the constraints below are not satisfied.  
* Constraint 1: All layers within a CNN model must be allocated to UAV swarm, and each layer must be allocated to exactly one UAV. This constraint is violated if unassigned layers occur or a layer is assigned to multiple UAVs.
* Constraint 2: for each UAV \(i\), the sum of the memory requirements and computational requirements of the assigned layers must not exceed the UAV's memory usage and computational capacity, respectively.
* Constraint 3: CNN Layers can only be assigned to operational UAVs, and assigning layers to inoperative UAVs violates this constraint.  
A penalty mechanism is structured so that if any constraint is violated, the reward for that specific time step is set to be zero. For example, if a UAV exceeds its resource limits as specified in Constraint 2, the corresponding variable is set to zero (\(\text{Cons}_{2}=0\)), effectively setting the reward to zero. Conversely, when all constraints are satisfied, the variables for each constraint are set to 1, ensuring that rewards are granted only when all conditions are satisfied. This penalty mechanism ensures that the model learns to distribute layers while strictly satisfying the constraints. A reward function applying the penalty mechanism \(R\) is defined as:  
\[R=\frac{A}{L_{\text{comp}}+L_{\text{tran}}}\cdot\text{Cons}_{1}\cdot\text{Cons} _{2}\cdot\text{Cons}_{3}, \tag{16}\]  
where \(\text{Cons}_{i}\) is a parameter that denotes whether Constraint \(i\) is violated, \(L_{\text{comp}}\) is total computation latency, \(L_{\text{tran}}\) is total transmission latency, \(A\) is a constant, and the value of the reward function increases as \(L_{\text{comp}}\) and \(L_{\text{tran}}\) decrease.
## 2 System Architecture
### System Components  
The experimental set-up consisted of a thermal camera Testo 882 and a triangulation laser scanner Konica Minolta Vivid 9i, both shown in Figure 1. The Testo 882 has an FPA detector type with 320 \(\times\) 240 pixel (but image output up to 640 \(\times\) 480 thanks to the super-resolution feature), a FOV of 32\({}^{\circ}\)\(\times\) 23\({}^{\circ}\) and a range of detected temperature switchable between -20 \({}^{\circ}\)C and +100 \({}^{\circ}\)C and 0 \({}^{\circ}\)C and +350 \({}^{\circ}\)C (accuracy \(\pm\)2% of reading for both). The Konica Minolta specifications are 307,200 pixels, three interchangeable lens of focal length 25 mm, 14 mm and 8 mm, and a weight of approximately 15 kg.
## 4 Evaluating the Performance of WSN Routing Strategies with Respect to DSA
### Passive Strategy  
Such routing protocols do not consider the link state of the receiving node while sending data towards the sink. Data packets are usually flooded in all available paths to the sink in a constrained manner or without any constraint at all. A good representation of this strategy is message-initiated constrained flooding (MCF) routing [25]. While flooding-based strategies generally decide whether to broadcast at each hop, in MCF flooding, the decision is regulated by a cost function known as the  
Figure 2: Classification of routing protocols with respect to transmission strategy.  
