esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Attack Classification in IoT Using Support Vector Machines +
## 3 Methodology for IDS Using SVM
### C-Support Vector Machine  
The C-support optimization type of SVM is used when there is more than one class of data. In our case, we have two classes: benign and malicious. Consider the training set \(x_{i}\in\mathbb{R}^{n}\), where \(i=1,\ldots,l\); \(x_{i}\) is separated in two classes; \(y\in\mathbb{R}^{l}\) such that \(y_{i}\in\{1,-1\}\); \(l\) is the number of local node activity. C-SVM solves the optimization problem shown in Equation (1) [7].  
\[\begin{split}\min_{\mathbf{\omega},b,\xi}&\frac{1}{2} \mathbf{\omega}^{\intercal}\mathbf{\omega}+C\sum_{i=1}^{l}\xi_{i}\\ \text{subject to}& y_{i}(\mathbf{\omega}^{\intercal}\mathbf{ \phi}(\mathbf{x}_{i})+b)\geq 1-\xi_{i}\\ &\xi_{i}\geq 0,i=1,\ldots,l\end{split} \tag{1}\]  
In Equation (1), \(\mathbf{\phi}(x_{i})\) maps \(x_{i}\) into a higher-dimensional space and \(C>0\) is the regularization parameter. Due to the possible high dimensionality of the weights \(w\), we solve the problem shown in Equation (2) [7].  
\[\begin{split}\min_{\mathbf{\alpha}}&\frac{1}{2}\mathbf{ \alpha}^{\intercal}Q\mathbf{\alpha}-\mathbf{e}^{\intercal}\mathbf{\alpha}\\ \text{subject to}&\mathbf{y}^{\intercal}\mathbf{\alpha}=0\\ & 0\leq\alpha_{i}\leq C,\quad i=1,\ldots,l\end{split} \tag{2}\]  
In Equation (2), \(e=[1,\ldots,1]^{\intercal}\) is the vector of all ones, \(Q\) is an \(l\) by \(l\) positive semi-definite matrix, \(Q_{ij}\equiv y_{i}y_{j}K(\mathbf{x}_{i},\mathbf{x}_{j})\) is the kernel function. The kernel function we used for our purposes is discussed in Section 3.4. After we solve Equation (2), we solve Equation (3) to find the optimal \(w\).  
\[\mathbf{\omega}=\sum_{i=1}^{l}y_{i}\alpha_{i}\mathbf{\phi}(\mathbf{x}_{i}) \tag{3}\]  
The decision function for the C-class SVM is shown in Equation (4) [7].  
\[sgn(\mathbf{\omega}^{\intercal}\mathbf{\phi}(\mathbf{x})+b)=sgn\Bigg{(}\sum_{i=1}^{l}y_{i} \alpha_{i}K(\mathbf{x}_{i},\mathbf{x})+b\Bigg{)} \tag{4}\]
# Common-Ground Photovoltaic Inverters for Leakage Current Mitigation: Comparative Review
## 2 Classification of Common-Ground Converters
### Voltage-Source CG Converters (VSCG)
#### 2.2.2 Boosting VSCG  
Figure 5 shows the converters that belong to this group. Table 6 summarizes the features and limitations of each converter. The following remarks can be reported about these converters:  
* The boosting capability offered by these converters facilitates a reduction in the required decoupling capacitance. This can significantly improve reliability. In addition, all these converters offer continuous input current; this can significantly improve MPPT operation;
* The converters introduced in [56; 60; 61] uses coupled inductors to acquire high boosting capability. However, this results in increased cost and size also. On the other hand, the converters presented in [55; 57; 62] offer low boosting gain. Finally, the boosting gain offered by other boost converters is high; thus, no series connection of PV modules is needed for grid integration;
* Among these topologies, the converters introduced in [62; 63] offer five levels at the output voltage. In addition, these converters do not need a pre-charging circuit to charge the voltages on their capacitors. However, high electrolytic capacitors may be needed for voltage balancing over these capacitors. On the other hand, the converters introduced in [60; 61] can offer only two voltage levels. Thus, a large filter size is needed. All other inverters belonging to this category can offer three voltage levels;
* Among these converters, the converters introduced in [54; 55; 56; 60] use a high number of semiconductors. This can significantly increase the cost and reduce efficiency. Figure 5: Voltage-source common-grounded (CSCG) converters (boosting type). (**a**) Converter presented in the work of [54], (**b**) converter presented in the work of [55], (**c**) converter presented in the work of [56], (**d**) converter presented in the work of [57], (**e**) converter presented in the work of [58], the work of [59], (**f**) converter presented in the work of [60], (**g**) converter presented in the work of [61], (**h**) converter presented in the work of [62], (**i**) converter presented in the work of [63].
