esa-sceva/satcom-chunk-collection huggingface dataset:
# LSH Models in Federated Recommendation
## 2 Related Works
### Introduction of Locality Sensitive Hashing (LSH)  
LSH is a randomization algorithm for the nearest neighbour problem, which is significantly faster than the other existing methods, especially in high-dimensional space. It uses the hash method to hash the data from the original space into a new space, so that similar data in the original space have a large probability in the new space, while dissimilar data in the original space have a small probability of being similar in the new space.  
The basic idea of LSH is the following:  
1. To randomly select the hash function from the family of LSH functions and hash each data point into the hash table;
2. When finding the nearest neighbor, LSH only scans the points with the same hash index as the query point.  
Definition and Algorithmic Process are as follows:  
**Definition 1**.: _For \(r_{1}\leq r_{2}\) and \(p_{2}\leq p_{1}\), a hash family \(H\) is (\(r_{1},r_{2},p_{1}\), \(p_{2}\))-sensitive if for all \(x,y\in S^{d-1}\)._  
\[\text{if }\|x-y\|_{2}\leq r_{1},\text{ then }\Pr_{H}[h(x)=h(y)]\geq p_{1}\]  
\[\text{if }\|x-y\|_{2}\geq r_{2},\text{ then }\Pr_{H}[h(x)=h(y)]\leq p_{2}\]  
If the hash collision probability of a point that is close is greater than that of a point that is far away, it is a locality sensitive hash, that is, t The hash function is sensitive to the distance between data points, locally close points can be hashed into a bucket as much as possible. According to the above definition, two points are considered close if the distance is not greater than \(r_{1}\); if the minimum distance is \(r_{2}=r_{1}*c\) (\(c>1\)), \(c\) is the distance coefficient between the farand near points.  
The quality of the hash function is defined by two key parameters: \(p_{1}\) represents the hash conflict probability of proximity points, while \(p_{2}\) represents the hash conflict probability of distance points. The difference between these two probabilities determines the sensitivity of the hash function to distance in changes and can usually be expressed as a function of the distance gap.
## 3 Results
### Experimental Data and Result Visualization  
#### 3.4.1 Experiments Based on the German-Street Dataset  
Table 1 compares the evaluation results on the German-Street dataset. IoU and Recall for the background class are generally high, while those for the road class are lower, reflecting the challenges of binary semantic segmentation with imbalanced samples, where models tend to prioritize background regions. The CCNet model, based on criss-cross attention, performs relatively poorly overall but achieves a low omission rate for roads. Other methods demonstrate balanced performance, though PSPNet converges more slowly. U-Net outperforms other methods in accuracy, convergence speed, and parameter efficiency but falls slightly behind our method. Overall, our method achieves the highest scores across all four metrics, demonstrating superior performance and effectively balancing segmentation between background and roads.  
Figure 4 presents a visual comparison of results from different models on the German-Street dataset. Red boxes highlight areas where the ground truth fails to correctly label roads, such as misidentifying rooftops and parking lots as roads or missing roads due to occlusion. In contrast, our method accurately identifies these areas, effectively mitigating the impact of inter-class similarity and complex backgrounds on road extraction. Furthermore, our method achieves notable improvements in road continuity and completeness compared to other models.
## 3 Proposed Architecture Overview  
### Request-Response Model  
In this study, we define a new request-response model that consists of a smart broker designed to manage subscriber requests for topics and deliver them with a quality score (see Figure 1). There are two types of requests in this model, as follows:  
* _Direct Request-Response_: Like traditional PS models, the subscriber asks for data for a topic (\(\tau\)), and the smart broker responds with the data. Along with the data, the broker also sends a quality score \(q_{\tau}\) [0, 1]. It is expected that the broker, if based on a blockchain, will provide reliable, unbiased, and quality information.
* _Substitution Request-Response_: A special case arises in this model when the requested data are not present at the current time. As the broker is smart and based on blockchain, it can recommend some other topics to the subscriber that are similar to the topic originally requested. These substitution recommendations may also be given if the quality of the data for the \(q_{\tau}\) drops dramatically. It is then up to the subscriber to decide on a recommended topic. Once the subscriber chooses a substitute topic \(\tau^{\prime}\), further communication follows the direct request-response as before.  
