esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hardware Implementation  
Figure 14 represents the datapath of the proposed RBER estimation method, which is embedded in the ECC framework. When implemented with 40 nm technology library, the modules only occupy 0.039 mm\({}^{2}\) in total at 333 MHz clock speed at worst process corner condition, which is a very low cost for the NAND flash controller. For data programming, the input message is firstly inserted with a true-value sequence, which is for RBER estimation. The message is then encoded with the QC-LDPC code, and the codeword is interleaved for alleviating the unbalanced error distribution on pages. Afterwards, the interleaved codeword is modulated to corresponding voltage levels before it is finally programmed into the cells. For data reading, the voltage charged in cells is firstly demodulated to corresponding codes, and these codes are then deinterleaved to the original orders. Subsequently, the correct known true-value sequence is filled back into the restored codeword, and the errors are counted at the same time. The RBER estimation module will wait until collecting \(N\) frames to calculate a \(\overline{BER_{est}}\) and the collection can be done during the normal work process without the extra time required. The codeword is then decoded with the help of \(\overline{BER_{est}}\) to select a proper decoding status. Meanwhile, the enhanced strategies are applied to improve the error performance of the decoder and speed up the converge of decoding. Finally, the true-value sequence is removed before the message is output.  
Figure 13: Frame error rate vs. number of iterations in the NAND flash memory, where the (10,800, 8400) QC-LDPC code is considered. In fact, the last iteration drops the FER to 0, but we set it to \(10^{-5}\) for the plotting purpose, indicating that the FER less than 1/10,800.
# Time Classification Algorithm Based on Windowed-Color Histogram Matching
## 3 Proposed Time Classification Algorithm
### Time Classification
#### 3.2.3 Weighted Histogram Comparison  
Brightness of the acquired image depends on the color band during the day. We observe that some specific color components are important to separate the time period. For example, blue and green components can give good separability between \"dawn\" and \"day\" periods. However, a red color is not a good indicator to verify them, as shown in Figure 3. Therefore, we design a weighted matching scheme between the reference color histogram and the input image histogram as the following:  
\[d(H_{1},H_{2})=\sum_{i}w_{i}d_{i}(H_{1}^{i},H_{2}^{i}) \tag{7}\]  
where \(d_{i}\) is defined as the \(i\)-th band difference in the above, \(w_{i}\) is a tuned weighting factor for each color band. We set this weighting factor \(w_{i}\) through the experiments.  
With the above weighted histogram mechanism, we use a hierarchical classification structure: (1) First, the input image is classified into \"day (dawn)\", and \"night\" classes based on the windowed-color histogram and weighting factor, (2) After that, the input image, which is in the \"day (dawn)\" class, is separated into \"day\" and \"dawn\" classes based on the refined windowed-color histogram and weighting factor.
# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
## 4 Experiments and Results
### Experimental Setup  
#### 4.1.1 Default and TrivialAugment  
For completeness, we include in our experiments a \"standard\" augmentation strategy, a strategy based on augmentations often manually selected by researchers, we dubbed _DefaultAugment_ and use it as our baseline. This default augmentation strategy is inspired by semantic segmentation literature [28; 29] and uses the following standard augmentations: horizontal flipping \((p_{flip}=0.5)\), random rotation \((range=[-45,45])\), random scaling \((range=[-0.35,0.35])\), where \(p_{flip}\) represents the probability of applying this particular augmentation. Furthermore, we extended another recent method, TrivialAugment (see Figure 2a), for semantic segmentation and integrated it in our experiments.