Q-value, indicating the minimum cost to the destination from each node, which can be found if not known _a priori_. This value is updated every time a node receives a packet from its neighbors. Two techniques are used to control the flood: (a) the transmit time difference is appended to the broadcast so that nodes with better estimates transmit first while suppressing duplicates or (b) if the receiving nodes estimate a higher cost than the transmitting node, the node only updates its cost and refrains from broadcasting.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Noisy Environment  
We deploy our VUCA system in a big shopping mall called WANDA Plaza, where it is noisy and many people walk around and pass the line connected from the acoustic source and the user. The real experiment scenario is shown in Figure 6c. The sound pressure of the noisy shopping mall is highly unstable. It is between 55 and 75 dB SPL, which is shown in Figure 7b. We conduct four sets of experiments in total:  
1. There is only one speaker broadcasting single high frequency acoustic signals of 20,500 Hz, \(L=5\) m, 10 m, 20 m, 30 m and \(\beta=0^{\circ},\phi=45^{\circ}\) and \(\theta=0^{\circ}\);
2. There is only one speaker broadcasting high frequency acoustic signals of 20,500 Hz and the common music, \(L=5\) m, 10 m, 20 m, 30 m and \(\beta=0^{\circ},\phi=45^{\circ}\) and \(\theta=0^{\circ}\);
3. There are two speakers in the environment. One of them is our target speaker. They broadcast different high frequency acoustic signals of 19,500 Hz and 21,500 Hz. To the target speaker, \(L=5\) m, 10 m, 20 m, 30 m and \(\beta=0^{\circ},\phi=45^{\circ}\) and \(\theta=0^{\circ}\);
4. There are two speakers in the environment. One of them is our target speaker. They broadcast different high frequency acoustic signals of 19,500 Hz and 21,500 Hz with the common music. To the target speaker, \(L=5\) m, 10 m, 20 m, 30 m and \(\beta=0^{\circ},\phi=45^{\circ}\) and \(\theta=0^{\circ}\).  
We gather the mean angular errors of all the experiments. Then, we plot the CDF of the angular error in the noisy environment in Figure 12b. The results of experiments show that almost all errors are less than 12 degrees for \(L\leq 30M\), which is acceptable for daily use.
# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 6 Conclusions  
The study focuses on processing and analyzing large amounts of Turkish data with text mining and creating a new sentiment dictionary that includes contexts specific to the cosmetics industry through sentiment analysis.  
In this study, which was conducted with 875,445 extensive Turkish textual data, the study's first research question (RQ1) can be answered positively by obtaining high performance results. In addition, machine learning approaches were used to evaluate the performance of the sentiment dictionary created from 65,378 words within the scope of RQ2, and it was concluded that the proposed system worked effectively and successfully. Creating a domain-specific Turkish dictionary has significantly contributed to the literature and has been implemented primarily to address the lack of sentiment analysis studies with Turkish texts. This study fills an important gap in the literature, as studies analyzing Turkish texts using untranslated texts have been less researched than analyses in other languages.  
Within the scope of RQ3, in the system created with the supervised approach, the data were divided into two, training and test data, and the performance of the system was measured using K-nearest neighbor, support vector machine, decision tree, random forest, and logistic regression algorithms. The algorithms used achieved high accuracy and metric rates in the context of product groups and categories, indicating that the system worked effectively. When the performances of machine learning approaches were evaluated, it was seen that the support vector machine algorithm gave the highest results in product groups. Thus, this study, which significantly contributes to the Turkish sentiment analysis literature, showed that machine learning methods can provide substantial results with Turkish e-commerce data.  
Conceptualization, C.G.O. and S.G.; methodology, C.G.O.; software, S.G., validation; S.G.; formal analysis, C.G.O. and S.G.; investigation, C.G.O.; resources, C.G.O., data curation, C.G.O. and S.G.; writing--original draft preparation, C.G.O.; writing--review and editing, S.G. visualization, C.G.O.; supervision, S.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
K-NNK-Nearest Neighbor  
Support Vector Machine  
Decision Tree  
Random Forest  
Logistic Regression
# Learning Part-Based Features for Vehicle Re-Identification with Global Context
## 3 Proposed Unified Framework for Global and Part-Based Local Features  
The structure of the part-based network is shown in Figure 1. Here, the images are pre-processed and passed through a backbone network to obtain a convolutional tensor T, which is then partitioned into a number of uniform parts by a pooling layer. The parts are then trained independently using an equal number of fully connected (FC) layers. During evaluation, the obtained parts before the FC layer are concatenated to form the feature vector, which is used for re-identification.  
In the images from the VRei dataset shown in Figure 2, we can see that there are certain parts of the image, such as background information and occlusion, which do not contain meaningful information and which can give rise to unnecessary, irrelevant information in the parts obtained from the image. To address this and improve image retrieval accuracy, we need contextual global information with respect to the entire image. However, this comes with added complexity. We illustrate this below.  
