esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Detection Using Multi-Scale Balanced Sampling
## Experiments
### Performance Comparison with Other Method  
Several one stage and two stage object detection methods is adopt to evaluate the effectiveness of MB-RPN: SSD [3] RetinaNet [10], FPN [8], Libra RCNN [11], GA-RPN [13] and SRetinaNet [12]. All of these methods except SRetinaNet are implemented with the source code provided by authors. For SRetinaNet method, it is implemented by adjusting hyperparameters of RetinaNet.  
Table 2 shows the quantitative results on DOTA, the best performance for each category is colored in red. The mAP of MB-RPN achieves 68.5%, which outperform other methods. Compare with the one-stage approaches, since they are lack of positive/negative discrimination process, the detection accuracy are lower than all of the two-stage approaches obviously. Compare with original FPN, the mAP is 3% higher, moreover the AP of each category is also higher. Compare with GA-RPN and Libra RCNN, the mAP is increased about 1.7 and 0.9% respectively, the AP of the most of categories are increased. The visualization comparison between MB-RPN and Libra RCNN is shown in Figure 10, MB-RPN detects more accurate small objects in various challenging cases, e.g., small vehicle objects at bottom left of Figure 10a and middle of Figure 10b. At the same time, the performances of other medium and big objects are not decreased. The above phenomenon proves the effectiveness of MB-RPN in enhancing the detection performances for images with small objects and large scale variants.  
Considering the performances gap between one-stage and two-stage approaches, in this paper the performance comparison of UAVB dataset is only carried out between FPN, GA-RPN, Libra RCNN and MB-RPN. Table 3 shows the quantitative evaluation of these approaches, the best performance for each category is colored in red. All of the mAP acquired from the approaches above are not ideal, this may because the imbalance of samples. However, the MB-RPN still largely outperform other approaches on both mAP and AP for each category. Figure 11 provides a visual comparison of our approach and Libra RCNN, since both of the two approaches' performance are not ideal, it only shows  
Figure 8: Distribution of multi-scale balanced,FPN and Libra RCNN sampling method.  
Figure 9: Example of Multi-scale Balanced Sampling Results of FPN, Libra RCNN and Multi-scale Balanced.  
the localization results but without categories. It can be seen that MB-RPN detects more small objects such as cars at upper of the input image which is corresponding to sampling results and distribution shown above. The above phenomenon proves the effectiveness of MB-RPN in enhancing the detection performances.  
Figure 10: Selected visual comparison of DOTA benchmark dataset. Each subfigure include result of Libra RCNN on the left and MB-RPN on the right. (**a**) small objects (**b**) middle objects.
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### Transmission Tower matching Network
#### 2.3.3 Triplets Generation Strategy Based on Dynamic Negative Allocation
 \tag{11}\]  
\[s_{i,j}=-score_{i}^{j},i,j=1,2,\ldots m,m\leq q/2 \tag{12}\]  
\[x_{i,j}= \begin{cases}1&Assignment\ from\ jth\ negative\ to\ ith\ anchor\\ 0&others\end{cases}  \tag{13}\]  
\[S=\sum_{i=1}^{m}\sum_{j=1}^{m}x_{i,j}s_{i,j} \tag{14}\]  
where \(value_{i}^{j}\) represents the triplet value computed from the anchor and positive in the \(ith\) item and the negative in the \(jth\) item; \(score_{i}^{j}\) represents the triplet score for the anchor and positive in the \(ith\) item and the negative in the \(jth\) item; \(s_{i,j}\) represents the element in the \(ith\) column of the allocation matrix, and \(s\) is a m \(\times\) m matrix. Each row represents the score of the anchor and positive in the \(ith\) item corresponding to the negative in the \(jth\) item; m represents the total number of items in the batch; \(q\) represents the total number of transmission tower instances in dataset; \(x_{i,j}\) represents the allocation matrix; \(S\) represents the objective function to be optimized. Under the assumption that feature vectors of the same type of transmission towers are closer compared to those of different types, and that these distances ensure non-zero triplet loss, we developed a triplet mining strategy. These newly formulated triplets, in accordance with our hypothesis, are delineated in Figure 8. In Figure 8, each transmission tower instance is labeled as tower '\(i\)', indicating its sequence as the \(ith\) instance (tower_ID). Figure 7: Flowchart of the new triplet generation process. Detailed information about its data type, and other relevant details are provided in Table 3. Table 3's structure will be detailed in Section 2.4. Additionally, item '\(i\)' refers to the \(ith\) triplet sample. Within an item, 'A' represents the anchor, 'P' denotes the positive, and 'N' signifies the negative.