###### Abstract
For example, narrow fringing or linear habitats and benthic features that are less than 60 m in their smallest dimension are largely missed by publicly available 30 m resolution global datasets. Conversely, higher spatial resolution imagery from private satellite companies can be expensive to acquire and process; hence, these products are typically used for mapping smaller geographic areas. Though there has been some success in other geographies using acoustic methods to map benthic habitats (e.g., side scan and multibeam sonar) [23; 24; 25], these data are not readily available in the Caribbean and would be time-consuming and expensive to procure for very large areas. However, as remote sensing technologies and classification methods continue to improve, the ability to map larger areas and quantify finer-scale parameters that integrate ancillary datasets, such as geographic zones, bathymetry, and benthic composition, has increased [26; 27; 28; 29; 30]. To fill the data gap and address the need for more consistently mapped, detailed benthic habitat products across broader scales, a first-of-its-kind high-resolution (4 m) thirteen-class regional benthic habitat map was developed for 203,676 km\({}^{2}\) of shallow habitat (<30 m depth) across the Insular Caribbean using an object-based classification approach. The map was created using the PlanetScope (PS) Dove Classic satellite constellation and utilized a standardized benthic habitat classification scheme that was performed on a composite of 38,642 scenes. Each scene was selected for water clarity and optimal water column properties with low sunlight and water surface roughness. When compared to global benthic datasets, the object-based method using the PS imagery captured more detailed, ecologically meaningful shapes and classes that were derived from both spectral and non-spectral attributes of the imagery, including bathymetry, geomorphic zones, and corresponding spatial and contextual information (Figure 1). Thousands of GPS-referenced field video transects, drone imagery, and scuba diver data collected throughout the region were used to train the classification algorithm and assess the accuracy. Local experts throughout the region were consulted to manually adjust and refine the final product. Compared to global products, this innovative, scalable approach to coastal ecosystem mapping and monitoring delivers a much higher spatial resolution and regionally consistent map product and ecological baseline. For many Caribbean countries that lack these data, this product represents the basis for more strategic and targeted conservation spatial actions, such as the improved design and implementation of more effective marine protected area networks and marine spatial plans, a baseline for monitoring biophysical conditions and system changes, such as post-storm damage assessments, and the provision of more accurate input data for ecosystem service models and benefit calculations.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 4 System Model
### DRAMBR Assumptions  
To ensure a realistic communication network, our study relies on assumptions about vehicle capabilities, behaviors, and system conditions. These assumptions reflect practical and feasible conditions in real-world vehicular networks. The key assumptions are outlined below:  
* Pseudonymity: Vehicles interact via pseudonyms and communicate over an anonymous network.
* Connectivity setup: In the OE phase, vehicles communicate out of range of network connectivity, hence, the periodic synchronization with the network to obtain the latest CRLs and RVs is limited.
* Reputation setup: Each vehicle is initialized with an RV. The RV will increase with positive behavior and decrease with misbehavior.
* Independent behavior: Each vehicle can behave either honestly or misbehave. The vehicle's behavior is independent of others such that the actions of vehicle \(V_{i}\) do not influence the behavior of vehicle \(V_{j}\) for \(i\
eq j\).
* Communication range: Vehicles communicate using Dedicated Short-Range Communication (DSRC) technology that has been developed specifically to provide reliable and effective communication in a range of (300-900 m), ensuring only nearby vehicles can interact even without connectivity.
* OBUs detection: Each vehicle is equipped with On-Board Units (OBUs) that detect any abnormal activity or irregular behavior.
* Messages checking: Each vehicle has a mechanism to check its outgoing messages and detect any misbehavior before they are transmitted.
* Limited misbehavior reporting: Not all observed activities of misbehavior result in generating and transmitting the MR. The decision-making process regarding whether a vehicle generates an MR based on observed misbehavior is specific to the vehicle's implementation.
## 3 Related Work
### Mediation-Based User Modeling  
Attempts of bridging user modeling in various systems require conversion of user modeling data between various representation, applications and domains, and exploiting semantically-enhanced knowledge bases [10]. These has been done using mapping techniques of user models [6, 28, 29, 30, 31], and recently using machine learning techniques [10].  
[PERSON] _et al._[28] used mediation techniques based in multi-agent technologies. In their approach the agents map help requests to possible service providers. [PERSON] _et al._[6] provided mediation and integration in recommender system domain. [PERSON] _et al._[29] developed a Generic User model Component (GUC) using Semantic Web technologies to provide mediation between web applications. A human designer constructs a rule-based mapping schema. Mappings are not defined automatically. In 2005, [PERSON] [30] proposed an open architecture for sharing partial user models. They provided a well-defined conceptual basis of the conversion mechanism. More recently, [PERSON] _et al._[7] presented a solution with high flexibility representing user models and providing semantic mapping of user data from heterogeneous sources. However, to take part in the interoperability process, every provider must comply with a standard format for exchange and maintain a sharable user model which includes fragments of it as RDF statements.  