A convolutional or a fully connected layer contains trainable parameters. Let the number of trainable parameters in the backbone network be denoted as \(x\). For a part-based network with \(p\) parts, each part is accompanied by a pooling layer and a fully connected layer. Let the number of parameters for a fully connected layer be denoted as \(c\).  
Figure 1: Structure of the part-based network.  
So, for a part-based network, say \(W\) with \(p\) parts, the number of trainable parameters would be  
\[No.\,of\,Parameters(W)=x+pc \tag{1}\]  
For a global branch, an additional pooling layer and a fully connected layer are added. Let the branched network containing a part-based branch and a global branch be denoted as \(G\).  
\[No.\,of\,Parameters(G)=x+pc+c=x+(p+1)(c) \tag{2}\]  
If the number of global branches is \(g\), then the number of parameters of network \(F\) becomes  
\[No.\,of\,Parameters(F)=x+(p+g)(c) \tag{3}\]  
So, we can see that just by adding a single global branch, the number of trainable parameters increases. In order to overcome these shortcomings, we propose a framework in which the global features are accounted for without adding any trainable parameters to the network. In our present work, we propose a model where the learning of uniform horizontal parts is augmented by taking into account their relative importance with respect to the global feature without introducing any new trainable layer. The learning is induced and influenced by the similarity between the global feature and the partitioned features. In this light, we call our approach the global-local similarity-induced part-based network (GLSPNet). This approach does not add any extra trainable parameters other than those from the part-based network, and the number of parameters is static.  
\[No.\,of\,Parameters(GLSPNeT)=x+pc \tag{4}\]
## 3 Methods
### Preprocessing Techniques  
We perform data preprocessing during the data loading phase to ensure that the image instance segmentation model can efficiently process and learn from the input data. The process begins with reading each image and its corresponding labels. All images are resized to a standard input dimension of 640 \(\times\) 640 while preserving their original aspect ratios. If an image does not naturally fit the target dimensions, padding or scaling is applied to ensure consistency across the dataset and compatibility with model architecture.  
Next, data augmentation techniques are applied. The first enhancement method employed is Mosaic augmentation with a probability of 1.0. Mosaic combines four images into one large image, increasing the diversity of training samples. This augmentation increases the variety of training data, making the model more robust by presenting it with combinations of different input images. Furthermore, to further augment the data, the MixUp technique is applied with a probability of 0.15. This technique combines two images and their respective labels with weighted combinations, which helps to create more diverse data and improve the model's generalization ability. During the data augmentation process, we also perform color space augmentation using the HSV (hue, saturation, value) enhancement technique. The hue is adjusted by up to 1.5%, saturation is varied by up to 70%, and brightness is varied by up to 40%. These modifications simulate different lighting and environmental conditions, thereby improving the model's adaptability to real-world scenarios.  
Once augmentation is complete, label formats are converted to facilitate training. The original format, which encodes bounding boxes using normalized width, height, and center coordinates, is transformed into a format defined by the coordinates of the top-left and bottom-right corners. This format is more appropriate for detection and segmentation tasks. For segmentation specifically, polygonal annotations are converted into binary masks by filling the interior regions defined by the polygons. These masks may be down-sampled to reduce memory consumption during training.  
Finally, spatial transformations are applied to further increase data variability. Images are randomly flipped horizontally with a probability of 0.5, while vertical flipping is disabled. These transformations enable the model to learn orientation-invariant representations of objects.
## 5 Discussion  
Recently, many networks have been proposed and have made significant progress, even reaching human-level performance [10]. However, the study of the underlying mechanisms is still in its infancy [35]. In this study, different from the visualization of filter or feature map in intermediate layers, the mechanisms of network behavior in the higher level are explained. The effects of network structure and parameters on frequency resolution are quantitatively analyzed through the proximity experiment, providing a new method to explain the \"black box\" of deep networks from the perspective of auditory perception mechanisms.  