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 2 Problem Formulation and Cramer-Rao Lower Bound  
In this section, the system model is first described in detail, followed by the formulation of the localization problem. The CRLB for RSS-AOA time series-based localization is also derived.
# Consistency Models of NoSQL Databases
## 5 NoSQL Consistency Implementations
### OrientDB  
OrientDB has a master-slave strategy to achieve a more scalable architecture. Every time a client makes a request, it would have to hit a single master. This master would then propagate any state change to its replicas. Finally, it would acknowledge the client. This approach made the database strong consistent, and it was only scaling the reads because there was only a single master node, which represented a severe bottleneck in the system.  
Some years ago, OrientDB announced a new paradigm shift, the multi-master architecture, with the premise that all nodes must accept writes. In the default configuration, a client acknowledges only after the majority of the master nodes commit the new date state. Then, asynchronously, the master nodes that have not committed are fixed, and the data propagates to the replicas (read-only). This new approach made the system eventual consistent when reading from an unfixed master or some replica that has not yet received the latest data. However, if the same master is hit over and over again, the client will get strong consistency, while compromising performance. OrientDB handles this with three client load balancing configurations:  
* **STICKY**. The default configuration. The client remains connected to the same server until the database closes. (Strong Consistency, high latency)
* **ROUND_ROBIN_CONNECT**. The client connects to a different server at each connection following a round robin schedule [41]. (Session Consistency)
* **ROUND_ROBIN_REQUEST**. The client connects to a different server at each request following a round robin schedule [41]. (Eventual Consistency, low latency)  
Clients have the ability to know whether the version of the data retrieved is updated [42]. OrientDB supports Multi-Version Concurrency Control (MVCC) with atomic operations. This avoids the use of locks in the server. Every time a read request is made, if the version of the data is equal to the one that is on the response payload addressed to the client, the operation is successful. Otherwise, OrientDB generates an error that can be handled by the client.
## 3 Results
### Intelligibility  
It is clear from the data gathered that the speech output of commercially available devices is limited. The speech output of commercially available devices does not often reflect the user's language, age, sex, or personality [4; 36; 64; 65]. In a study by [PERSON] et al. [5], parents of children using SGDs responded positively towards voices that they believed matched their child's identity, particularly as they were sex and age appropriate. However, as the synthesised prototype child voice had flawed intonation and periods of unintelligibility, they reported that they would rather opt for an adult voice that was clear and easy to understand. This was supported by the teachers who needed to understand the voice in demanding surroundings [5]. Comparatively, one of the child users stated that he would have liked to use the child voice on his SGD, but that he did not like the sound of the prototype voice created [5]. [PERSON] et al. [5] found that both the children and their communication partners would prefer a child voice that matches the child's vocal identity but not at the cost of intelligibility. A higher-quality speech output may be more important than matching the child's vocal identity [5].  
There are additional factors that may affect the intelligibility of the speech output. According to a study by [PERSON] et al. [66], two contextual variables (words vs. sentences and topic cues vs. no topic cues) interacted with speech type (digitised vs. synthesised speech). Listeners experienced increased intelligibility for contextual words and sentences and in particular, increased intelligibility of sentences compared to single words. In other words, children who communicate with other children that make use of a single word AAC device may find it difficult to understand as they have little contextual information to point them in the right direction [66]. This was also found in other studies [36; 67] as listeners reported increased intelligibility of words when words where embedded in sentence utterances rather than in isolation.