A semantic layer to improve mapping among application was presented in [31]. The authors showed a flexible user modeling system based on the concept of mapping among applications. They focus in management of user profile data propagation. Their G-Profile formalized the correct identification of mappings between applications; it is an abstract and flexible protocol.  
Each profile provider builds a specific user model, but they do not easily comply with a standard format, vocabulary or technique to enable user modeling interoperability. On the other hand, mappings are defined at different granularity, and it is not always implemented by a fully automated process.
## 3 Results and Discussion
### Sensory Analysis  
Figure 4 illustrates the sensory characteristics in terms of overall acceptability, consistency, attractiveness, aftertaste, appearance, color, flavor, taste and texture of pasta samples with different percentages of carob flour.  
It can be observed that the pasta with a medium carob powder, P4 CP, with the addition of 4% of carob powder was the most preferred due to its attributes, with a mean value of 8.62 \(\pm\) 3.8 out of 9 points. This indicates that the consumer prefers a small amount of carob powder in pasta. The addition of a higher amount of carob powder was rejected; samples with 6% carob flour were undervalued by panelists. Based on texture, taste, color and consistency, sample P4 CP was the most appreciated, being ranked with the highest value out of a possible 9 points. This was very close to sample P6 CP, which had flavor, color and texture scoring 8 \(\pm\) 7.3 points. Samples with 6% carob powder addition received the lowest scores due to the affected attractiveness and appearance. The sensory analysis is an important parameter to evaluate the adaptation of several pulses' capacity in fresh pasta and if the addition of novel constituents in the structure of products influences the properties of final products and consumers' perception.  
Figure 3: Crushing resistance of pasta samples with the addition of carob powder. PM: control sample; P2 CP: sample with 2% (\(w\)/\(w\)) carob powder; P4 CP: sample with 4% (\(w\)/\(w\)) carob powder; P6 CP: sample with 6% (\(w\)/\(w\)) carob powder.  
It has been demonstrated that the addition of gluten-free flour to a pasta recipe alters the gluten network and reduces the overall structure of the pasta, resulting in a negative effect on sensory properties [53; 55]. Furthermore, it may result in increased solid substance losses from pasta in cooking water. Darker color was observed when pasta was supplemented with 10% carob fruit which negatively influenced consumers' perceptions [41]. Consumers rejected products with excessively high concentrations due to their dark color and bitter taste [58]. According to [PERSON] and [PERSON], 2020, the highest values in terms of sensory analysis were determined in the 20% carob flour addition to pasta [41].
## 3 Results and Discussion
### Ablation Experiment Results and Discussion
#### 3.1.3 Fine-Tuning Transfer Learning Ablation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **IoU (\%) \(\uparrow\)** & **R (\%) \(\uparrow\)** & **P (\%) \(\uparrow\)** & **F1 (\%) \(\uparrow\)** & **Loss \(\downarrow\)** & **Params (M) \(\downarrow\)** & **Batch Time (S) \(\downarrow\)** \\ \hline not FTTL & 59.62 & 73.18 & 74.09 & 73.63 & 0.3212 & 6.38 & 123 \\ not frozen & 70.00 & 78.03 & 83.25 & 80.56 & 0.0283 & 6.38 & 114 \\ freeze the \(L_{1}\) & 67.58 & 77.06 & 80.51 & 78.74 & 0.0324 & 6.38 & 105 \\ freeze the \(L_{1}\) and \(L_{2}\) & 62.49 & 73.15 & 77.05 & 75.05 & 0.0382 & 6.38 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of different feature weights.
## 3 Methodology
### Multi-Scale Feature Pyramid Network  
The overview architecture of the MSFPN is proposed as shown in Figure 2. The overall structure consists of two paths: one for extracting deep emotional features from bottom to top and another for merging multi-scale emotional features from top to bottom.  
In the bottom-up pathway, as shown in Figure 3, the MSFPN includes a series of ConvBlock layers to learn and protrude the fine-grained emotion-related features in the different levels. Each ConvBlock contains two CNN layers for feature learning and is mixed with an improved convolutional self-attention (detailed in Section 3.2) layer for local correlation learning.  