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 3 Problem Statement  
The problem addressed in this paper is to enforce the output to track a given reference. The problem is known in the literature as the output regulation problem when the reference is generated by a neutrally stable exosystem described by [20]  
\[\frac{dz}{dt}=\Theta(z),\quad z(0)=z_{0} \tag{4}\]  
where \(z(t)\in\mathbb{R}^{s}\). Under this assumption, the reference \(x_{ref}\) is expressed by  
\[x_{ref}=\Pi_{1}(z)\]  
where \(\Pi_{1}(0)=0\). Define the set of reference signals  
\[X_{ref}=\left\{x_{ref}:\mathbb{R}_{+}\rightarrow\mathbb{R}^{n}:x_{ref}=\Pi_{1 }(z)\text{ and }\dot{z}=\Theta(z),\;z(0)=z_{0},\;\;z_{0}\in\mathbb{R}^{s}\right\}\]If \(\dot{z}=0,\ z(0)=K\in\mathbb{R}\) and  
\[x_{ref}=\Pi_{1}(z)=-\frac{1}{K}\left[\sum_{i=1}^{M}\lambda_{ei}(A_{0}+A_{i}) \right]^{-1}\left[\sum_{i=1}^{M}\lambda_{ei}(B_{0}+B_{i})\right]V_{in}z,\lambda_ {e}\in\Lambda_{R}\]  
then \(X_{ref}=X_{eq}\). This means that the stabilization problem solved in [19] is a particular case of a tracking problem. From a practical point of view, not all the reference signals can be followed by a given converter. In general, the converter is designed to follow a specific set of signals referred to as _admissible_. To define the structure of the set of such signals, define the following map:  
\[\Pi_{2}(z)=-A_{0}\Pi_{1}(z)+\frac{\partial\Pi_{1}}{\partial z}\Theta(z) \tag{5}\]  
**Definition 1**.: _A reference signal is said to be admissible if it belongs to the following set:_  
\[X_{Ad}=\left\{x_{ref}\in X_{ref},\exists\lambda_{ref}:\mathbb{R}\to\Lambda_{ R}:\Pi_{2}(z)=B_{0}(x_{ref})\lambda_{ref}\right\}\]  
In other words, from (5), we have  
\[\frac{\partial\Pi_{1}}{\partial z}\Theta(z)=A_{0}\Pi_{1}(z)+\Pi_{2}(z)\]  
From the admissibility of the reference signal, we deduce that there exists \(\lambda_{ref}:\mathbb{R}\to\Lambda_{R}\) such that  
\[\frac{\partial\Pi_{1}}{\partial z}\Theta(z)=A_{0}\Pi_{1}(z)+B_{0}(\Pi_{1}(z)) \lambda_{ref} \tag{6}\]  
which is nothing but  
\[\dot{x}_{ref}=A_{0}x_{ref}+B_{0}(x_{ref})\lambda_{ref}\]  
Equation (6) is known as a _regulator equation_ and the existence of \(\Pi_{1}(z)\) and \(\lambda_{ref}\) is a necessary condition for the solvability of the tracking control problem (see [25]). We introduce the tracking error signal  
\[e(t)=x(t)-x_{ref}(t)=x(t)-\Pi_{1}(z)(t)\]  
Then, we have  
\[\frac{de(t)}{dt} =\frac{dx(t)}{dt}-\frac{\partial\Pi_{1}}{\partial z}\Theta(z)(t) \tag{7}\] \[=A_{0}\ x(t)+B_{0}(x(t))\lambda(t)-\frac{\partial\Pi_{1}}{ \partial z}\Theta(z)(t)\] \[=A_{0}e(t)+B_{0}(x(t))\lambda(t)-\Pi_{2}(z)(t)\ \text{from}\ \eqref{eq:c_ref}\]  
The tracking problem can now be precisely stated.  