## 3 Results and Discussion
### She Ethnic Clothing Classification Based on FPA-CNN
#### 3.2.2 Analysis of the She Ethnic Clothing Classification Results  
Table 2 presents the classification results for the She ethnic clothing in different regions. From Table 2, it can be observed that the FPA-CNN method developed in this study achieves good recognition results for She ethnic clothing in the five regions. Among the 74 samples of She ethnic clothing, 73 are correctly classified, resulting in an overall classification accuracy of 98.65%. However, there is a case of misclassification. For instance, one item of She ethnic clothing from the Xiapu region is incorrectly classified as Fuding clothing.  
There are two main reasons for the misclassification. Firstly, compared to Xia Pu clothing, Fuding clothing has two \" Red bayberry Flower\" features on the collar. Xiapu clothing does not have the \"Red bayberry Flower,\" and the proportion of this color feature is relatively small compared to the entire item of clothing. This makes it difficult to establish effective feature differences, leading to difficulties in distinguishing clothing from these two regions and resulting in incorrect classification. Secondly, the collection of She ethnic clothing images is incomplete, which hampers the ability to fully capture the distinct features of clothing from different regions. This incomplete representation during the image collection and segmentation processes leads to feature omissions and incomplete transformation into image information. As a result, there may be similarities between the color histograms and color moment features of Fuding and Xiapu clothing, leading to the misclassification.  
In summary, while the FPA-CNN method demonstrates high accuracy in classifying She ethnic clothing from different regions, there are still challenges in distinguishing certain clothing items due to subtle feature differences and incomplete image representation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Jingning** & **Fu’an** & **Luoyuan** & **Xiapu** & **Fuding** & **Classification Accuracy/\%** \\ \hline Jingning & 12 & 0 & 0 & 0 & 0 & 100 \\ Fu’an & 0 & 13 & 0 & 0 & 0 & 100 \\ Luoyuan & 0 & 0 & 14 & 0 & 0 & 100 \\ Xiapu & 0 & 0 & 0 & 20 & 1 & 93.75 \\ Fuding & 0 & 0 & 0 & 0 & 14 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clothing classification of the different branches of the She ethnic group.
## 2 Pseudo-Membranes, Systems, and Applications
### Applications  
(a) Heat-to-Power Systems  
RED stacks with inorganic membranes can be applied in a heat-to-power generator, a closed-loop system where a regenerator is used to bring the concentrations of the outlet streams to their original values. This regenerator can be based on water distillation or evaporation of a volatile salt and is powered by waste heat. Osmosis through a classical membrane requires additional evaporation, and the low water transport through the inorganic membranes in such a heat-to-power system is therefore advantageous. Moreover, the thermal stability with respect to permselectivity and the small deterioration at elevated temperature make the application of inorganic membranes an interesting option. Because the volume of the closed-loop system is relative small, the price of salts is less important.  
(b) Energy-Storage Systems  
A RED/ED salinity gradient storage system consists, in the simplest form, of a RED/ED stack and two vessels with salt solutions. The solutions circulate through the stack in a closed system. During charge, a surplus of electrical energy is used to drive the stack in the electrodialysis (ED) mode, resulting in an increased salinity gradient between the two solutions. During discharge, this stack operates in the RED mode and the stored salinity gradient is converted to electrical energy. A drawback is that there is continuous water transport from the low- to high-concentration solution due to osmosis. Here, the low water permeability and high permselectivity of inorganic membranes are very advantageous. In contrast to the heat-to-power application, large amounts of electrolyte are needed and the price of the salt is important.  