To achieve this model in a PS application, several new procedures have been developed, including protocols for publishers to join the PS system, assessment of the data quality, and running the processes from within a blockchain.
## 4 Proposed CLCQ Protocol
### Packet Structure Design  
The packet structure design in this protocol is shown in Figure 7.  
RTS: This packet is used by sending nodes to request channel access from their neighbors and carries a test data segment for calculating the BER. After receiving the RTS packet, the neighbor node calculates the BER through the test data segment and writes the result to the corresponding field of the CTS packet.  
CTS: Neighbors send CTS packets to the sending node simultaneously at the beginning of the slot to confirm that they have received the RTS packet. To avoid multiple CTS packets colliding at the sending node, each neighbor node uses different subcarrier frequency bands to transmit CTS packets.  
DATA: In addition to the payload to be transmitted, it also contains routing information such as the destination address and receiving address, as well as additional information such as the V-value and residual energy.  
ACK: After the receiving node successfully receives the DATA packet, it sends an ACK packet to confirm receipt of the data and provides feedback about its own status information to the sending node.
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Fusion of User Comments and News Content
#### 3.5.1 User Comment and Image Fusion (UCI)  
We adopt a cross-attention mechanism to fuse news images and user comments, enabling the model to identify visual elements that users are discussing or questioning:  
The comment-to-image attention mechanism involves several sequential transformations, each serving a specific purpose in establishing cross-modal relationships.  
The query transformation extracts attention queries from comment features:  
\[Q_{CI}=\text{Linear}_{Q}(F_{C}) \tag{12}\]  
where \(Q_{CI}\) represents the query matrix derived from comments, \(F_{C}\) denotes the comment feature representations, and \(\text{Linear}_{Q}\) is a learnable linear transformation layer that projects comment features into the query space.  
The key transformation maps image features to attention keys:  
\[K_{CI}=\text{Linear}_{K}(F_{V}) \tag{13}\]  
where \(K_{CI}\) is the key matrix generated from visual content, \(F_{V}\) represents the visual feature representations, and \(\text{Linear}_{K}\) is a learnable linear transformation that projects image features into the key space.  
The value transformation converts image features into attention values:  
\[V_{CI}=\text{Linear}_{V}(F_{V}) \tag{14}\]  
where \(V_{CI}\) denotes the value matrix from visual features, \(F_{V}\) represents the same visual feature representations, and \(\text{Linear}_{V}\) is a learnable linear transformation that projects image features into the value space.  
The attention weight computation applies scaled dot-product attention:  
\[A_{CI}=\text{softmax}\Bigg{(}\frac{Q_{CI}K_{CI}^{T}}{\sqrt{d_{k}}}\Bigg{)} \tag{15}\]  
where \(A_{CI}\) represents the normalized attention weights, \(Q_{CI}K_{CI}^{T}\) computes the dot product between queries and keys, \(d_{k}\) is the dimensionality of the key vectors used for scaling, and softmax ensures attention weights sum to one.  
The final attended feature computation aggregates visual information based on attention weights:  
\[F_{CI}=A_{CI}V_{CI} \tag{16}\]where \(F_{CI}\) represents the final comment-informed visual features, \(A_{CI}\) are the attention weights, and \(V_{CI}\) are the value representations that are weighted and aggregated.
## 2 Material and Methods
### Two ET Models Based on ANN
#### 2.2.1 ANN-PM Model  
We trained an ANN-PM model based on ANN and PM equations to estimate ET. ANN is a commonly used ML method, which has been widely used in estimating ET. It consists of a large number of nodes, called neurons, which are connected to each other. The typical structure of ANN used to estimate ET is shown in Figure 3.  