**Problem 1**.: _Design a control law \(\lambda:\mathbb{R}\to\Lambda_{S}\) such that, for any initial condition \(e(0)\in\mathbb{R}^{n}\), system (7) is globally asymptotically stable, ensuring that_  
\[\lim_{t\to\infty}e(t)=0\]
## 3 Results and Discussion
### Infrared Image Analysis and Calculations
#### 3.2.1 Calculation of Zone Length and Molten Zone Thermal Gradient
This means that the melt temperature close to the crucible bottom could differ slightly from the temperature measured at the surface. Since the zone refining process is very sensitive to temperature fluctuations generated for example by current or voltage fluctuation in the induction system, these aspects have to be taken into consideration while looking at the effective temperature values measured. However, when considering the high thermal conductivity of aluminum, the small thickness (15-20 mm) of the sample and the great degree of convection promoted by the induction heating system, the vertical gradient temperature is assumed to be very small and has no significant degree of influence on the results presented in this work. This assumption can be reinforced through the observation of a longitudinal section macrograph of the middle of the bar, shown in Figure 12. This macrograph demonstrates a rather stable grain growth direction, aligned horizontally towards the moving direction. This fact indirectly indicates that the vertical temperature gradients are not strong enough to influence the growth behavior of the sample, being rather mostly influenced by the horizontal temperature gradients. This is partially due to the strong effect of convection generated by the induction heating applied to the system. If these temperature differences in the vertical section between the bottom and top of the molten region were too high, the expected result would be a grain orientation towards the bottom of the crucible. However, this was not observed within our experiments. Figure 11: Temperature gradient according to the position in the bar. Figure 10: Temperature profile of the exemplary Frame 210 showing the region over the liquidus temperature of aluminum, as well as the \(\Delta\)T and \(\delta\)x. In addition, the obtained information on the variation of the molten zone length, the effective growth rate on the freezing interface and the horizontal temperature gradient of the molten zone along the whole bar length is far more important than the punctual values of temperature measured by the camera.
## 5 Results and Discussion
### A Brief Description of the Models Used for Comparison
```
1:functionBUILD_VAE(input_shape, latent_dim=32)
2: inputs \(\leftarrow\) Input(shape=input_shape)
3: encoded \(\leftarrow\) LSTM(64)(inputs)
4: z_mean \(\leftarrow\) Dense(latent_dim)(encoded)
5: z_log_var \(\leftarrow\) Dense(latent_dim)(encoded)
6: z \(\leftarrow\) Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])
7: encoder \(\leftarrow\) Model(inputs, [z_mean, z_log_var, z])
8: latent_inputs \(\leftarrow\) Input(shape=(latent_dim,))
9: decoded \(\leftarrow\) RepeatVector(input_shape[0])(latent_inputs)
10: decoded \(\leftarrow\) LSTM(64, return_sequences=True)(decoded)
11: denseLayer \(\leftarrow\) Dense(input_shape[\(-\)1])
12: decoded \(\leftarrow\) TimeDistributed(denseLayer)(decoded)
13: decoder \(\leftarrow\) Model(latent_inputs, decoded)
14: outputs \(\leftarrow\) (decoder(encoder(inputs)[2]))
15: vae \(\leftarrow\) Model(inputs, outputs)
16: vae_loss = K.mean(K.square(inputs \(-\) outputs)) + K.mean(0.5 * K.sum(1 + z_log_var \(-\) K.square(z_mean) \(-\) K.exp(z_log_var), axis=\(-\)1))
17: vae_add_loss(vae_loss)
18: vae_compile(optimizer='adam')
19: return_vae
20:endfunction
```  
**Algorithm 4** Pseudocode for building a Variational Autoencoder (VAE)
## 3 Results & Discussion
### Glass and Anode Cross Sections  
TEM lift-outs of the anode and glass envelope were extracted using FIB to examine deposit layering. The glass sample was taken from G6, in the Ba-Al flash pan region. The anode sample was  
Figure 7: **(a)** A photograph of the cathode assembly before removal of the cathodes but after removal of the anode. This image shows the ceramic spacer penetrated by the cathode housings (blue ring in “zoom-in”) and the cathodes visible through holes in the housing (green ring in “zoom-in”, ~2 mm in diameter). **(b)** Plan-view SEM and **(c–f)** EDS maps of Ba/Mg, Mg, O, and Ca from one of the discolored regions of the anode that were observed directly opposite a cathode’s emitting surface (Figure 2). Each image is comprised of multiple scans combined to produce a large area image, allowing for examination of the entire deposition region (red ring in **(b)** and “zoom-in”, ~6 mm in diameter).  
taken from A3, close to the pinhole directly above a cathode in the primary deposition area. High-angle annular dark-field (HAADF) images of the glass and anode samples are shown in Figures 8a and 9b, respectively. The substrate--glass or Mo--is visible as the uniform region in the upper right of Figure 8 and lower left in Figure 9b,c, respectively.  