(c) End Membranes in ED or RED Stacks  
End membranes are the outer membranes of a RED or ED stack and separate the electrode compartments from the feed compartments. In many applications, it is important that these membranes are a good barrier for unwanted transport from the electrode rinse solution to the feed water and vice versa. An example is the need to maintain low pH in the electrode rinse solution if the Fe\({}^{2+}\)/Fe\({}^{3+}\) redox couple is used [23; 24; 25; 26; 27]. In other applications, there is the danger of aggressive compounds in the electrode compartment that can damage the end membranes; chlorine is generated in an NaCl electrode rinse [27]. In stacks for H\({}_{2}\) production, there may be a need for solid pressure-resistant walls around the electrodes [3; 4]. The high specific permeability and mechanical robustness of pseudo-membranes can deal with these threats.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 3 Results
### Variation of Nominal Refractive Index Structure
#### 3.1.2 Impact on Rytov Variance  
Rytov variance is a crucial factor in determining the strength of the turbulence on both uplink and downlink channels. The values of the parameter vary depending on the wavelength and refractive index structure value at ground level. The value for the refractive index structure at ground level was varied to show the variations in turbulent strength. Variation in turbulent strength arises due to variations in geographical conditions, the weather, and the time of day. For example, ground-level turbulent strength during the day is considerably more than at right. From a design standpoint, Rytov variance with lower values should be preferable in order to reduce turbulence impact.  
Figure 4 depicts the varying characteristics of Rytov variance with respect to the refractive index structure value at ground level for various wavelengths and elevation angles in an uplink channel. Figure 5 depicts the variation of Rytov variance with respect to refractive index structure value at ground level for a downlink channel for different satellite distances and wavelengths. The observations from Figures 4 and 5 show that Rytov variance rapidly grows beyond the \(10^{-13}\) m\({}^{-2/3}\) refractive index structure at ground level. This is especially true for longer satellite distances and higher elevation angles. However, Rytov variance was essentially constant for smaller refractive index structures at ground level, i.e., a \(\text{C}_{n}^{2}(0)\) less than \(10^{-14}\) m\({}^{-2/3}\). The same observations depict the role of wavelengths in determining the values of Rytov variance with different refractive index structure values at ground level.  
From a wavelength point of view, we can see from Figures 4 and 5 that the Rytov variance from longer wavelengths was less compared to values at shorter wavelengths. Rytov variance at 1550-nanometer was comparatively less for both uplink and downlink FSO channels. Moreover, as seen in Figure 4, the satellite distance increased, and the value for Rytov variance increased for turbulent regions beyond the \(10^{-13}\) m\({}^{-2/3}\) in an uplink channel. In Figure 5, we see that at the same wavelength, elevation angles with higher values had higher Rytov variances compared to smaller elevation angles.  
The difference in Rytov variance in uplink and downlink channels is also clearly observed in Figures 4 and 5. A greater value for Rytov variance in an uplink channel for  
Figure 3: The Hufnagel Valley empirical model.  
the same spatial conditions and times of day, compared to a downlink channel, shows that moderate to strong turbulence has more impact on an uplink channel than a downlink channel because the turbulence on uplink FSO is closer to the transmitter.
## 1 Introduction  
Synthetic Aperture Radar (SAR) polarimetry is an active and fruitful field of research in Earth observation. Polarimetry provides sensitivity to the soil moisture, as well as to the structural and geometric properties of the targets under observation, allowing a more accurate identification and classification than with non-polarimetric data. Moreover, the increasing number of spaceborne SAR systems equipped with polarimetric capabilities, as well as future planned missions, enables the advance in this research field at all levels, from theory and physical modeling to final applications.
## 2 Results
### Changes in Transcript Levels of Transcription Factors  
The transcript levels of _TPS_ genes were regulated by a number of different TFs. So far, six TF families in plants associated with terpenoid metabolism have been identified, including AP2/ERF [25; 26; 32; 33], bHLH [31], MYB [27], NAC [28], WRKY [29], and bZIP [30]. Their gene IDs are summarized in Table S3. Changes in the transcript levels of these six TF families from developing finger citron tissues were analyzed and presented as heat maps (Figure S6). The AP2/ERF family with 251 members was the largest, followed by the NAC family with 184 members and bHLH family with 169 members. As shown in Figure S5, TFs from different families had unique expression patterns.  