Figure 6: SI-SDRi (dB) as a function of TRT (ms) and AF (semitones), where **(a–i)** represent results of three well-trained networks (ConvTasNet, DPRNN, and DPTNet) with three different parameters, respectively. The dashed white curve indicates the temporal coherence boundary in the human auditory system.  
To our knowledge, it is the first demonstration that regardless of network structures and parameters, the same auditory separation mechanisms are learned by networks from unrelated nature sources. These networks based on CNN, RNN, or Transformer are purely statistical models and do not specifically model the auditory periphery and cortical processing or extract specific auditory attributes. Although only the proximity principle is explored in this study due to the limited space, the emergence of the harmonic effect line in the simultaneous experiment suggests that networks are likely to learn other auditory-like principles. In addition, the difference in network frequency-selective ability at low and high frequencies also illustrates the biological plausibility of the networks. It motivates us to think further about the deep networks and auditory system.  
Is the destination of deep separation networks to learn the same mechanisms as human beings? All three networks with different structures in our study have achieved this in some respects spontaneously. This result implies that the behavior characteristics of human auditory scene analysis can be understood as consequences of optimization for the selective listening task in natural environments. Humans may have chosen the optimal strategy under environmental constraints. When facing the same challenges, networks also tend to learn this optimal strategy from statistical regularities. [PERSON] and [PERSON] [36] illustrated that a deep network replicated some key characteristics of human sound localization. [PERSON] et al. [35] demonstrated that a deep network optimized to recognize speech and music replicated some human auditory behavior and predicted brain responses. In our study, in the separation task, networks also mimic the human auditory system spontaneously.
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 2 Internet of Things (IoT)  
The term \"Internet of Things\" or \"Internet of Objects\" has come to represent electrical or electronic devices, of varying sizes and capabilities, that are connected to the Internet, but excluding those primarily involved in communications with human beings, i.e., the traditional Internet. The scope of the connections is ever broadening beyond basic machine-to-machine communication (M2M) [2].  
IoT devices employ a broad array of networking protocols, applications and network domains [3]. The rising preponderance of IoT technology is facilitated by physical objects being linked to the Internet by various types of short-range wireless technologies such as: RFID, UWB, ZigBee, sensor networks and through location-based technologies [4]. The emergence of IoT as a distinctive entity was achieved, according to the Internet Business Solutions Group (IBSG), actually when more inanimate objects were connected to the Internet than human users [5]. According to this definition, this occurred in mid-2008. This is an accelerating ongoing process, especially with the rollout of Cisco's \"Planetary Skin\", the Smart Grid and intelligent vehicles [5]. IoT will make the impact of the Internet even more pervasive, personal and intimate in the daily lives of people.  
IoT devices are not currently strongly standardized in how they are connected to the Internet, apart from their networking protocols; however, this could be a relatively short-term inhibiting factor. IoT may be employed with added management and security features to link, for example, vehicle electronics, home environmental management systems, telephone networks and control of domestic utility services. The expanding scope of IoT and how it can be used to interconnect various disparate networks is shown in Figure 1[5].  
Figure 1: IoT can be viewed as a Network of Networks. Adapted from [5].  
Based on the type of use, IoT can be further categorized as Industrial Internet of Things (IIoT) and Consumer Internet of Things (CIoT), alternatively known as Human Internet of Things (HIoT), as shown in Figure 2:  
A generic IoT system typically consists of five components, which are:  
1. Sensors: which are used to mainly collect and transduce the data;
2. Computing Node: a processor for the data and information, received from a sensor;
3. Receiver: to facilitate collecting the message sent by the computing nodes or other associated devices;
4. Actuator: based on the decision taken by the Computing Node, processing the information received from the sensor and/or from the Internet, then triggering the associated device to perform a function;
5. Device: to perform the desired task as and when triggered.  
As an example, Figure 3 lists the 2018 top ten IoT segments, compiled by [PERSON] [7], who mined the Web to identify 1600 actual enterprise IoT projects, based on a strict definition.  
Figure 2: IIoT and CIoT, adapted from Moor Insights & Strategy’s Report [6].