Specifically, there are three groups \((X,Y,Z)\) of bottom-up pathways with different kernel widths (\(kw=3,5,7\)), each extracting three levels of semantic features at different time scales. Given a speech dataset with n utterances, the characteristics of Group1 \((x_{1},x_{2},x_{3})\) are calculated using hierarchical ConvBlock layers shown in Figure 3. After each layer, the number of output channels is reduced by half. To be specific, the output channels for \(x_{1}\) are 1024, for \(x_{2}\) are 512, and for \(x_{3}\) are 256. The deep features of Group2 (\(y_{1}\), \(y_{2}\), \(y_{3}\)) and Group3 (\(z_{1}\), \(z_{2}\), \(z_{3}\)) are computed in the same way. The difference is that \(y_{i}\) and \(z_{i}\) can receive the previous fine-grained acoustic emotional features from the above groups by a forward fusion to enhance the local semantic understanding of phrases. Typically, the forward fusion here is adding function.  
In the top-down pathway, a backward fusion mechanism is introduced to combine semantic feature maps with high-resolution acoustic features. As shown in Figure 4, for  
Figure 3: Bottom-up pathway, where \(kw\) denotes different kernel widths, and \(CSA\) denotes convolutional self-attention.  
deep features \(F_{i}=[x_{i},y_{i},z_{i}]\), firstly, calculate the attention score \(\alpha_{i}\) for the \(i\)-th layer using Equation (1). Then, multiply \(\alpha_{i}\) with features from various depth levels to extract the most pertinent emotion features for the \(i\)-th layer. Lastly, sum these products to derive the feature \(G_{i}\) for the \(i\)-th layer.  
\[\alpha_{i}=softmax(W_{2}\times Tanh(W_{1}F_{i}^{T})) \tag{1}\]  
\[G_{i}=\sum_{i=1}^{3}\alpha_{i}\times F_{i} \tag{2}\]  
where \(G_{i}\) is the multi-scale deep features of the \(i\)-th level in the MSFPN, and \(W_{1}\) and \(W_{2}\) are trainable parameters.  
Then, the multi-scale deep feature G is upsampled by a transposed convolution function, and the low-level features add high-level semantic information. We acquire the deep features that contain adequate semantic information for understanding the emotional expression of each utterance and a high resolution with the sequential structure for temporal dependence learning.
## 6 Security Analysis
### Informal Analysis
#### 6.3.6 Trace Attack and Privacy-Preserving  
The vehicle \(v_{i}\) does not send its real identity \(ID_{i}\) over an open channel. The vehicle generates the pseudonym identity \(HID_{i}=h(ID_{i}||PW_{i}||a_{i})\). And also \(RSU_{j}\) uses the \(RA_{j}\) instead of real identity \(ID_{RSU_{j}}\). Moreover, as above mentioned Sections, \(\mathcal{A}\) cannot impersonate legitimate vehicles and also cannot generate a validated session key. Therefore, the proposed protocol provides the privacy-preserving. \(v_{i}\) and \(RSU_{j}\) communicate the information using the session key without pseudonym identities. Thus, we can say that the proposed protocol can prevent trace attack.
## 3 Flame
### Flame Analysis
#### 3.1.2 Spreading Mechanisms  
To the best of our knowledge, no dropper component of Flame was ever made available to the research community. It is even possible that no dropper was identified at all. Thus, we do not know how Flame infects the first computer in a network. However, once infiltrated, Flame can spread locally by various methods. First of all, it uses the same print spooler exploit (MS10-061) and LNK exploit (MS10-046) as Stuxnet. Second, it can turn an infected computer into a proxy for Windows Update. As a result, computers in the local network try to obtain updates for Windows from the infected computer, which sends them the malware instead. In order to be successful, the installer of the malware must be digitally signed such that it appears to be created by Microsoft. For this purpose, the attackers created a private signing key and a fake certificate for the corresponding public signature verification key that appears to be a valid certificate issued by Microsoft. We will explain how the attackers managed to obtain such a fake certificate in Section 3.2.
# Sliding Balance Control of a Point-Foot Biped Robot Based on a Dual-Objective Convergent Equation
## 2 Dual-Objective Convergence Equation for the Biped Robot
### Convergence Equation for Sliding  
Suppose that the robot slides at a high speed on the ground with constant friction \(\mu\). A flywheel inverted-pendulum model is established to mathematically describe the trajectory of the CoM. As shown in Figure 3, point \(C=[x_{com},y_{com}]^{T}\) denotes the coordinates of the CoM, m is the total mass of the robot, and \(L_{c}\) is the total angular momentum of all rigid bodies around the CoM. The magnitude of the gravitational action is \(mg\). \(F_{x}\) and \(F_{y}\) are the x-axis and y-axis components of the contact force.  