ANN contains three layers: the input layer, hidden layer, and output layer. The input layer is responsible for receiving input data, the hidden layer constructs the relationships between the input and output, and the output layer outputs the predicted target values. The variables input to ANN in this study includes Ta, precipitation (P), solar radiation (SW), Ca, vapor pressure deficit (VPD), normalized difference vegetation index (NDVI), and near-infrared reflectance of vegetation (NIRv). In the variables we used, Ta, SW, Ca, and VPD can affect canopy conductivity from different aspects [50]. The consideration of P is mainly to represent the influence of canopy interception on ET. Thus they are selected to model _Gs_. There is an interaction and mutual influence between the transpiration and photosynthetic capacity of plants, and ET is dominated by transpiration. The vegetation index, NIRv, is considered in order to better reflect the impact of the photosynthetic capacity of the surface vegetation on evapotranspiration. NIRv is able to characterize seasonable variations in canopy scale photosynthesis rate without additional environmental factors that are conventionally used to constrain photosynthesis [34]. These variables are used to train ANN to the _Gs_ model. Referring to [PERSON] et al. [19], we used the ANN model to model _In(Gs)_ rather than _Gs_ because the logarithmic form can effectively reduce the effect of errors in _Gs_ calculated from the observations. Finally, the logarithm of _Gs_ obtained by ANN simulation is converted to _Gs_, and then the converted _Gs_ is input into the PM equation to calculate ET. Here, _Gs_ values used to train the ANN model were calculated from the observed ET along with the inverted PM equation [51]. In order to avoid over-fitting, the network model is repeatedly trained, where the number of hidden layers ranges from 1 to 10, and the number of neurons in each layer increases from 1 to 128, with an interval of 8. Then, we choose the optimal ANN structure as the best model.
# In Vitro Evidence of Stating' Protective Role against COVID-19 Hallmarks _Comptes_ [PERSON]
## 3 Results  
### Statins Modulate ACE2 Receptor and TMPRSS2 Expression  
Aiming to analyse the toxicity of statins, we examined the effect of ATS and PVS, lipophilic and hydrophilic statins, respectively, on cell viability in different cell lines expressing different levels of ACE2 receptor. For the range of concentration here examined (1 uM-20 uM), both ATS and PVS have shown a safe profile, without toxicity, in both A549-hACE2, overexpressing ACE2 receptor, thus highly permissive to SARS-CoV-2 infection and CaCO2 cell lines, constitutively expressing normal ACE2 receptor levels (Supplementary Figure S1A,B). In normal human lung fibroblast MRC5 cells, resistant to SARS-CoV-2 due to the low expression of ACE2 receptor [29], only ATS showed a significant cytotoxic effect, particularly pronounced at the highest concentrations (10 uM and 20 uM) (Supplementary Figure S1C).  
We next evaluated the effect of the compounds on ACE2 receptor and TMPRSS2 expression, in the highly permissive A549-hACE2 cells. As expected, after 24 h of treatment both ATS 5 uM and PVS 10-20 uM upregulate ACE2 receptor protein expression, but the strongest effect has been obtained after 48 h of treatment, where the compounds induce a strong upregulation (Figure 1A,B). After the binding with ACE2 receptor on host cells, SARS-CoV-2 spike proteins are activated by host cell protease TMPRSS2, responsible for S protein priming. The TMPRSS2-mediated cleavage at the S1/S2 site is required for viral entry into human lung cells [2; 30]. We then analyzed the effect of the two statins on TMPRSS2 expression. After 24 h of treatment with both ATS and PVS, the expression of TMPRSS2 results markedly increased. However, prolonged time of treatment (48 h), with the highest concentrations of ATS (5 uM) and PVS (20 uM), produced a slight but not significant decrease of TMPRSS2 (Figure 1A,C). Interestingly, Western blot analysis allows us to detect an additional band of about 32 kDa which, based on the literature, corresponds to serine protease catalytic domain, directly involved in cell surface protein interaction, which is a product of auto-cleavage [31; 32; 33]. In this model, the cleaved domain of TMPRSS2 results in significant downregulation starting from 24 h of treatments with both the compounds and prolonged after 48 h in a dose-dependent manner (Figure 1A,C).