Figure 8a shows a poorly adhered porous layer (bottom left) atop a smooth glass substrate (upper right). Elemental mapping of the imaged region are shown in Figure 8b--e; they show Si confined to the glass substrate and Ba to the deposited layer. Ca and Al are present in both materials, with Al enhanced in the near-substrate regions of the deposited layer. Ca is weakly present throughout the glass and the deposit. A similar porous layer \(\sim\)8 um thick was also observed on the Mo anode (Figure 9a). Figure 9b shows poor adhesion of the deposited layer and the presence of large voids. These features may have formed during sample preparation, but the numerous smaller voids (less than 1 um) are likely features formed during deposition. Elemental mapping indicates that Ba is present throughout the deposit. Thin (\(\sim\)100 nm) Ca-rich layers are observed suggesting that deposition may have occurred at different times during activation/operation.
## 5 Discussion
### Algorithms
#### 5.2.2 Platforms  
In terms of AI research and development platforms, we have identified three (Table 1). Although Unity allows for a more flexible, fast, and easy-to-use environment creation editor, it is still very heavy compared to ViZDoom and DeepMind Lab, which are very lightweight due to being based on older engines.  
ViZDoom can be considered the most flexible in terms of code, as shown by [14], as it can use a wide array of algorithms and frameworks, instead of being chained to what comes out of the box, such as in Unity's ML-Agents' case.  
Unlike Unity, ViZDoom and DeepMind Lab also have software rendering [32; 34], meaning that rendering of graphics can be performed in the CPU, leaving the GPU free to compute the ML part of the projects.  
In terms of physics, due to being based on such old engines, ViZDoom and DeepMind Lab have limited simulations, something that Unity compensates for with its Nvidia PhysX integration.  
Although ViZDoom is more lightweight than Unity, we still feel that researchers should consider using Unity or an equivalent due to its flexible environment construction and advanced physics. Both ViZDoom and DeepMind Lab are based on very old engines that are considered more than outdated in today's time, and as such should only be considered in cases where unity or any newer alternative cannot be used in any way.  
In conclusion, if what we are looking for is a flexible and easy-to-use training environment that can produce agents usable in commercial projects, we may want to consider using Unity's ML-Agents. However, if our objective is to research algorithms and frameworks in a resource-light platform, then ViZDoom or even DeepMind Lab might be more appropriate.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Example of ODYSSE Operations  
Figure 2 illustrates the general functioning of ODYSSE. The source U has a data packet \"D\" to send to node V. It starts by transmitting beacons \"B\". We set the parameter MAX_NB_REPLY to 2, hence node U should receive two Reply messages before selecting the forwarder. Node N\({}_{2}\) replies \"R\" before node N\({}_{1}\) because this latter was in the sleep state. Node N\({}_{2}\) remains in the active state for a maximum period given by WAIT_DATA_PERIOD. Meanwhile, the node N\({}_{1}\) becomes active and receives the beacon message from U, thus it sends back a Reply message, then it waits for WAIT_DATA_PERIOD. Receiving two Reply messages, the source U selects node N\({}_{2}\) as a forwarder and transmits the data message. At node N\({}_{1}\), the WAIT_DATA_PERIOD expires with no data received, it then returns to the sleep state. Node N\({}_{2}\) starts sending beacons. The node N\({}_{3}\) replies when it wakes-up. However, as the MAX_NB_REPLY is set to 2, node N\({}_{2}\) waits the end of the MAX_BEACON_PERIOD to transmit the packet \"D\" to node N\({}_{3}\). Finally, node N\({}_{3}\) forwards the data to the final destination V. A flowchart illustrating the operations of ODYSSE is provided in the Appendix (Figure A1).
###### Abstract  
The key elements used for receiving and processing signals in communication systems are the bandpass filters. Initially, a common operating mechanism was applied for the design of broadband filters, i.e., by cascading low-pass filters or high-pass filters using multiple line resonators with length quarter-half- or full-wavelength with central frequency, but using these approaches, the design topology becomes expensive and complex. The above mechanisms can be possibly overcome using a planar microstrip transmission line structure due to its simple design fabrication procedure and low cost. So, pointing out the above problems in bandpass filters such as low-cost, low insertion loss, and good out-of-band performance, this article presents a broadband filter with multifrequency suppression capability at 4.9 GHz, 8.3 GHz, and 11.5 GHz using a T-shaped shorted stub-loaded resonator with a central square ring coupled to the basic broadband filter. Initially, the C-shaped resonator is utilized for the formation of a stopband at 8.3 GHz for a satellite communication system, and then a shorted square ring resonator is added to the existing C-shaped structure for the realization of two more stopbands at 4.9 GHz and 11.5 GHz for 5G (WLAN 802.11j) communication, respectively. The overall circuit area covered with the proposed filter is 0.52 ag \(\times\) 0.32 ag (\(\Delta\)g is the wavelength of the feed lines at frequency 4.9 GHz). All the loaded stubs are folded in order to save the circuit area, which is an important requirement of next-generation wireless communication systems. The proposed filter has been analyzed using a well-known transmission line theory, even-odd-mode, and simulated with the 3D software HFSS. After the parametric analysis, some attractive features were obtained, i.e., compact structure, simple planar topology, low insertion losses of 0.4 dB over the entire band, good return loss greater than 10 dB, and independently controlled mutli stopbands, which make the proposed design unique and can be used in various wireless communication system applications. Finally, a Rogers RO-4350 substrate is selected for the fabrication of the prototype using an LPKF S63 ProtoLaser machine and then measured using a ZNB20 vector network analyzer for matching the simulated and measured results. After testing the prototype, a good agreement was found between the results.  