Because the rate of change of the centroidal angular momentum depends on the sum of torque around the CoM generated by the contact forces, the mathematical expression of the flywheel inverted-pendulum model is established as  
\[\dot{L}_{c}=y_{com}F_{x}-x_{com}F_{y}. \tag{6}\]  
Figure 3: Flywheel inverted-pendulum model of the biped robot.  
According to [PERSON]'s law of friction, the contact force of the robot during sliding is  
\[F_{x}=-\mu F_{y}. \tag{7}\]  
\(F_{y}\) can be expressed as the acceleration of the CoM:  
\[F_{y}=m(y_{com}+g)=m\big{(}J_{y}\big{(}q_{p}\big{)}\dot{q_{p}}+\dot{J}_{y}\big{(} q_{p},q_{p}\big{)}q_{p}+g\big{)}, \tag{8}\]  
where \(J_{y}\big{(}q_{p}\big{)}\in R^{1\times 3}\) is the Jacobian matrix of the y-axis component of the CoM. By substituting (7) and (8) into (6), the flywheel pendulum model is rewritten as  
\[\dot{L}_{c}=-(\mu y_{com}+x_{com})m\big{(}J_{y}\dot{q_{p}}+\dot{J}_{y}\dot{q_{p }}+g\big{)}. \tag{9}\]  
Comparing the forms between (1) and (9), the dual-objective convergence equation for the sliding process is constructed as  
\[E_{sl}=L_{c}, \tag{10}\]  
\[U_{sl}\big{(}q_{p}\big{)}=-(\mu y_{com}+x_{com}), \tag{11}\]  
\[P_{sl}\big{(}q_{p},q_{p},\dot{q_{p}}\big{)}=\ m\big{(}J_{y}\dot{q_{p}}+\dot{J} _{y}\dot{q_{p}}+g\big{)}, \tag{12}\]  
where \(E_{sl}\) is the posture stability criterion in sliding, \(U_{sl}\big{(}q_{p}\big{)}\) describes the relation between the equilibrium variable and the CoM, and \(P_{sl}\big{(}q_{p},\dot{q_{p}},\dot{q_{p}}\big{)}\) is the instantaneous constraint, which equals the y-axis component of the contact force.  
Please note that (6) is tenable for any ground situation, while (7) is only suitable for horizontal ground. We suppose that the ground is flat with a constant friction coefficient \(\mu_{0}\), but at a certain angle \(\theta_{g}\) to the horizontal plane. The relation between \(F_{x}\) and \(F_{y}\) can be rewritten as  
\[F_{x}=-\mu^{*}F_{y}, \tag{13}\]  
where  
\[\mu^{*}=\tan\Bigl{(}\tan^{-1}\left(\mu\right)+\theta_{g}\Bigr{)}. \tag{14}\]  
During the actual sliding of the robot, \(\theta_{g}\) is difficult to measure. However, \(\mu^{*}\) can be estimated by measuring the actual contact force \(F_{x}^{*}\) and \(F_{y}^{*}\) at the feet:  
\[\mu^{*}=-\frac{F_{x}^{*}}{F_{y}^{*}}. \tag{15}\]  
In this way, the convergence method can adapt to uneven ground with the replacement of \(\mu\) in (11) with \(\mu^{*}\).
## 2 Materials and Methods
### Proposed Model Architecture  
As shown in Figure 3, the proposed overall model architecture contains three parts: a feature extractor, an LSTM or transformer encoder block, and our linear classifier. In the feature-extractor part, three convolutions with different filter sizes are used to extract features from the segmented input sequence. The extracted features and the input sensor signal are concatenated to obtain 4N, where N is the number of features of the input signals. In the next block, we will use two different types of models to extract the dependency among the input sensor signals. The first has three stacked bidirectional LSTM encoders, and the second has three stacked transformer encoders. Each LSTM or transformer encoder layer is followed by layer normalization and then passes through the Gelu activation function. The global average pooling layer and the dense layer are applied to the result found from the last LSTM or transformer encoder block. Finally, a sigmoid classifier is used for classifying falls from non-fall activities.