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 3 Materials and Methods
### Sensors
#### 3.2.1 Chest Band  
Measurement of RR is performed by a compact physiological monitoring device, Zephyr BioModule 3 ([[https://www.zephyranywhere.com/system/components](https://www.zephyranywhere.com/system/components)]([https://www.zephyranywhere.com/system/components](https://www.zephyranywhere.com/system/components)), accessed on 12 June 2023), which can be attached to a strap that incorporates respiration detection sensors. The strap has a pressure sensor pad that detects the expansion of the rib cage measuring the BR. Moreover, the chest band also acquires HR and body temperature. The BioModule's battery can last about 20 h on average. The sensor data are processed internally in the device and are available to a connected device via Bluetooth. RR range is between 0 and 120 breaths per minute. HR is assumed to be stabilized by a proprietary algorithm that provides a maximum deviation of 10 bpm with \(\pm\)3 bpm accuracy while running. The data from the chest band are sent to mobile phone and relayed to an embedded computer for its data integration. Although the different sensors involved provide different sampling rates (i.e., ECG at 250 Hz, 25 Hz for RR, and 1 Hz for temperature), the transmission frequency to the framework for each value is 1 Hz (see Table 1).
# Asian Affective and Emotional State (A2 ES) Dataset of ECG and PPG for Affective Computing Research
## 5 Experimental Results and Discussion  
In order to validate the proposed dataset, an emotion recognition experiment was conducted using machine learning and deep learning algorithms. The emotion recognition was performed using each modality: ECG and PPG. From the emotion types and intensities labelled by the participants, the emotions were reclassified according to the arousal classes of high and low, valence classes of high and low, and the dimensional classes of high arousal and high valence (HAHV), high arousal and low valence (HALV), low arousal and high valence (LAHV), and low arousal and low valence (LALV). Classifying emotions according to arousal and valence is commonly adopted in emotion recognition works, as seen in [68]. Before the classification, the data were split into subsets for training and testing. The ratio of the training and testing was 70 to 30%.
## 3 Results  
### Features in Soil Images  
Figure 2 shows samples of soil surface images acquired at sampling points A, B, and C, along with texture, WC, and BD. Since the in situ soil surface image was taken, obstacles such as roots were also photographed on the surface layer. Depending on the texture of the soil, the color appears differently, and the color of the soil of the same texture appears differently depending on the conditions of WC and BD. At low BD, a distribution of agglomerated soil particles on the soil surface was observed. In soils with high BD, compacted soil solids were distributed on the surface and some cracks were observed. Roots that are naturally mixed with soil or cracks caused by compaction can be regarded as obstacles in soil surface images. However, as they are common elements in actual natural environments, we treated them as observation noise and included soil surface images containing them.  
Table 3 summarizes the features extracted from soil surface images in this study. A total of 18 features were extracted and used as input data for the regression model. These features were used as independent variables for multiple regression analysis and input variables for DNN regression models.  
Figure 2: Sample soil surface images acquired from sampling point A, B and C.  
Figure 3 shows coefficient of correlation (\(R\)) between soil properties (WC, BD) and image-based features. WC and BD exhibit different correlation patterns with the various features. WC has a strong relationship (\(|R|>0.75\)) with Value and Gray. Additionally, WC has a moderate relationship (\(0.5<|R|<0.75\)) with Skewness, Contrast, Dissimilarity, Homogeneity and Correlation. BD has a moderate relationship with Kurtosis. In terms of comparing WC and BD, it can be seen that WC has a stronger relationship with image-based features than BD.
## 2 Theoretical Analysis
### Numerical Simulation  
In order to validate the theoretical analysis and set up the corresponding experimental device, numerical simulations were implemented. In the simulations, the effect of \(\Delta\phi\) is the main consideration for the output voltage of the sensor. In this work, \(I_{0}=1\), \(\phi_{\rm s0}=0.5\), \(\phi_{0}=\pi\), and \(\rm k=1\), which represents the conversion coefficient between the light intensity and the output voltage in the sensing system. The frequency of ultrasonic waves was 50 kHz, the length of the delay coil was 1000 m, and \(c^{\prime}=2.045\times 10^{8}\) m/s (the effective refractive index of a single-mode quartz crystal fiber is \(\rm n_{eff}=1.467\)).  