uniform transmission line; super wideband; bandpass filter; wireless communication; triple notched bands; T-shaped shorted stub-loaded resonators; low insertion loss +
Footnote †: journal:  
0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
Although patulin can occur in many moldy fruits, cereals, and other foods, the major sources of patulin exposure are apple products. Patulin can act as a phytotoxin and is active against pathogenic fungi. Patulin was initially suggested as a treatment of the common cold, as it is anti-viral in addition to being antibacterial, but the trials on humans were quickly stopped because of the toxicity of patulin [9]. Patulin has been reported to be toxic, teratogenic, and cardiotoxic; it influences the permeability of the colic epithelium and renal function and is immunotoxic [10]. [PERSON] et al. conducted a study where normal intestinal cells were exposed to non-toxic levels of PAT for longer durations and found that PAT exposure induced pre-cancerous cells in normal intestinal cells [11]. The study by [PERSON] et al. showed that patulin altered the transcription and translation of \(\alpha\)1-AR in HEK293 cell-induced renal toxicity [12]. Kojic acid (KA) is a mycotoxin produced by various fungal or bacterial strains, such as Aspergillus strains, Penicillin, or Acetobacter species. KA possesses weak antimicrobial properties and is active against several common bacterial strains. It is widely used as a food ingredient and dermatological skin lightening agent. Kojic acid has a complex toxicological profile. It was reported to be non-toxic after a single intraperitoneal, oral, or topical dose in rodents [13] but genotoxic in several in vitro tests. Chronic oral administration of kojic acid has been reported to produce thyroid adenomas in rats, although no increase in the incidence of malignant tumors due to continuous serum thyroid-stimulating hormone (TSH) stimulation by a non-genotoxic mechanism [14]. Novel kojic acid derivatives showed the best inhibitory effects on diphenolase activity and monopholase activity with anti-melanogenic activity confirmed by assessing the inhibition of melanin content and intracellular tyrosinase activity in zebrafish models and could provide new insights into the development of novel anti-melanogenic agents [15]. While most behavioral tests have been conducted on rodents and most histological research (involving mycotoxins) had zebrafish embryos as animal models, this study uses adult zebrafish. Zebrafish were chosen because of their small size, social behavior, resistance to environmental stress, and due to the histological, physiological, histopathological, and genetic similarity with mammals and humans [16]. Thus, the costs, time, and space requirements are lower compared to those required in the case of rodents. The main goal of the research is to assess the impact of patulin and kojic acid exposure on the explorative behavior and internal organs of the zebrafish. By using NTT, \(\Upsilon\)-maze test, and statistics for analyzing the behavior and microscopy for detecting histopathological alterations, we can observe how these two mycotoxins affect the life of zebrafish. We have also aimed to detect an automatic learning algorithm which can be trained to classify the behavior of patulin-/kojic acid-exposed fish, only by analyzing the characteristic features of the NTT behavioral test.
## 5 Simulation Results
### Path Length  
One of the important parameters in the establishment of the paths for the initial random topology is the mean path length, i.e., the average number of hops of every path over all of them. Every path will end in the gateway node on the surface. The results obtained in FAHP and in SPRINT-Fuzzy [34] using different numbers of nodes (100 and 400) and different transmission ranges (4-7 km for 100 nodes, 2-4 km for 400 nodes) are shown in Figure 9.  
For a better comparison, Table 10 is introduced, presenting the values obtained in the bar graph of Figure 9.  
Although the conclusion is that FAHP has a worse behavior than SPRINT-Fuzzy in creating the paths, they are close (see 100 nodes, transmission range 7 km). Moreover, FAHP can admit more criteria to be taken into account for enhancing the selection. So, these results are not definitive, and they can be enhanced when including new metrics in FAHP, such as density nodes in a region and residual energy for extending lifetime.