For the AP2/ERF family, members were grouped into clusters according to expression profiles in developing tissues of finger citron (Figure S6). There were 88 (35%) _AP2/ERFs_ mainly expressed in developing fruit, where nine members had the highest expression level in ripe fruit at the S6 stage. For flowers, 36 _AP2/ERFs_ had the most abundant transcripts in full flower stage at F2. For finger citron  
Figure 4: Hierarchical clustering and heat map visualization of changes in _TPS_ gene expression level in developing finger citron organs. F1, flower bud; F2, full flower, L1, young leaf; L2, mature leaf; S1–6, fruit developing stages 1–6. The left side of the heat map represents hierarchical clustering based on Pearson correlation. The color scale (\(-2\) to \(2\)) is shown on the right; red represents high content, blue represents low content. Hierarchical clustering and heat map were constructed using MetaboAnalyst 4.0.  
leaves, higher transcript levels of 18 _AP2/ERFs_ were observed in young leave L1 than mature leave L2 (Figure S6).  
Among the 169 members in the bHLH family, 58 genes were mainly expressed in fruit, 43 genes in flowers and 68 genes in leaves (Figure S6). For fruit, 11 _bHLHs_ had the highest expression level in young fruit at the S1 stage. During flower development, expression of 21 _bHLHs_ increased from F1 to F2, while 22 members decreased. Of the _bHLHs_ mainly expressed in leaves, 58 genes had higher transcript abundance in young leaves L1 than mature leaves L2 (Figure S6).  
A total of 76 bZIP members were identified in finger citron, where 31 (41%) members were mainly expressed in leaves (Figure S6). For MYB family, 58 (37%) of the total 155 family members were enriched in flower tissue. A total of 44 _MYBs_ exhibited higher transcript levels in young flower at F1 (Figure S6). We observed that _NAC_ genes were enriched in flowers, where 52 (28%) members were expressed mainly in young flowers (Figure S6). Nearly half of the _WRKYs_ (27 out of 66 genes) were enriched in fruit tissues. All of these TF expression profiles, together with those of the _TPS_ genes, were used for the gene co-expression analysis.
## 4 Conclusions and Discussion  
We propose an applicable mechanism for utilizing weakly annotated images for object detection models in a setting where bounding box information is insufficient for achieving high classification performance. The proposed method enables a successful increase of the size of strong annotations by safely assigning bounding box predictions as ground truth. The method is applied to the primary task of detecting masses in BUS and tested on the Stanford Dog dataset to verify generality. A comparison with different variants of the method supports the reasoning behind the manner of controlling the influence of weakly annotated images. We notice that maintaining the loss from weakly annotated images at a low level until the RPN proposes bounding boxes containing objects guides the model to have a higher classification capability. Additionally, we set specific configurations for the active learning scheme, which can be a risky work, since there is no way to confirm the correct assigning of GT bounding boxes. The results show that it can enhance classification performance if it was an issue.  
For our future work, we plan to extend the proposed method to autonomously detect whether if the RPN is proposing bounding boxes containing objects and control the weight, which was originally increased following a fixed schedule. This will increase the generality of the method, since the point of RPN convergence may vary depending on the size and detection difficulty of a dataset. We believe that the proposed method can be applied to typical cases of medical imaging tasks where strong annotations are costly and weakly labeled data are relatively easy to obtain from the diagnosis procedure.  
Conceptualization, J.Y.; methodology, J.Y.; software, J.O.; validation, J.Y.; formal analysis, I.Y.; investigation, J.Y. and J.O.; resources, I.Y.; data curation, I.Y.; writing--original draft preparation, I.Y.; writing--review and editing, J.O.; visualization, J.O.; supervision, I.Y.; project administration, I.Y.; funding acquisition, I.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF). This research was funded by the Ministry of Education, Science, Technology (No. 2019R1A2C1085113).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SNUBH & Seoul National University Bundang Hospital \\ BUS & Breast Ultrasound \\ GANs & Generative Adversarial Networks \\ CNN & Convolutional Neural Networks \\ FCN & Fully Connected Networks \\ \end{tabular}  
Figure 6: Prediction results from the Stanford Dog images. Image on the right and left are predictions for a Blackhound image and a English Foxhound image, respectively.