Figure 2 shows the waveform of the output voltage and its corresponding frequency spectrum in initial status. It can be seen that the frequency was 100 kHz, while the frequency of 50 kHz could not be observed, and the amplitude of voltage was relatively small. Figure 3 is the waveform of the output voltage and its corresponding frequency spectrum when a bias phase of \(\Delta\phi=\pi/2\) was introduced into the sensor. It can be seen that the waveform presents a regular sinusoidal characteristic, the amplitude of voltage was increased more than six times, and the frequency spectrum shows that the frequency of 50 kHz was detected accurately. It is also evident that there is a third harmonic component, but it had little effect on the fundamental component. From Figures 2 and 3, it can be seen that the voltage was increased effectively, which means the phase sensitivity of the sensor improved, and the results of the theoretical analysis were validated.  
To investigate the influence of phase modulation on phase sensitivity, an interval of \(\pi/30\) within \(\Delta\phi\in[0,\pi]\) was adopted in the numerical simulations. The relationship between the amplitude of the output fundamental voltage and \(\Delta\phi\) is shown in Figure 4. It can be seen that there is a characteristic of sinusoidal function between the bias phase and the output fundamental voltage. The phase sensitivity was the largest when the bias phase was \(\Delta\phi=\pi/2\) and it was the smallest when the bias phase was \(\Delta\phi=0,\pi\).  
Figure 4: Relationship between the amplitude of the output fundamental voltage and \(\Delta\phi\). The red scatter points represent the simulative values, and the blue curve is the corresponding fitting curve.  
Figure 3: Output voltage with a phase modulation of \(\Delta\phi=\pi/2\) in the numerical simulation. (**a**) Output voltage waveform; (**b**) corresponding frequency spectrum.  
Figure 2: Output voltage signal without phase modulation in the numerical simulation. (**a**) Output voltage waveform; (**b**) corresponding frequency spectrum.
# New Vector-Space Embeddings for Recommender Systems
## 3 Proposed Approach
### Prediction  
In this formulation, we seek to predict the similarity scores between a user \(u\) and item \(i\). An example is the prediction of how user \(u\) would rate item \(i\). This would be accomplished by observing the pattern of ratings of the concerned user and other similar users and items. This problem corresponds to Term 2 in Equation (2), which tackles user-item similarity terms, where vectors \(x_{i}\) correspond to the items, and vectors \(x_{u}\) correspond to the users.  
The system uses a corpus of users' transactions where a transaction specifies that user \(u\) rated item \(i\) with the rating \(r_{ui}\). The ratings are typically available as a number on a scale of 1 to 5 or 1 to 10 (which are translated in a normalized fashion to be in the range from 0 to 1), or they are \"like\" and \"dislike\" (for which \(r_{ui}\) is taken as 1 and \(-1\) respectively). Available ratings are used in the error function, and missing ratings (which is the great majority) will be estimated after training, using the dot product between the corresponding vectors. The error function is given by:  
\[E=\sum_{u=1}^{U}\sum_{i=1}^{N}w_{ui}\Big{[}x_{u}^{T}x_{i}-r_{ui} \Big{]}^{2}\] \[\text{subject to}||x||=1,\forall u,i, \tag{6}\]  
where:  
* \(x_{u}\) is the user's vector.
* \(x_{i}\) is the item's vector.
* \(r_{ui}\) is the rating user \(u\) assigned to item \(i\), fed to the system as the rating scaled value where the maximum value is 1.
* \(w_{ui}\) is a weighing coefficient that constraints the system, which is designed to put different emphases on different ratings terms. In most practical cases we take \(w_{ui}\) as constant or one, unless certain ratings are deemed more influential.
* \(U\) is the number of users.
* \(N\) is the number of items.  
The algorithm to minimize this error function (6) is similar to that described in the last subsection. The proposed approach will therefore have the vectors for the users and the vectors for the items side-by-side in the same space. A vector pertaining to a user will be close to one pertaining to an item if the user likes the item. Like-minded users will also be drawn together, even though not explicitly trained for that. This is because common item preferences will act as the glue that pulls the users' vectors together. The same applies for vectors of similar items.